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A STUDY ON THE INFLUENCE OF MOBILE FOODIE

APPLICATIONS ON RESTAURANT SELECTION

DECISIONS

BY

MISTER ANUSORN PHOPIPAT

AN INDEPENDENT STUDY SUBMITTED IN PARTIAL

FULFILLMENT OF

THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF SCIENCE PROGRAM IN MARKETING

(INTERNATIONAL PROGRAM)

FACULTY OF COMMERCE AND ACCOUNTANCY

THAMMASAT UNIVERSITY

ACADEMIC YEAR 2017

COPYRIGHT OF THAMMASAT UNIVERSITY

Ref. code: 25605902040483CGF

A STUDY ON THE INFLUENCE OF MOBILE FOODIE

APPLICATIONS ON RESTAURANT SELECTION

DECISIONS

BY

MISTER ANUSORN PHOPIPAT

AN INDEPENDENT STUDY SUBMITTED IN PARTIAL

FULFILLMENT OF

THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF SCIENCE PROGRAM IN MARKETING

(INTERNATIONAL PROGRAM)

FACULTY OF COMMERCE AND ACCOUNTANCY

THAMMASAT UNIVERSITY

ACADEMIC YEAR 2017

COPYRIGHT OF THAMMASAT UNIVERSITY

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Independent Study Title A STUDY ON THE INFLUENCE OF

MOBILE FOODIE APPLICATIONS ON

RESTAURANT SELECTION DECISIONS Author Mister Anusorn Phopipat

Degree Master of Science Program in Marketing

(International Program)

Major Field/Faculty/University Faculty of Commerce and Accountancy

Thammasat University

Independent Study Advisor Professor Malcolm C. Smith, Ph.D.

Academic Year 2017

ABSTRACT

Online food delivery competition in Thailand is fierce. Public behavior has

changed from eating out at restaurants to ordering food from various online providers.

Social media allows users/customers to generate on-line content and share their

experiences with the online community. This study of “The influence of mobile

foodie applications on restaurant selection decisions” is an independent research

exercise focusing on the contemporary topic of technological issues regarding applied

marketing in Thailand.

There are four primary research were to 1) To identify customer profiles and

then classify them into the segments, 2) To determine consumer restaurant selection

behavior and experience, 3) To determine consumer price perception toward online

order fees, and 4) To identify key application features needed by customers.

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Exploratory research was conducted through a secondary data reviews and ten

in-depth interviews. Descriptive research was conducted by an online social media

survey using Facebook, LINE chat application, and e-mail. Target respondents were

males or females aged between 18 to 60 years old who had access to the internet and

had used a foodie application in the past three months. Data gathered from 265

respondents were analyzed using the Statistical Package for the Social Sciences

(SPSS) by Analysis of Variance (ANOVA), Chi-square, frequencies, percentages,

factor analysis, cluster analysis, and price sensitivity measurements.

Main findings from the quantitative research indicate that customers who used

online delivery food applications can be divided into four segments as achiever,

perfectionist, extrovert, and outdoor enthusiast. Top three restaurant selection criteria

for all respondents were speed of service, location, and value for money. The three

features respondents perceived to be important when using an application were

booking, payment option, and promotional information features. In term of awareness,

LINEMAN was ranked as number one followed by foodpanda, UberEat, and

Grabfood. Interestingly, the current online delivery fee is perceived to be acceptable

by respondents, and there is room to increase the service fee if needed.

The recommendation for the marketer is to focus on the achiever segment

because they are the heavy users of online food delivery services. This segment can

be engaged via online channels. Therefore, marketers should try to prevent this

segment from switching to other service providers. For developers, the top three

features of foodie applications to focus on are booking, payment option, and

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promotional information features. Also, the contents section of the application

is another important aspect to focus on.

This research will enable restaurant managers and application developers to

better understand changing customer behaviors better. Furthermore, the findings will

aid managers to design strategies to entice more customers to use their restaurants and

applications.

Keywords: Restaurant selections, food application, online delivery

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ACKNOWLEDGMENTS

Firstly, I would like to express my sincere appreciation and gratitude to

Prof. Malcolm C. Smith, my supportive advisor, for his valuable recommendations

throughout the entire independent study course. Prof. Malcolm C. Smith was always

accessible during his visits to Thailand. Without his support, comments, and advice,

this research would not have been completed.

Secondly, I would like to express my sincere gratitude to all the respondents

for giving their valuable time to participate in the in-depth interviews, complete the

surveys, and contribute to a significant part of this research. I would also like to thank

all the Professors from every course I have taken during my two years at Thammasat

University.

Lastly, I would like to thank my family, friends, and colleagues for their

understanding concerning my time devoted to the completion of this master’s degree

at Thammasat University.

Mister Anusorn Phopipat

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TABLE OF CONTENTS

Page

ABSTRACT (1)

ACKNOWLEDGEMENTS (4)

LIST OF TABLES (9)

LIST OF FIGURES (10)

CHAPTER 1 INTRODUCTION 1

1.1 Introduction to the study 1

1.2 Research objectives 3

CHAPTER 2 REVIEW OF LITERATURE 4

2.1 Restaurant delivery system 4

2.2 Thailand internet usage and customer changing behavior 4

2.3 Online spending in Thailand 4

2.4 Online food delivery service providers in Thailand 5

2.5 Customer decision-making process 5

2.6 Social Media, user-generated content and its effects 6

on purchase intention

2.7 Summary 7

CHAPTER 3 RESEARCH DESIGN 8

3.1 Research Methodology 8

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3.1.1 Exploratory Research Design 8

3.1.2 Secondary Data Research 8

3.1.3 In-depth interviews 8

3.2 Descriptive Research Design 8

3.2.1 Questionnaire survey 9

3.3 Data collection 9

3.3.1 Qualitative data 9

3.3.2 Quantitative data 9

3.4 Data Analysis 9

3.5 Theoretical Framework 10

3.6 Limitations of the study 10

CHAPTER 4 RESULTS AND DISCUSSION 11

4.1 Key findings from Secondary Research 11

4.2 Key findings from In-depth Interviews 11

4.3 Key findings from the questionnaire survey 13

4.3.1 General Profile of Respondents 13

4.3.2 Respondents’ Demographic profiles 13

4.3.3 Foodie application users’ segmentation 15

4.3.4 Customer segments 16

4.3.5 General Profile of each Customer Segment 17

4.3.6 Psychographic profile by segment 19

4.3.7 Restaurant Selection behavior by customer’s segments 20

4.3.8 Restaurant selection criteria 21

4.3.9 Key attributes that stimulates usage decision 22

of foodie applications

4.3.10 Key usage decision attributes by customer segments 23

4.3.11 Restaurant selection criteria via foodie application 23

4.3.12 Mean comparison of key restaurant selection criteria 24

via applications by customer segments

4.3.13 Importance of application features 25

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4.3.14 Mean comparison of key application features 25

by customer segments

4.3.15 Respondents’ awareness of online delivery 26

application in the market

4.3.16 Respondent’s perception on each application 26

4.3.16.1 LINEMAN Application’s Perception 26

4.3.16.2 GrabFood Application’s Perception 27

4.3.16.3 UBER EATS Application’s Perception 28

4.3.16.4 foodpanda Application’s Perception 28

4.3.17 Respondents’ perceptions toward fees charged 29

by online food delivery applications

4.3.18 Price sensitivity Measurement 30

4.3.19 Impact of price promotion on consumer 31

purchase intentions for foodie applications

4.3.20 Mean comparison of price promotion impact on 31

purchase intent by customer segments

CHAPTER 5 SUMMARY AND CONCLUSIONS 32

5.1 Research Summary 32

5.1.1 Customer Segmentation based on psychological factors 32

5.1.2 Consumer restaurant selection behavior 32

5.1.3 Consumer perception toward application’s features 32

5.1.4 Consumer perception toward each brand in the market 33

5.1.5 Consumer perception toward online delivery service fee 33

5.2 Recommendations 34

REFERENCES 36

APPENDICES

APPENDIX A: In-depth Interview’s questions 38

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APPENDIX B: Online questionnaire’s questions 39

APPENDIX C: Respondent’s profile and segmentation 52

APPENDIX D: Restaurant selection behavior 55

APPENDIX E: User’s perception on foodie application 65

APPENDIX F: Price perception toward online order fee 70

BIOGRAPHY 71

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LIST OF TABLES

Tables Page

4.1: All respondents’ demographic profile by frequency and percentage 13

4.2: Factor Analysis from psychological attributes 15

4.3: Number of respondents in each segment by frequency 16

4.4: Each customer segments by demographic profile 17

4.5: ANOVA test on restaurant selection behavior on 21

customer’s segments

4.6: All respondents' usage decision attributes for foodie application 23

by mean score

4.7: All Respondents' restaurant selection criteria via applications 24

by mean score

4.8: All Respondents' key application features by mean score 25

4.9: Online delivery application awareness by frequency and percentage 26

4.10: LINEMAN application’s perception by mean score 27

4.11: GrabFood application’s perception by mean score 27

4.12: UBER EATS application’s perception by mean score 28

4.13: foodpanda application’s perception by mean score 29

4.14: Respondents’ perception toward fees charged by mean score 29

4.15: Price promotion impact on purchase intent by mean score 31

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LIST OF FIGURES

Figures Page

2.1 The marketing Funnel 6

3.1 Research’s framework 10

4.1 Price sensitivity measurement 30

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CHAPTER 1

INTRODUCTION

1.1 Introduction to the study

Restaurants are critical businesses in Thailand as they are related to the travel

and tourism industry which accounted for 20.6% of the country’s GPD in 2016 and is

expected to rise by 9.4% to 21.9% in 2017 (World travel & Tourism council, 2017).

In the first three months of 2017, the number of newly registered restaurants

increased by 4% compared to the previous year (Languepin, 2017). Revenue for this

sector rose continuously from 2011-2015 with a CAGR of 9.07%. Despite this

growth, restaurant profit margins remained low at 2% due to high operation costs and

intense market. The restaurant industry needs to adjust and be open to new

technology. At the same time, it must operate more efficiently and become compatible

with changing customer behaviors. (กองขอ้มูลธุรกิจ กรมพฒันาธุรกิจการคา้ กระทรวง

พาณิชย,์ 2017).

In 2016, Thailand had approximately 43.87 million internet users, 11% more

than in 2015 (NBTC(กสทช), 2017).Thais spend six hours and thirty minutes on

weekdays and 18 minutes longer over the weekend using the internet ((ETDA), 2017).

Lifestyle changes and many external factors including time limitations, traffic

congestion, and a need for convenience have caused people to choose food delivery

over going to a restaurant. Therefore, many restaurants, especially those without an

in-house delivery service, decided to join online food delivery platforms to generate

more revenue from this booming channel. (Kasikornresearch, 2016). Therefore, it is

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crucial for restaurants and foodie applications to know what the consumer is looking

for when using their services. Most importantly, this would enhance the

competitiveness of local businesses and entrepreneurs in Thailand and ensure success

and survival in today’s frenetic online marketplace.

The literature review will further discuss how the customer reacts to WOM, e-

WOM, and the importance of online reviews which affect consumers’ restaurant

selection decisions. However, the questions that remain are: 1) Why does a customer

choose to use an online delivery service from a specific provider over another? 2)

What are the features loved by the customer and what remains to be improved? 3)

How can users be characterized? and 4) How price sensitive are the users?

This research aims to answers these questions by studying the influence of

foodie applications on Thai internet users’ decisions regarding restaurant choice as a

contemporary topic in applied marketing which focuses on the area of technology.

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1.2 Research objectives

Definition of Foodie application: Online/Mobile applications related to food reviews,

food delivery, and restaurant directory.

1.2.1 To identify customer profiles and classify them into segments

A. Demographic: Age, Gender, Marital status, Education, Income level,

Residential area, and Occupation, etc.

B. Behavioral: Internet usage duration, Type of internet connection, Type

of device, Application usage, Eating out frequency, etc.

C. Psychological (Lifestyle): Activities, Interest, and Opinion (AIO).

1.2.2 To determine consumer restaurant selection behavior based on past experiences

A. To determine purchase behavior including order frequency,

spending/bill, etc.

B. To determine factors that stimulate usage of foodie applications.

C. To identify restaurant selection criteria using foodie applications.

1.2.3 To identify the level of importance of features and user perceptions about foodie

applications

A. To identify the important features of foodie applications perceived by

users.

B. To identify users’ perceptions toward each application available in the

market.

1.2.4 To determine consumers’ price perceptions toward online order fees

A. To determine consumers’ perceptions of current fees charged by online

food ordering providers in Thailand.

B. To determine customer price sensitivity.

C. To identify the impact of price promotion on consumers’ purchase

intentions.

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CHAPTER 2

REVIEW OF LITERATURE 2.1 Restaurant delivery system

The goal of most businesses is to make a profit. It is the same for restaurants

that are not non-profit organizations. According to Matthew (2015), technology

makes it much easier for restaurants to increase sales and revenue through the

application of online delivery services. Many restaurants are adapting to new

technologies at a breakneck rate. Formerly, restaurants operated their own food

delivery services. However, many intermediaries now exist primarily to provide food

delivery to customers as “Third-party delivery services.” Third-party delivery exists

to ease the burden of restaurants operating delivery services at their own cost

(Matthew, 2015).

2.2 Thailand internet usage and customer changing behavior

Thailand is geographically located in Southeast Asia where the internet usage

and Electronic commerce is increasing. People in Southeast Asia spend three hours

and thirty minutes daily on their mobile phones. Interestingly, Thai people spend four

hours and ten minutes on average per day online which is longer compared to the

people in the same region (Anandan & Sipahimalani, 2017). This higher use of the

internet in Thailand can indicates that Thai people’s way of living and behavior has

changed. Online delivery service is popular in Thailand. Thai people have increased

usage of online delivery services from third-party online delivery providers rapidly

due to various factors including the need for convenience, time-saving, and avoiding

driving through bad traffic (Kasikornresearch, 2016).

2.3 Online spending in Thailand

In Thailand, average internet usage on all combined devices is around four to

seven hours daily. People of different ages have slight differences in the internet

usage time. Minimum daily internet usage is four hours for the older adults ((ETDA),

2017).

Thai people utilize the internet mainly for social media, gaming, entertainment, and

reading. They spend most frequently on fashion products, beauty products, and IT

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equipment at 44%,33%, and 26%, respectively; however, online food delivery

accounts for only 18.7%, and 77% of people who order food online have an average

bill of less than 1,000 baht ((ETDA), 2017).

2.4 Online food delivery service providers in Thailand

The first online delivery service provider to be introduced in Thailand was

Foodpanda which launched in 2012 and became very successful until “LINEMAN”

by LINE company joined the market in 2016. Lineman became very strong and

dominated the market soon after launch due to its massive user database on “LINE”

application which is the most used chat application in Thailand. More importantly,

LINEMAN successfully established a business collaboration with Wongnai, a leading

restaurant review platform, making its leading position unshakable by other

applications in the market. In 2017, UBER, a giant tech start-up in transportation,

joined the market under the name of “UberEATS” (Euromonitor, 2017). From the

above, we can see that online food delivery businesses are attractive as there are

always new players wanting to join the market.

2.5 Customer decision-making process

Ensuring customer loyalty is difficult but attracting a new customer is much

harder and costly to manage. Customers must move through stages of the marketing

funnel (Figure 2.1) from merely being aware to highly loyal (Kotler & Keller, 2016).

Therefore, restaurants need to be more efficient in operation, attracting new customers

and retaining existing patrons. Moreover, restaurants need to understand their target

market when using online tools and channels to be able to communicate more

efficiently.

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Figure 2.1: The marketing funnel, (Kotler & Keller, 2016)

2.6 Social Media, user-generated content and its effects on purchase intention

There are many definitions of social media given by experts. One research

project referred to social media as “a platform that allows users to generate content or

interact on the internet” (Kaplan & Haenlein, 2010). This user-generated content (e.g.,

hotel reviews and restaurant reviews), has an effect on consumers’ purchase

intentions. Higher reviews and rating can have an impact on the number of orders.

Average increases in the number of orders of products that have high volume reviews

and ratings are 10%-15% depending on the product category (Bazaar voice, 2017).

For a restaurant to have more customers and better store traffic, the manager must

build positive word-of-mouth which can be defined as “spoken communication as a

means of transmitting information” (Oxford Dictionary, 2017). Restaurants need to

understand how to manage both positive and negative feedback from the customer.

Additionally, electronic word-of-mouth (eWOM) is sharing of information about the

product, either in positive or negative ways, through the internet by current and past

customers (Hennig-Thurau, 2004). Social media and the online communities enable

customers to share their reviews, rating, and photos that are accessible by almost

anyone who has access to the internet. Research found that eWOM messages and

comments influence a consumer’s willingness to buy (Xiaofen, 2009). Online reviews

may contain many sentiments including satisfied, dissatisfied, and neutral. However,

they are sometimes fake and widely available on many review or rating websites.

Surprisingly, some firms are even willing to pay to professional reviewers to appraise

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their products to create awareness and get attention from the crowd. Moreover,

customers seem to respond to ratings and reviews better than their own discoveries

from the internet searches (Senecal, Nantel, & Jacques, 2004).

2.7 Summary

Thailand has the highest number of internet users in Southeast Asia. Thais

spend about four hours daily on the internet. The increasing trend of internet usage

and other factors, (e.g., time constraint, traffic congestion, and the need for

convenience) has changed the way of life from eating out at restaurants to online

ordering for home delivery. In the past, only a few restaurants were capable of food

delivery by their in-house delivery units. However, today, restaurants can enjoy the

support of a wide variety of online food delivery providers that can help to boost

revenue and expand customer bases. One expert has predicted that online delivery

providers and applications will promote and assist the restaurant industry to grow

significantly despite the current bad economic situation and fierce competition.

Moreover, social media and the online communities enables internet users to

share their opinions and experiences on a product or service in either a positive or

negative tone. Online communities are a new challenge and at the same time a golden

opportunity for restaurants to attain more exposure and increased their customer base.

Consumers can read reviews and other customers experiences through online channels

and then make their decision to purchase from the best company.

This review of the literature identified some gaps which included foodie

application users’ profiles and segmentation, consumer behaviors in restaurant

selection, the level of importance of each feature, which features can be improved,

and lastly consumers’ price sensitivity toward online delivery fees.

Therefore, this study will address these current research gaps and create a

valuable contribution to the restaurant industry as a critical foreign exchange earner

that is tied directly to the travel and tourism sector. Additionally, this study will assist

application developers to better understand and comprehend how customers perceive

current application features and settings to enable them to further improve their

services.

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CHAPTER 3

RESEARCH DESIGN

3.1 Research Methodology

The research methodology that was applied to conduct this research was both

exploratory research and descriptive research to ensure that all the objectives were

achieved.

3.1.1 Exploratory Research Design

Exploratory research was conducted by secondary data reviews and in-depth

interviews with the objective of the latter to study and predict factors for designing the

online questionnaire.

3.1.2 Secondary Data Research

Secondary data research was conducted to study current trends in the

restaurant businesses including internet usage, criteria customers use when choosing a

restaurant, and also to identify variable used in the questionnaire. Data were obtained

from credible sources including university journals, the Department of Business

Development (DBD), the Electronic Transactions Development Agency (ETDA),

Euromonitor International, the Royal Thai Embassy, newspapers, and websites.

3.1.3 In-depth interviews

Participants in the in-depth interviews were recruited using convenience

sampling. The objective of the in-depth interviews was to understand why customers

used online foodie application services. Insight gained from the interviews was

utilized and applied to the development of the online questionnaire survey for data

collection. Questions used for the in-depth interviews are listed in Appendix A.

3.2 Descriptive Research Design

Descriptive research was conducted by an online questionnaire survey. Target

samples for the online survey were selected by non-probability (i.e., convenience)

sampling.

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3.2.1 Questionnaire survey

The questionnaire was designed based on the secondary research data and

insights gathered from the in-depth interviews. Data obtained from the questionnaire

surveys were further analyzed as research findings. Questions used for the online

survey are listed in Appendix B.

3.3 Data collection

3.3.1 Qualitative data

In-depth interviews: A total of 10 subjects were interviewed between February

13, 2018, and February 28, 2018. The location used to conduct interviews was at

Starbucks Coffee at Central World Department store in Bangkok. The in-depth

interviews were conducted on a one on one and two on one basis. Open-ended

questions were asked to encourage participants to share their experiences and

opinions freely. Each respondent took between 10 and 20 minutes to answers all the

questions.

3.3.2 Quantitative data

Online questionnaire survey: The questionnaire survey was conducted through

the online survey platform called SurveyMonkey. Criteria for respondents were those

15 years old or older who had used a foodie application delivery service in the past 30

days. A total of 265 respondents completed the online survey. The online

questionnaires were distributed on social media (e.g., Facebook, Line Chat, and

respondent’s e-mail). Data collection period was from February 13, 2018, until

February 28, 2018.

3.4 Data Analysis

Results from quantitative data were analyzed by using the Statistical Package

for the Social Sciences (SPSS) program. Statistical methods used included Analysis of

Variance (ANOVA), means, standard deviation, custom table, frequency, factor

analysis, cluster analysis, and price sensitivity measurements.

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3.5 Theoretical Framework To study consumers’ restaurant selection decisions, the researcher decided to

gather characteristic data, experience, price perception, and service provider

perception to determine how customers made their final online selection decision on a

restaurant. (Figure 3.1)

Figure 3.1: Research’s framework

3.6 Limitations of the study

Due to time, budget, and resource constraints, the findings cannot be

generalized to the entire population because the survey was conducted by non-

probability sampling. Moreover, the samples were obtained by convenience sampling

via online channels.

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CHAPTER 4

RESULTS AND DISCUSSION

4.1 Key findings from Secondary Research

Based on the secondary research results, the trend of online food delivery is

growing rapidly in Thailand. People are much happier with their meals because of the

availability of foodie applications. Moreover, Thailand is a popular tourist destination,

and this positively affects the operation of the restaurants. However, the important

question for restaurant owners is how to adapt these recent changes. This is a

challenging time for business owners to react to the changes and continue to grow

within this new environment. Customers are more demanding about what they eat.

Satisfactory or unsatisfactory experiences can be shared on social media and spread

rapidly through the community. Therefore, it is crucial to understand how consumers

are thinking and predict their needs to serve them better.

4.2 Key findings from In-depth Interviews

The in-depth interviews were conducted with ten interviewees as following:

1. 32 years old, Male, Marketing executive

2. 32 years old, Male, Helicopter Pilot

3. 32 years old, Male, Commercial Pilot

4. 29 years old, Male, Telecommunication

5. 25 years old, Male, Freelancer

6. 28 years old, Female, sales officer

7. 26 years old, Female, Hotel’s employee

8. 27 years old, Female, Account executive

9. 28 years old, Female, Secretary

10. 32 years old, Female, Marketing manager

One of the female interviewees stated that factors that influencing her to use

online delivery were convenience, occasion, and price promotions. Most male

interviewees mentioned that the delivery fee per order was expensive due to the fact

that the delivery location was far from the preferred restaurant. Interestingly, almost

all the interviewees shared common ideas regarding the best way of ordering food via

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the online applications. They all ordered a large amount of food to get value for

money for the delivery fee paid. When respondents order food from an online delivery

service, they first browse through various restaurants available on the TOP 10 list

suggested by the application, and most will choose a restaurant from this provided

list.

One of the interviewees was a heavy user of online foodie applications. He

stated that he used the application mainly on his mobile. His reasons for using an

online delivery service were convenience, availability, and avoiding long queues. He

stated that the order quantity depended on delivery fee; if the fee was high, his order

portions will be higher. Ordering as a group, especially at his office, was likely to cost

more than ordering food to eat at home. Another male interviewee stated that he knew

what he was going to order, so it was not important to read customer reviews before

selecting a restaurant available in the application. One of the interviewees stated that

he easily switched the application to the one offering the cheapest delivery fee.

Interestingly, this idea was common among all the interviewees. Moreover, each

interviewee was asked about their feelings toward services from each application as

they all had different experiences with each application. Some respondents

complained about the availability of cash change when the food was delivered at

home. Some complained about the service coverage of one application that made

them switch to another. Lastly, one of the interviewees suggested that it would be

better if all application fees could be paid via credit or debit card.

Insight and information gathered from both secondary data research and in-

depth interviews were analyzed by the researcher and were used to complie questions

asked in the questionnaire survey.

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4.3 Key findings from the questionnaire survey 4.3.1 General Profile of Respondents

A total of 356 respondents attempted the online questionnaire, while 265

respondents completed the survey at a completion rate of 74%.

All 265 respondents were over 15 years old and had used an online foodie

application within the last month at the time they completed the survey.

4.3.2 Respondents’ Demographic profiles

From Table 4.1, the majority of the respondents were female at 62% with most

distributed into three age groups as 21-28 (27.5%), 29-35 (33.6%), and over 36

(27.2%). More than half the respondents were single (58%). The highest education

most respondents possessed was a bachelor’s degree (62.3%) followed by master’s

degree (26%). For occupation, 40% of respondents worked as a private company’s

employees while government officers and business owners accounted for 32.8% of all

respondents. In terms of income per month, 28.7% of respondents had a monthly

income of 10,001-15,000 baht and 23.8% had a monthly income of 15,001-

30,000baht. For resident type, 53.2% of respondents lived in a house, followed by

condominium at 24.2%. Results indicated that 32.5% lived alone, 30.6% lived as a

couple, and 22.3% lived with their parents. (Table 4.1).

Table 4.1: All respondents’ demographic profiles by frequency and percentage

All respondents' Demographic (n=265) Count %

What is

your

gender?

Male 100 37.7%

Female 165 62.3%

AgeGroup

16-20 year 31 11.7%

21-28 year 73 27.5%

29-35 year 89 33.6%

36-60 year 72 27.2%

What is Single 154 58.1%

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your

marital

status?

Married 93 35.1%

Divorced 14 5.3%

Widowed 2 .8%

Other 2 .8%

What is

your

highest

education?

Elementary school

or lower 1 .4%

High School 29 10.9%

Bachelor’s Degree 165 62.3%

Master’s Degree or

higher 69 26.0%

Other 1 0.40%

What is

your

occupation?

Student 37 14.0%

Unemployed 8 3.0%

Employees 106 40.0%

Government

employee 44 16.6%

Housewife/husband 5 1.90%

Business Owner 43 16.2%

Freelance 22 8.3%

How much

is your

monthly

income?

≤ 10,000 baht 26 9.8%

10,001-15,000 baht 76 28.7%

15,001-30,000 baht 63 23.8%

30,001-50,000 baht 59 22.3%

More than 50,000

baht 41 15.5%

Where do

you live?

Home 141 53.2%

Condominium 64 24.2%

Apartment 60 22.6%

Who do

you live

with?

Alone 86 32.5%

Relatives 24 9.10%

Parents 59 22.3%

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15

Friends 15 5.70%

Couple/Partner 81 30.6%

4.3.3 Foodie application users’ segmentation

To determine customer segmentation, eight psychological attributes were

reduced to three factors by factor analysis (Table 4.2). The three factors with

attributes’ loading scores over 0.5 were active lifestyle, sociable, and outdoor lover.

Active lifestyle: This factor described the psychological attributes that involve active

lifestyle aspects of the customers and included people who were active, perfectionists,

and get things done on time.

Sociable: This factor described the psychological attributes that involved the social

aspects of the customers and included opinion sharing, being a good listener, and a

love for good food.

Outdoor lover: This factor described the psychological attribute involving a love for

outdoor activities.

Table 4.2: Factor Analysis from psychological attributes

8 Psychological

attributes

3 Psychological factors

(1) Active

lifestyle (2) Sociable

(3) Outdoor

lover

(1) I am an active person .817

(2) I am a perfectionist .823

(3) I always get things

done on time .789

(4) I am very busy

(5) I love to eat good food .524

(6) I always share my

opinion .842

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(7) I prefer to listen than

speak .705

(8) I love Outdoor

activities .874

4.3.4 Customer segments

The three clusters were assessed by K-means cluster analysis, (see Appendix

C-1), to differentiate the customers into four psychological segments. Table 4.3 lists

each customer segment as achiever, perfectionist, extrovert, and outdoor enthusiast.

The segments can be identified as follows:

Achiever (n=58): Achievers strive for the best in their life. They pay attention to

details, are quick to take actions, and finish their tasks on time. They love hanging out

with friends or family at a good restaurant to talk and share life experiences. They

enjoy outdoor activities and are not a home-loving person. Achievers accounted for

21.8% of total respondents.

Perfectionist (n=59): Perfectionists have an active lifestyle. They pay attention to

detail, are quick to take actions, and finish their tasks on time. Perfectionists

accounted for 22.2% of total respondents.

Extrovert (n=84): Extroverts love to socialize. They share their opinions with the

community while remaining open-minded to alternative viewpoints. Extroverts

accounted for 31.7% of total respondents.

Outdoor Enthusiast (n=64): Outdoor enthusiasts enjoy outdoor activities. They

prefer going out rather than staying at home. Outdoor enthusiasts accounted for 24.1%

of total respondents.

Table 4.3: Number of respondents in each segment by frequency

Number of respondents

in each segment Count %

(1) Achiever 58 21.8%

(2) Perfectionist 59 22.2%

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(3) Extrovert 84 31.7%

(4) Outdoor enthusiast 64 24.1%

Total respondents 265 100%

4.3.5 General Profile of each Customer Segment

General profiles of each customer segment are listed in Table 4.4 based on

their demographics. Frequency analysis was also conducted on behavioral aspects to

depict the profile of each segment. (see Appendix C-2)

Table 4.4: Each customer segments by demographic profile

4 Clusters' demographic

profile

Achiever

(n=58)

Perfectionist

(n=59)

Extrovert

(n=84)

Outdoor

Enthusiast

(n=64)

n % n % n % n %

What is

your

gender?

Male 19 32.8% 18 30.5% 38 45.2% 25 39.1%

Female 39 67.2% 41 69.5% 46 54.8% 39 60.9%

Others 0 0.0% 0 0.0% 0 0.0% 0 0.0%

58 100.0% 59 100.0% 84 100.0% 64 100.0%

AgeGroup 16-20 year 7 12.1% 3 5.1% 13 15.5% 8 12.5%

21-28 year 13 22.4% 17 28.8% 25 29.8% 18 28.1%

29-35 year 17 29.3% 21 35.6% 30 35.7% 21 32.8%

36-60 year 21 36.2% 18 30.5% 16 19.0% 17 26.6%

58 100.0% 59 100.0% 84 100.0% 64 100.0%

What is

your

marital

status?

Single 33 56.9% 31 52.5% 53 63.1% 37 57.8%

Married 20 34.5% 25 42.4% 26 31.0% 22 34.4%

Divorced 3 5.2% 2 3.4% 4 4.8% 5 7.8%

Widowed 2 3.4% 0 0.0% 0 0.0% 0 0.0%

Other 0 0.0% 1 1.7% 1 1.2% 0 0.0%

58 100.0% 59 100.0% 84 100.0% 64 100.0%

What is

your

highest

education?

Elementary school

or lower 0 0.0% 0 0.0% 0 0.0% 1 1.6%

High School 2 3.4% 4 6.8% 12 14.3% 11 17.2%

Bachelor’s Degree 43 74.1% 34 57.6% 52 61.9% 36 56.3%

Master’s Degree or 13 22.4% 20 33.9% 20 23.8% 16 25.0%

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higher

Other 0 0.0% 1 1.7% 0 0.0% 0 0.0%

58 100.0% 59 100.0% 84 100.0% 64 100.0%

What is

your

occupation?

Others 0 0.0% 0 0.0% 0 0.0% 0 0.0%

Student 6 10.3% 4 6.8% 17 20.2% 10 15.6%

Unemployed 1 1.7% 2 3.4% 4 4.8% 1 1.6%

Employees 31 53.4% 30 50.8% 28 33.3% 17 26.6%

Government

employee 10 17.2% 6 10.2% 15 17.9% 13 20.3%

Housewife/husband 0 0.0% 1 1.7% 2 2.4% 2 3.1%

Business Owner 6 10.3% 12 20.3% 12 14.3% 13 20.3%

Freelance 4 6.9% 4 6.8% 6 7.1% 8 12.5%

58 100.0% 59 100.0% 84 100.0% 64 100.0%

How much

is your

monthly

income?

≤ 10,000 baht 4 6.9% 5 8.5% 9 10.7% 8 12.5%

10,001-15,000 baht 15 25.9% 10 16.9% 31 36.9% 20 31.3%

15,001-30,000 baht 20 34.5% 10 16.9% 15 17.9% 18 28.1%

30,001-50,000 baht 12 20.7% 18 30.5% 16 19.0% 13 20.3%

More than 50,000

baht 7 12.1% 16 27.1% 13 15.5% 5 7.8%

58 100.0% 59 100.0% 84 100.0% 64 100.0%

Where do

you live?

Other 0 0.0% 0 0.0% 0 0.0% 0 0.0%

Home 28 48.3% 38 64.4% 48 57.1% 27 42.2%

Condominium 14 24.1% 11 18.6% 20 23.8% 19 29.7%

Apartment 16 27.6% 10 16.9% 16 19.0% 18 28.1%

58 100.0% 59 100.0% 84 100.0% 64 100.0%

Who do

you live

with?

Other 0 0.0% 0 0.0% 0 0.0% 0 0.0%

Alone 20 34.5% 12 20.3% 26 31.0% 28 43.8%

Relatives 4 6.9% 3 5.1% 11 13.1% 6 9.4%

Parents 10 17.2% 21 35.6% 22 26.2% 6 9.4%

Friends 5 8.6% 3 5.1% 3 3.6% 4 6.3%

Couple/Partner 19 32.8% 20 33.9% 22 26.2% 20 31.3%

58 100.0% 59 100.0% 84 100.0% 64 100.0%

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4.3.6 Psychographic profile by segment

Psychological aspects of customers were analyzed by means and standard

deviations among each customer segment (see Appendix C-3). Furthermore, one-way

analysis of variance (ANOVA) was run to test for significant differences in terms of

psychological

characteristics among each customer segment at a significance level of 0.05

(see Appendix C-4).

All eight psychological attributes were significantly different among the four

customer segments including “I am active person” (F(3,261) = 45.8, p < .05), “I am a

perfectionist” (F(3,261)= 44.5, p < .05), “I always get things done on time”

(F(3,261)= 50.5, p < .05), “I am very busy” (F(3,261)= 25.7, p < .05), “I love to eat

good food” (F(3,261)= 53.3, p < .05), “I always share my opinion” (F(3,261)= 52.6, p

< .05), “I prefer to listen than speak” (F(3,261)= 72.2, p < .05), and “I love outdoor

activities” (F(3,261)= 82.1, p < .05).

“I am active person”: Mean scores for the Achiever segment (MAchiever = 3.93) and

the Perfectionist segment (MPerfectionist= 3.75) were significantly higher than the mean

score for either the Extrovert segment (MExtrovert= 2.90) or the Outdoor enthusiast

segment (MOutdoor enthusiast=2.91).

“I am a perfectionist”: Mean scores for the Perfectionist segment (MPerfectionist= 4.00)

and the Achiever segment (MAchiever =3.90) were significantly higher than the mean

score for either the Extrovert segment (MExtrovert= 3.25) or the Outdoor enthusiast

segment (MOutdoor enthusiast=2.77).

“I always get things done on time”: Mean scores for the Perfectionist segment

(MPerfectionist= 4.00) and the Achiever segment (MAchiever =3.86) were significantly

higher than the mean score for either the Extrovert segment (MExtrovert= 3.25) or the

Outdoor enthusiast segment (MOutdoor enthusiast=2.56).

“I am very busy”: Mean scores for the Achiever segment (MAchiever = 3.81), the

Perfectionist segment (MPerfectionist = 3.61), and the Extrovert segment (MExtrovert

=3.71) were significantly higher than the mean score for the Outdoor segment

(MOutdoor enthusiast = 2.69).

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“I love to eat good food”: Mean scores for the Extrovert segment (MExtrovert = 4.60),

the Perfectionist segment (MPerfectionist = 4.29), and the Achiever segment (MAchiever

=4.24) were significantly higher than the mean score for the Outdoor segment

(MOutdoor enthusiast = 3.09).

“I always share my opinion”: Mean scores for the Achiever segment (MAchiever =

4.59) and the Extrovert segment (MExtrovert =4.54) were significantly higher than the

mean score for either the Outdoor segment (MOutdoor enthusiast = 3.56) or the

Perfectionist enthusiast segment (MPerfectionist enthusiast=3.49).

“I prefer to listen than speak”: Mean scores for the Achiever segment (MAchiever =

4.67), the Extrovert segment (MExtrovert = 4.15), and the Outdoor segment (MOutdoor

enthusiast =4.08) were significantly higher than the mean score for the Perfectionist

segment (MPerfectionist = 2.97).

“I love Outdoor activities”: Mean scores for the Achiever segment (MAchiever = 4.59)

and the Outdoor segment (MOutdoor =4.41) were significantly higher than the mean

score for either the Extrovert segment (MExtrovert = 3.14) or the Perfectionist segment

(MPerfectionist =3.10).

4.3.7 Restaurant Selection behavior by customer’s segments

A Chi-square test was run to test for significant differences in terms of

restaurant selection behavior among each customer segment. The Chi-square test

revealed no significant differences in behavior among each customer segment for

either “Time visit to restaurant per month” (x² (9) = 13.43, p = 0.14) or “Meal of the

day at restaurant” (x² (6) = 6.41, p = 0.37) (Table 4.5).

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Table 4.5: Chi-square test on restaurant selection behavior on customer’s

segments

Restaurant selection behavior Achiever

(n=58)

Perfectionist

(n=59)

Extrovert

(n=84)

Outdoor

enthusiast

(n=64)

Total

How many time do

you go to the

restaurants per

month?

1-3 time 28 25 38 33 124

4-6 time 25 20 27 19 91

More than 7

time 5 12 19 12 48

Never 0 2 0 0 2

Total 58 59 84 64 265

What meal of the day

do you usually go to a

restaurant?

Breakfast 9 5 16 8 38

Lunch 27 21 34 27 109

Dinner 22 33 34 29 118

Total 58 59 84 64 265

Chi-Square Tests Value df

Asymptotic

Significance

(2-sided)

Pearson Chi-Square

13.436a 9 .144

6.410a 6 .379

4.3.8 Restaurant selection criteria

Restaurant selection criteria were analyzed by means and standard deviations

among each customer segment (see Appendix D-1). Furthermore, ANOVA was run to

test if there are significant differences in terms of psychological characteristics among

each customer segment at a significance level of 0.05 (see Appendix D-2).

All respondents were asked to identify to what extent they placed the level of

importance towards each restaurant selection criterion using a Likert scale.

Considering the top three restaurant selection criteria, the results showed that the

mean score of “Speed of service” was the highest. Among other selection criteria,

“Speed of service” attained a mean score of 3.97, followed by “Location”, and “Value

for money” with average mean scores of 3.92, and 3.77, respectively.

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For restaurant selection criteria perception result among each customer

segment, refer to Appendix D-2. All six restaurant selection criteria showed

significant differences among the four customer segments including “Convenience”

(F(3,261) = 5.4, p < .05), “Food taste” (F(3,261) = 6.0, p < .05), “Cleanliness”

(F(3,261) = 6.2, p < .05), “Value for money” (F(3,261) = 6.0, p < .05), “Location”

(F(3,261) = 4.8, p < .05), “Speed of service” (F(3,261) = 7.0,p < .05).

“Convenience”: The mean score for the Achiever segment (MAchiever = 3.66) and the

Perfectionist segment (MPerfectionist =3.78) were significantly higher than the mean

score for the Outdoor segment (MOutdoor = 3.28). Additionally, the mean score for the

Perfectionist segment was also significantly higher than the mean score for the

Extrovert segment (MExtrovert = 3.39).

“Food taste”: The mean score for the Perfectionist segment (MPerfectionist = 3.98) was

significantly higher than the mean score for both the Extrovert segment (MExtrovert =

3.49) and the Outdoor segment (MOutdoor = 3.31).

“Cleanliness”: The mean score for the Perfectionist segment (MPerfectionist = 3.98) was

significantly higher than the mean score for the Outdoor segment (MOutdoor = 3.25).

“Value for money”: The mean score for the Perfectionist segment (MPerfectionist =

3.98), the Extrovert segment (MExtrovert = 3.49), and the Achiever segment (MAchiever =

3.66) were significantly higher than the mean score for the Outdoor segment (MOutdoor

= 3.38).

“Location”: The mean score for the Extrovert segment (MExtrovert = 4.12) and the

Achiever segment (MAchiever = 4.12) were significantly higher than the mean score for

the Outdoor segment (MOutdoor = 3.59).

“Speed of services”: The mean score for the Extrovert segment (MExtrovert = 4.27) and

the Achiever segment (MAchiever = 4.10) were significantly higher than the mean score

for the Outdoor segment (MOutdoor = 3.55).

4.3.9 Key attributes that stimulates usage decision of foodie applications

All respondents were asked to identify to what extent they place the level of

importance towards each usage decision attribute using a Likert scale. Considering the

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top three key usage decision attributes, the results showed that the mean score of

“Service coverage” was highest one among the other usage decision attributes with

the average mean score of 4.22, followed by “Status tracking”, and “Payment option”

with the average mean scores of 4.08, and 3.79, respectively (Table 4.6).

Table 4.6: All respondents' usage decision attributes for foodie application by

mean score

All Respondents' key usage decision

attributes for foodie application

(n = 265)

Mean Standard

Deviation

Ease of use 3.50 .94

Time-saving 3.55 .89

Restuarant data completeness 3.52 .91

Payment option 3.79 .94

Status tracking (Ordering food from App) 4.08 .91

Service coverage (Ordering food from App) 4.22 .89

4.3.10 Key usage decision attributes by customer segments

Key usage decision attributes were analyzed by means and standard deviations

among each customer segment (see Appendix D-3). Furthermore, one-way ANOVAs

were run to test for significant differences in terms of key usage decision attributes

among each customer segment at a significance level of 0.05 (see Appendix D-4).

The four key usage decision attributes were significantly different among the

four customer segments including “Ease of use” (F(3,261) = 8.4, p < .05), “Time

saving” (F(3,261) = 16.1, p < .05), “Restaurant data completeness” (F(3,261) = 10.9,

p < .05), and “Payment option” (F(3,261) = 5.9, p < .05). Multiple comparisons of

each usage decision attribute among each group can also be found in Appendix D-4.

4.3.11 Restaurant selection criteria via foodie application

All respondents were asked to identify to what extent they placed the level of

importance towards restaurant selection criteria via foodie applications using a Likert

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scale. Considering the top three restaurant selection criteria via foodie applications,

results showed that the mean score of “Appropriate price” was highest one among

other usage decision attributes with the average mean score of 4.17, followed by

“Variety of menus”, and “Location” with the average mean scores of 3.82, and 3.32,

respectively (Table 4.7).

Table 4.7: All Respondents' restaurant selection criteria via applications by

mean score

All Respondents' restaurant selection criteria

by means of foodie application (n = 265)

Mean Standard Deviation

Beautiful photo 3.18 .81

Good reviews 3.31 1.00

Location (Near me) 3.32 1.02

Variety of menus 3.82 1.00

Appropriate price 4.17 .89

4.3.12 Mean comparison of key restaurant selection criteria via applications by

customer segments

Key usage decision attributes were analyzed by means and standard deviations

among each customer segment (see Appendix D-5). Furthermore, one-way ANOVAs

were run to test if there were significant differences in terms of key usage decision

attributes among each customer segment at a significance level of 0.05 (see Appendix

D-6).

Key usage decision attributes (refer to Appendix D-6) for three restaurant

selection criteria via foodie application were significantly different among the four

customer segments including “Beautiful photo” (F(3,261) = 8.7, p < .05), “Good

reviews” (F(3,261) = 16.6, p < .05), “Restaurant data completeness” (F(3,261) = 10.9,

p < .05), and “Location” (F(3,261) = 6.1, p < .05). Multiple comparisons of each

usage decision attribute among each group can also be found in Appendix D-6.

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4.3.13 Importance of application features

All respondents were asked to identify to what extent they placed the level of

importance towards each application feature using a Likert scale. Considering the top

three application features, the results showed that the mean score of “Booking

system” was highest one among the other usage decision attributes with the average

mean score of 3.96, followed by “Payment option”, and “Promotion information”

with the average mean scores of 3.95, and 3.85 respectively (Table 4.8).

Table 4.8: All Respondents' key application features by mean score

All Respondents' key application features (n = 265)

Mean Standard Deviation

Menus & Price 3.46 .81

Restaurant business hour 3.46 .83

Restaurants database 3.47 .86

Review & Rating 3.69 .82

Original content from application 3.75 .92

Restaurant Booking system 3.96 .94

Payment Option 3.95 .93

Promotion information 3.85 1.01

4.3.14 Mean comparison of key application features by customer segments

Key usage decision attributes were analyzed by means and standard deviations

among each customer segment (see Appendix E-1). Furthermore, one-way ANOVAs

were run to test for significant differences in terms of key usage decision attributes

among each customer segment at a significance level of 0.05 (see Appendix E-2).

Six application features were significantly different among the four customer

segments including “Menu Price” (F(3,261) = 15.7, p < .05), “Restaurant business

hour” (F(3,261) = 13.4, p < .05), “Restaurant database” (F(3,261) = 3.9, p < .05),

“Original content from application” (F(3,261) = 4.5, p < .05) , “Restaurant booking

system” (F(3,261) = 6.6, p < .05) , and “Payment option” (F(3,261) = 4.7, p < .05).

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Multiple comparisons of each usage decision attributes among each group can also be

found in Appendix E-2.

4.3.15 Respondents’ awareness of online delivery application in the market

All respondents were asked about brand awareness of online delivery

applications which included GrabFood, UBER EATS, foodpanda, and LINEMAN.

Results showed that among the four brands LINEMAN had the highest brand

awareness (78%) followed by foodpanda and UBER EATS whose brand awareness

was identical (72%). At the same time, GrabFood scored lowest in terms of

customers’ brand awareness (63%) (Table 4.9).

Table 4.9: Online delivery application awareness by frequency and percentage

All respondents' awareness of

online delivery application

(n=265)

Grab

Food %

UBER

EATS %

food

panda %

Line

man %

Which online

delivery application

do you know?

Selected 166 63% 191 72% 192 72% 207 78%

4.3.16 Respondent’s perception on each application

4.3.16.1 LINEMAN Application’s Perception

Respondents who used the LINEMAN service were asked to identify to what

extent they placed the level of appropriateness of each attribute using a Likert scale.

Considering the top two key attributes, results showed that the mean score of

“Application interface” was highest among the other usage decision attributes with

the average mean score of 4.23, followed by “Payment option” with the average mean

score of 3.97 (Table 4.10).

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Table 4.10: LINEMAN application’s perception by mean score

Respondents' perception toward delivery service N Mean Std.

Deviation

LINEMAN

Restaurant

availability 155 3.68 0.83

Service area

coverage 155 3.81 0.78

Payment option 155 3.97 0.81

Application

interface 155 4.23 0.77

4.3.16.2 GrabFood Application’s Perception

Respondents who used the GrabFood service were asked to identify to what

extent they placed the level of appropriateness of each attribute using a Likert scale.

Considering the top two key attributes, the result showed that the mean score of

“Application interface” was highest among the other usage decision attributes with

the average mean score of 4.22, followed by “Service area coverage” with the average

mean score of 3.76 (Table 4.11).

Table 4.11: GrabFood application’s perception by mean score

Respondents' perception toward delivery service N Mean Std.

Deviation

GrabFood

Restaurant

availability 83 3.65 0.88

Service area

coverage 83 3.76 0.84

Payment option 83 3.73 0.93

Application

interface 83 4.22 0.87

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4.3.16.3 UBER EATS Application’s Perception

Respondents who used the UBER EATS service were asked to identify to

what extent they place the level of appropriateness of each attribute using a Likert

scale. Considering the top two key attributes, the result showed that the mean score of

“Application interface” was highest among the other usage decision attributes with

the average mean score of 3.99, followed by “Payment option” with the average mean

score of 3.82 (Table 4.12).

Table 4.12: UBER EATS application’s perception by mean score

Respondents' perception toward delivery service N Mean Std.

Deviation

UBER EATS

Restaurant

availability 72 3.56 0.87

Service area

coverage 72 3.67 0.87

Payment option 72 3.82 0.81

Application

interface 72 3.99 0.94

4.3.16.4 foodpanda Application’s Perception

Respondents who used foodpanda service were asked to identify to what

extent they placed the level of appropriateness of each attribute using a Likert scale.

Considering the top two key attributes, the result showed that the mean score of

“Application interface” was highest among the other usage decision attributes with

the average mean score of 4.14, followed by “Payment option” with the average mean

score of 3.83 (Table 4.13).

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Table 4.13: foodpanda application’s perception by mean score

Respondents' perception toward delivery service N Mean Std.

Deviation

foodpanda

Restaurant

availability 78 3.62 0.74

Service area

coverage 78 3.72 0.72

Payment option 78 3.83 0.80

Application

interface 78 4.14 0.80

4.3.17 Respondents’ perceptions toward fees charged by online food delivery

applications

Respondents who used an online delivery service were asked to identify to

what extent they placed the level of appropriateness of price charged by each brand

using a Likert scale. Results showed that the mean score of “Grab Food” was highest

among the other usage decision attributes with the average mean score of 3.52,

followed by “foodpanda” with the average mean score of 3.46 (Table 4.14).

Table 4.14: Respondents’ perception toward fees charged by mean score

Respondents' perception toward service fee N Mean Std.

Deviation

LINEMAN

Service fee

155 3.41 0.81

GrabFood 83 3.52 0.79

UBER EATS 72 3.44 0.82

foodpanda 78 3.46 0.88

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4.3.18 Price sensitivity Measurement

All respondents were asked to state their opinions on four questions regarding

pricing. The questions were “how much they thought was cheap, too cheap,

expensive, and too expensive for using a foodie application delivery service?” Results

indicated that the indifferent price point was around 100 baht. However, results

showed that the marginal cheapness or lower boundary of an acceptable price range

was 140 baht and the point of marginal expensiveness or upper boundary of an

acceptable price range was 199 baht for an online delivery fee. Interestingly, results

indicated that an optimal price point or point at which an equal number of respondents

described the price as exceeding either their upper or lower value for an online

delivery fee was 200 baht per delivery (see Figure 4.1 below).

Figure 4.1: Price sensitivity measurement

Red = Indifferent price point

Blue = Point of marginal cheapness

Black = Optimal price point

Grey = Point of marginal expensiveness

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4.3.19 Impact of price promotion on consumer purchase intentions for foodie

applications

All respondents were asked to identify to what extent they agreed that price

promotion impacted their purchase intent using a Likert scale. Results showed that the

mean score was very high at 4.22 (Table 4.15).

Table 4.15: Price promotion impact on purchase intent by mean score

All respondents ’opinion on price

promotion Mean Standard Deviation

Price promotion will make you use more

online food delivery service 4.22 .71

4.3.20 Mean comparison of price promotion impact on purchase intent by

customer segments

The same variable was analyzed by means and standard deviations among

each customer segment (see Appendix F-1). Furthermore, a one-way ANOVA was

run to test for any significant differences in terms of price promotion impact on

purchase intent among each customer segment at a significance level of 0.05 (see

Appendix F-2).

Results showed that there were no significant differences among the four

customer segments for “Price promotion impact on purchase intent” (F (3,261) = 1.6,

p > .05).

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CHAPTER 5

SUMMARY AND CONCLUSIONS 5.1 Research Summary

5.1.1 Customer Segmentation based on psychological factors

Foodie application users can be divided into four segments which are achiever,

perfectionist, extrovert, and outdoor enthusiast. Achievers are people who have an

active lifestyle and enjoy living life to the fullest. Perfectionists usually get their jobs

done on time, and they love to eat good food. Extroverts worshipped good food and

love to share their opinion with other people. Outdoor enthusiasts enjoyed outside

activities in the sun. They love to listen to stories and would also share their opinions

with others. In terms of spending power, the perfectionists have the highest income

level compared to the other customer segments.

5.1.2 Consumer restaurant selection behavior

From the 265 respondents, results showed that “Speed of service”, “Location”,

and “Value for money” were the top three criteria that gained the highest mean scores.

This indicated that the respondents lived their lives at a fast pace and were always on

the move. Therefore, the key decision factors for selecting a restaurant when not using

a foodie application remained unchanged; however, respondents required efficient and

prompt service to match with their changing lifestyle.

5.1.3 Consumer perception toward application’s features

All respondents were asked to what extent they perceived the level of

importance for each application’s feature. The top three mean scores showed that

customers pay most attention to “Booking feature”, “Variety of payment option”, and

“Promotional information”. Changing of lifestyle creates the demand for advanced

booking at the restaurants. Therefore, application developers and restaurants owners

should prepare to entice customers to use more services by taking into consideration

the above features.

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5.1.4 Consumer perception toward each brand in the market

The survey compared four key attributes when considering a foodie

application. In terms of “restaurant database”, respondents perceived that LINEMAN

was at the top. However, mean scores for each application were not significantly

different. For “service area coverage”, LINEMAN secured the first rank followed by

Grabfood, and foodpanda. Interestingly, the result of “Payment option” winner as

perceived by respondents was also LINEMAN. Although, LINEMAN does not offer

credit card payment options, respondents still ranked it as the highest by mean score.

Lastly, “application interface” recorded the same champion as LINEMAN. One brand

that should be improved in terms of the database is UBER EATS which had the

lowest mean score compared to other brands.

5.1.5 Consumer perception toward online delivery service fee

Consumer perception was tested toward online delivery fee of foodie

applications by asking four pricing questions in the survey. Results obtained from

price sensitivity measurements (PSM) were very surprising. Firstly, customers care

mainly about delivery price and will purchase more if there is a price promotion.

Secondly, the service fee appropriateness test among each brand scored a relatively

low mean score (Maverage = 3.46). This PSM result suggested that the customer

indifference price point (IPP) was 100 baht. However, in contrast, the optimal price

point (OPP) for online food delivery was 200 baht.

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5.2 Recommendations

Recommendations for each customers segment are as follows:

Achiever

This segment is a heavy user of foodie applications. Most of the respondents

in this segment used on online delivery more than five times a month. They are

addicted to the internet and more than half spend more than five hours daily online.

Restaurants and foodie applications should focus on this segment and try to engage

them through online channels since they spent the longest time on the internet. To

retain the achiever segment, the application developers could consider building an

online loyalty program to discourage them from switching to other platforms.

Perfectionist

This segment is a light user of foodie applications. However, they have the

highest income and use the internet the most compared to other segments.

Applications and restaurants could consider enticing them to try online service

through price promotions, especially on dinner. After changing the habit of this

segment, companies should give priority to improving the speed of service as this

customer segment enjoys a fast-moving lifestyle.

Extrovert

This segment contains influencers. They loved to socialize, talk, and share

opinions. They visit restaurants very often during the week but also order food online.

To attract this group to use more online delivery services, applications should focus

on creating original content on the platform because this segment loves to listen to

other people’s experiences. Restaurants should focus on the speed of service as this

group scored the highest on this aspect.

Outdoor enthusiast

This segment uses the least internet compared to the other segments. They

enjoy outdoor activities and socializing. They are interested in booking systems and

varieties of payment options. Most are working people who love the outdoor life.

Therefore, outdoor advertising could help to communicate with them.

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If the goal of the marketer is to maximize profit, the research results would

suggest capturing the achiever segment because they are heavy users of online food

delivery services. At the same time, it is crucial to convert light users to become

regular users by educating and enticing them through price promotions.

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REFERENCES

(ETDA), E. T. (2017). Thailand Internet User profile 2017. Bangkok: ETDA.

Retrieved 10 10, 2017, from https://www.etda.or.th/documents-for-download.html

Anandan, R., & Sipahimalani, R. (2017, December 12). Google. Retrieved December 12, 2017, from Blog google: https://www.blog.google/topics/google-asia/sea-internet-economy/

Bazaar voice. (2017, October 15). bazaar voice. Retrieved December 12, 2017, from Higher review volume and average rating correlate with order increases, according to a Top Internet retailer’s data: http://www.bazaarvoice.com/case-studies/Higher-review-volume-and-average-rating-correlate-with-order-increases.html

Euromonitor. (2017, May). Passport. Retrieved October 10, 2017, from Euromonitor: http://www.euromonitor.com/full-service-restaurants-in-thailand/report

Hennig-Thurau, T. a. (2004). Electronic word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate themselves on the internet? Journal of interactive marketing, 18(1), 38--52. Retrieved December 12, 2017

Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business horizons, 53, 59-68.

Kasikornresearch. (2016, December 9). Thansettakij multimedia. Retrieved December 12, 2017, from Thansettakij multimedia: http://www.thansettakij.com/content/118867

Kotler, P., & Keller, K. (2016). Marketing Management (15e ed.). Edinburgh Gate, Harlow, England: Pearson Education, Inc.

Languepin, O. (2017, May 23). Royal Thai Embassy Washington D.C. Retrieved December 10, 2017, from http://thaiembdc.org/2017/05/23/thailand-tourism-analysts-forecast-up-to-37-million-arrivals-in-2017/

Matthew. (2015, July 8). Gourmet Mktg. Retrieved December 10, 2017, from https://www.gourmetmarketing.net: https://www.gourmetmarketing.net/basics-marketing-restaurant-delivery-service/

NBTC(กสทช). (2017, October 6). Internet Users. Retrieved October 6, 2017, from NBTC: http://webstats.nbtc.go.th/netnbtc/INTERNETUSERS.php

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Oxford Dictionary. (2017, December 10). Oxford Dictionaries. Retrieved December 10, 2017, from Oxford Living Dictionaries: https://en.oxforddictionaries.com/definition/word_of_mouth

Senecal, Nantel, S. a., & Jacques. (2004). The influence of online product recommendations on consumers’ online choices. Journal of retailing, 80(2), 159-169.

World travel & Tourism council. (2017, December 13). Travel & Tourism. Economic Impact 2017, Thailand, p. 5. Retrieved December 13, 2017, from https://www.wttc.org/-/media/files/reports/economic-impact-research/countries-2017/thailand2017.pdf

Xiaofen, J. a. (2009). The Impacts of Online Word-of-mouth on. International symposium on web information systems and applications, (pp. 24-28). Nanchang,China. Retrieved December 10, 2017, from https://pdfs.semanticscholar.org/4ffd/fe6c335d6157498afd1b7691b6eddd7b951c.pdf

กองขอ้มูลธุรกิจ กรมพฒันาธุรกิจการคา้ กระทรวงพาณิชย.์ (2017, May). Restaurant Business. Retrieved October 10, 2017, from Restaurant Business: http://www.dbd.go.th/download/document_file/Statisic/2560/T26/T26_201703.pdf

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APPENDICES

Appendix A: In-depth Interview’s questions

In-depth interview questions

1. Have you ever use food application?

2. What are the reasons you decide to use food application?

3. Have you ever use Food Panda? What do you think about it?

4. Have you ever use LINEMAN? What do you think about it?

5. Have you ever use GRAB Food What do you think about it?

6. Have you ever use UBER EAT? What do think about it?

7. Does the price of service affect your decision to select the application?

8. What factor influence your decision to use foodie application?

9. Is application platform important to you?

10. What function you like in the application?

11. Does the score or comment in the application affect your decision to select a

restaurant?

12. What are the reasons you select a restaurant?

13. Do you have any suggestion for the foodie applications in the market?

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Appendix B: Online questionnaire’s questions

FOODIE APPLICATION USERS IN BANGKOK, THAILAND

Dear Participant,

I would like to invite you to take part in a research study entitled “FOODIE

Application Users in Bangkok, Thailand”. I am a student presently enrolled in the

Master's Degree Program in Marketing at Thammasat University, Bangkok, Thailand.

The purpose of the research is to find factors that influence foodie application

user to select the restaurants. Your participation in the survey will help the researcher

better understand selection criteria, consumer behavior and perception toward Foodie

application. The study is for academic purpose only.

There are no known risks to participate. Your responses will remain

confidential and anonymous. Data from this research will be kept under lock and key

and reported only as a collective combined total. No one other than the researchers

will know your answers to this questionnaire.

Your participation in this survey is voluntary. You may decline to answer any

question and you have the right to withdraw from participation at any time without

penalty. There are no right or wrong answers to these questions, please feel free to

answer these questions as you deem fit.

If you agree to take part in this project, please answer the questions on the

questionnaire as best you can. We estimate that it will take about 15 minutes to

complete the questionnaire. Please return the questionnaire to the surveyor in person

or via e-mail, champg@gmail.com.

If you have any questions or clarifications about this survey, please feel free to

contact me at champg@gmail.com.

Your assistance is highly appreciated.

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Screening question

A. Are you older than 15 years old? Yes No (End of questionnaire)

B. Have you order food from online food delivery service in the last month?

Yes No (End of questionnaire) เลย)

Consumer behavior using foodie application and in general

Instruction: Please mark one or more answers for each question or fill in the blank as appropriate.

How many hours do you approximately spend on the Internet in a day? (Objective 3.1.B)

1-2 hours 1-2 3-4 hours

5-6 hours 5-6 7 hours or more

How many times do you use online food delivery application in the last month? (Objective 3.1.B)

1-3 times 4-6 times 7 times or more Other, please specify_______

Which devices do you use to access to these applications? (Can choose more than 1 answer) Mobile phone PC/Laptop

iPad/Tablets Other (Please specify) ___________

Consumer behavior on restaurant selection

Instruction: Please mark one or more answers for each question or fill in the blank as appropriate.

How many times do you go to the restaurants per month? (Objective 3.2.A)

1-3 times1-3 4-6 times 7 times or more Never

What meal of the day do you usually go to a restaurant? (Objective 3.2.A)

Breakfast Lunch Dinner Other (Please specify) _____

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Instruction: Please check on each of the following questions based on your opinions.

Please check on level of importance of each factor in choosing a restaurant. (Objective 3.2.A)

Factors Not at all Important

(1) Slightly

Important (2) Moderate

Important (3) Very

Important (4) Extremely

Important (5)

Convenience

Food taste

Cleanliness

Value for Money

Location

Speed of services

Consumer behavior using foodie application

Please check on level of importance of each factor that make you use foodie application (Objective 3.2.B)

Factors Not at all Important (1)

Slightly Important (2)

Moderate Important (3)

Very Important (4)

Extremely Important (5)

Ease of Use

Time-saving

Database completeness

Payment channels

Status tracking

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Service coverage

Instruction: Please mark one answers for each question. Foodie application can help you to find many new restaurants?

Strongly disagree Disagree Neutral Agree Strongly agree

Foodie application can help you to choose better restaurants?

Strongly disagree Disagree Neutral Agree Strongly agree

Do you think good user’s reviews and rating represent a good restaurant?

Strongly disagree Disagree

Neutral Agree Strongly agree

Please check on level of importance of each criteria in choosing a restaurant by foodie application

Criteria in choosing a

restaurant by application

Not at all Important (1)

Slightly Important (2)

Moderate Important (3)

Very Important (4)

Extremely Important (5)

Beautiful photo

Good reviews

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Location (Near me)

Variety of menus

Value for money

Perception on application features

Please check on level of importance of each feature

Application Feature

Not at all Important (1)

Slightly Important (2)

Moderate Important (3)

Very Important (4)

Extremely Important (5)

Menus & Price

Restaurant business hour

Restaurants database

Review & Rating

Original content from application

Restaurant Booking

Payment Option

Promotion information

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User’s perception toward each application

Which online food delivery application do you know? (Can choose more than 1)

Grabfood

UBEREAT

foodpanda

LINEMAN

Have you ever use LINEMAN online food delivery service?

Yes

No

Instruction: Please check on level of appropriateness on LINEMAN services.

Service evaluation

criteria

Inappropriate (1)

Slightly Inappropriate

(2) Neutral (3)

Slightly appropriate

(4)

Appropriate (5)

Delivery fee

Variety of restaurants

Service coverage

Payment option

Application interface

Have you ever use GrabFood online food delivery service?

Yes

No

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Instruction: Please check on level of appropriateness on GrabFood services.

Service evaluation

criteria

Inappropriate (1)

Slightly Inappropriate

(2) Neutral (3)

Slightly appropriate

(4)

Appropriate (5)

Delivery fee

Variety of restaurants

Service coverage

Payment option

Application interface

Have you ever use UberEats online food delivery service?

Yes

No

Instruction: Please check on level of appropriateness on UberEats services.

Service evaluation

criteria

Inappropriate (1)

Slightly Inappropriate

(2) Neutral (3)

Slightly appropriate

(4)

Appropriate (5)

Delivery fee

Variety of restaurants

Service coverage

Payment option

Application interface

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Have you ever use foodpanda online food delivery service?

Yes

No

Instruction: Please check on level of appropriateness on foodpanda services.

Service evaluation

criteria

Inappropriate (1)

Slightly Inappropriate

(2) Neutral (3)

Slightly appropriate

(4)

Appropriate (5)

Delivery fee

Variety of restaurants

Service coverage

Payment option

Application interface

Price sensitivity measurement (Objective 3.4.B)

What price would represent a good value for online food delivery fee (is appropriate)?

________

What price would be expensive, yet still acceptable for online food delivery fee?

________

What price would be too cheap, thus raising doubts about quality for online food delivery fee?

________

What price would be too expensive, thus ruling out any consideration of purchase for online food delivery fee?

________

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Do you think price promotion will make you use more online food delivery services?

Strongly disagree Disagree Neutral

Agree Strongly agree

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Respondent information

What is your gender?

Male

Female

How old are you?

________

What is your marital status?

Single

Married

Divorce

Other (Please specify) _____

What is your highest education?

High School

Bachelor’s Degree

Master’s Degree

Doctor’s Degree

Other (Please specify) _____

What is your occupation?

Students

Employees

Housewife

State Enterprise Officer

Self-employed/ Business owner

Other (Please specify) _____

How much is your monthly income?

≤ 10,000 baht

10,001 – 15,000 baht

15,001 – 30,000 baht

30,001 – 50,000 baht

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> 50,000 baht

Where do you live?

Dormitory

House

Apartment

Rented House

Condominium

Other (Please specify) ___ __

Who do you live with?

I live alone

I live with relatives

I live with friends

I live with parents

Other (Please specify) _____

What are your hobbies? (Can choose more than 1 answer)

Shopping Traveling

Reading Cooking

Exercise Seeking good restaurants

Movies/Music Other (Please specify) _____

Please check the answer that match with your opinion

I am an active person

Strongly disagree

Disagree

Neutral

Agree

Strongly agree

I am a perfectionist

Strongly disagree

Disagree

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Neutral

Agree

Strongly agree

I always get things done on time

Strongly disagree

Disagree

Neutral

Agree

Strongly agree

I am very busy

Strongly disagree

Disagree

Neutral

Agree

Strongly agree

I love to eat

Strongly disagree

Disagree

Neutral

Agree

Strongly agree

I love to share my opinion

Strongly disagree

Disagree

Neutral

Agree

Strongly agree

I prefer to listen more than speak

Strongly disagree

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Disagree

Neutral

Agree

Strongly agree

I love outdoor activities

Strongly disagree

Disagree

Neutral

Agree

Strongly agree

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APPENDIX C

RESPONDENT’S PROFILE AND SEGMENTATION

Appendix C-1: K-Means Cluster Analysis for customer’s segmentation

3 Psychological factors

4 Customer segments

Achiever

(n=58)

Perfectionist

(n=59)

Extrovert

(n=84)

Outdoor

enthusiast

(n=64)

Active Lifestyle .78246 .76468

Sociable .68507 .65492

Outdoor Lover .91019

.83392

Appendix C-2: Each customer segments by behavioral profile

Behavioral attributes

Customer Segments

Achiever

(n=58)

Perfectionist

(n=59)

Extrovert

(n=84)

Outdoor

Enthusiast

(n=64)

Count % Count % Count % Count %

How many

hours do you

approximately

spend on the

Internet in a

day?

0-2 Hour 18 16.7% 17 15.7% 40 37.0% 33 30.6%

3-4 Hour 20 22.7% 17 19.3% 29 33.0% 22 25.0%

5-6 Hour 12 27.9% 14 32.6% 8 18.6% 9 20.9%

More than 7 hour 8 30.8% 11 42.3% 7 26.9% 0 0.0%

How many time

do you use

online food

delivery

application in

the last month?

1-2 time 26 22.6% 36 31.3% 35 30.4% 18 15.7%

3-4 time 21 17.5% 16 13.3% 45 37.5% 38 31.7%

5-6 time 10 41.7% 4 16.7% 2 8.3% 8 33.3%

> 6 time 1 16.7% 3 50.0% 2 33.3% 0 0.0%

Devices use to

access

application

Mobile 49 21.5% 50 21.9% 74 32.5% 55 24.1%

Computer 39 25.8% 24 15.9% 53 35.1% 35 23.2%

iPad/Tablet 30 22.1% 19 14.0% 51 37.5% 36 26.5%

How many time

do you go to the

restaurants per

month?

1-3 time 28 22.6% 25 20.2% 38 30.6% 33 26.6%

4-6 time 25 27.5% 20 22.0% 27 29.7% 19 20.9%

More than 7 time 5 10.4% 12 25.0% 19 39.6% 12 25.0%

Never 0 0.0% 2 100.0% 0 0.0% 0 0.0%

What meal of

the day do you

usually go to a

restaurant?

Breakfast 9 23.7% 5 13.2% 16 42.1% 8 21.1%

Lunch 27 24.8% 21 19.3% 34 31.2% 27 24.8%

Dinner 22 18.6% 33 28.0% 34 28.8% 29 24.6%

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Appendix C-3: Mean comparison and standard deviation on psychological

attributes among customer’s segments

8 Psychological attributes

Achiever

(n=58)

Perfectionist

(n=59)

Extrovert

(n=84)

Outdoor Enthusiast

(n=64)

Mean SD Mean SD Mean SD Mean SD

I am an active person 3.93 .72 3.75 .80 2.90 .53 2.91 .56

I am a perfectionist 3.90 .74 4.00 .74 3.25 .73 2.77 .50

I always get things done on

time 3.86 .76 4.00 .74 3.25 .82 2.56 .53

I am very busy 3.81 .85 3.61 .74 3.71 .90 2.69 .75

I love to eat good food 4.24 .80 4.29 .81 4.60 .56 3.09 .83

I always share my opinion 4.59 .53 3.49 .84 4.54 .55 3.56 .73

I prefer to listen than speak 4.67 .51 2.97 .69 4.15 .61 4.08 .76

I love Outdoor activities 4.59 .62 3.10 .74 3.14 .79 4.41 .64

Appendix C-4: ANOVA test on psychological aspects

ANOVA

Psychographic by customer segments

Sum of

Squares df Mean Square F Sig.

I am an active person Between

Groups 57.787 3 19.262 45.877 .000

Within

Groups 109.586 261 .420

Total 167.374 264

I am a perfectionist Between

Groups 62.730 3 20.910 44.510 .000

Within

Groups 122.614 261 .470

Total 185.343 264

I always get things

done on time

Between

Groups 80.343 3 26.781 50.506 .000

Within

Groups 138.397 261 .530

Total 218.740 264

I am very busy Between

Groups 52.069 3 17.356 25.762 .000

Within

Groups 175.841 261 .674

Total 227.909 264

I love to eat good food Between

Groups 88.606 3 29.535 53.385 .000

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Within

Groups 144.398 261 .553

Total 233.004 264

I always share my

opinion

Between

Groups 69.878 3 23.293 52.655 .000

Within

Groups 115.458 261 .442

Total 185.336 264

I prefer to listen than

speak

Between

Groups 91.634 3 30.545 72.273 .000

Within

Groups 110.306 261 .423

Total 201.940 264

I love Outdoor

activities

Between

Groups 123.875 3 41.292 82.154 .000

Within

Groups 131.182 261 .503

Total 255.057 264

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APPENDIX D

RESTAURANT SELECTION BEHAVIOR

Appendix D-1: Mean comparison and standard deviation on restaurant selection

attributes among customer’s segments

Restaurant

selection

attributes

Customer Segments

Achiever

(n=58)

Perfectionist

(n=59)

Extrovert

(n=84)

Outdoor Enthusiast

(n=64)

Mean

Standard

Deviation Mean

Standard

Deviation Mean

Standard

Deviation Mean

Standard

Deviation

Convenience 3.66 .87 3.78 .77 3.39 .73 3.28 .79

Food taste 3.66 1.05 3.98 .82 3.49 .87 3.31 .91

Cleanliness 3.66 1.05 3.98 .96 3.61 .92 3.25 .85

Value for Money 3.95 .98 3.92 .95 3.86 .73 3.38 .85

Location 4.12 .92 3.80 1.00 4.12 .87 3.59 1.08

Speed of services 4.10 1.07 3.86 .96 4.27 .90 3.55 1.08

Appendix D-2: ANOVA test on restaurant selection criteria

ANOVA

Restaurant selection criteria

Sum of

Squares df

Mean

Square F Sig.

Convenience Between

Groups 10.014 3 3.338 5.438 .001

Within

Groups 160.212 261 .614

Total 170.226 264

Food taste Between

Groups 15.160 3 5.053 6.083 .001

Within

Groups 216.825 261 .831

Total 231.985 264

Cleanliness Between

Groups 16.617 3 5.539 6.228 .000

Within

Groups 232.122 261 .889

Total 248.740 264

Value for Money Between

Groups 13.708 3 4.569 6.063 .001

Within

Groups 196.707 261 .754

Total 210.415 264

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Location Between

Groups 13.374 3 4.458 4.809 .003

Within

Groups 241.962 261 .927

Total 255.336 264

Speed of services Between

Groups 20.902 3 6.967 7.025 .000

Within

Groups 258.856 261 .992

Total 279.758 264

Restaurant selection criteria

Mean

Difference (I-

J) Std. Error Sig.

Convenience Achiever Perfectionist -.12449 .14487 .826

Extrovert .26232 .13376 .206

Outdoor

enthusiast .37392* .14204 .044

Perfectionist Achiever .12449 .14487 .826

Extrovert .38680* .13309 .021

Outdoor

enthusiast .49841* .14140 .003

Extrovert Achiever -.26232 .13376 .206

Perfectionist -.38680* .13309 .021

Outdoor

enthusiast .11161 .13000 .826

Outdoor

enthusiast

Achiever -.37392* .14204 .044

Perfectionist -.49841* .14140 .003

Extrovert -.11161 .13000 .826

Food taste Achiever Perfectionist -.32788 .16853 .212

Extrovert .16708 .15561 .706

Outdoor

enthusiast .34267 .16524 .164

Perfectionist Achiever .32788 .16853 .212

Extrovert .49496* .15482 .008

Outdoor

enthusiast .67055* .16450 .000

Extrovert Achiever -.16708 .15561 .706

Perfectionist -.49496* .15482 .008

Outdoor

enthusiast .17560 .15123 .652

Outdoor

enthusiast

Achiever -.34267 .16524 .164

Perfectionist -.67055* .16450 .000

Extrovert -.17560 .15123 .652

Cleanliness Achiever Perfectionist -.32788 .17438 .239

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Extrovert .04803 .16100 .991

Outdoor

enthusiast .40517 .17097 .086

Perfectionist Achiever .32788 .17438 .239

Extrovert .37591 .16019 .090

Outdoor

enthusiast .73305* .17021 .000

Extrovert Achiever -.04803 .16100 .991

Perfectionist -.37591 .16019 .090

Outdoor

enthusiast .35714 .15647 .105

Outdoor

enthusiast

Achiever -.40517 .17097 .086

Perfectionist -.73305* .17021 .000

Extrovert -.35714 .15647 .105

Value for

Money

Achiever Perfectionist .03302 .16052 .997

Extrovert .09113 .14821 .927

Outdoor

enthusiast .57328* .15739 .002

Perfectionist Achiever -.03302 .16052 .997

Extrovert .05811 .14747 .979

Outdoor

enthusiast .54025* .15668 .004

Extrovert Achiever -.09113 .14821 .927

Perfectionist -.05811 .14747 .979

Outdoor

enthusiast .48214* .14404 .005

Outdoor

enthusiast

Achiever -.57328* .15739 .002

Perfectionist -.54025* .15668 .004

Extrovert -.48214* .14404 .005

Location Achiever Perfectionist .32408 .17804 .266

Extrovert .00164 .16438 1.000

Outdoor

enthusiast .52694* .17455 .015

Perfectionist Achiever -.32408 .17804 .266

Extrovert -.32244 .16355 .201

Outdoor

enthusiast .20286 .17378 .648

Extrovert Achiever -.00164 .16438 1.000

Perfectionist .32244 .16355 .201

Outdoor

enthusiast .52530* .15975 .006

Outdoor

enthusiast

Achiever -.52694* .17455 .015

Perfectionist -.20286 .17378 .648

Extrovert -.52530* .15975 .006

Speed of

services

Achiever Perfectionist .23904 .18415 .565

Extrovert -.17036 .17002 .748

Ref. code: 25605902040483CGF

58

Outdoor

enthusiast .55657* .18054 .012

Perfectionist Achiever -.23904 .18415 .565

Extrovert -.40940 .16917 .076

Outdoor

enthusiast .31753 .17974 .292

Extrovert Achiever .17036 .17002 .748

Perfectionist .40940 .16917 .076

Outdoor

enthusiast .72693* .16524 .000

Outdoor

enthusiast

Achiever -.55657* .18054 .012

Perfectionist -.31753 .17974 .292

Extrovert -.72693* .16524 .000

Appendix D-3: Mean comparison and standard deviation on usage decision

attributes of foodie application among customer’s segments

Usage decision attributes

of foodie application

Customer Segments

Achiever

(n=58)

Perfectionist

(n=59)

Extrovert

(n=84)

Outdoor Enthusiast

(n=64)

Mean

Standard

Deviation Mean

Standard

Deviation Mean

Standard

Deviation Mean

Standard

Deviation

Ease of use 3.78 .96 3.85 1.00 3.31 .86 3.19 .81

Time-saving 3.78 .88 3.95 .86 3.55 .80 2.98 .77

Restuarant data

completeness (No.of

restaurant in system)

3.84 .95 3.75 .98 3.52 .81 3.03 .71

Payment option 4.07 .92 3.90 .96 3.82 .88 3.41 .90

Status tracking

(Ordering food from

App)

4.22 .88 4.12 .81 4.15 .95 3.83 .95

Service

coverage (Ordering food

from App)

4.29 .88 4.24 .75 4.19 .96 4.19 .92

Appendix D-4: ANOVA test on key usage decision attributes for foodie application

ANOVA

Key usage decision attributes Sum of

Squares df Mean Square F Sig.

Ease of use Between

Groups 20.833 3 6.944 8.493 .000

Ref. code: 25605902040483CGF

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Within

Groups 213.416 261 .818

Total 234.249 264

Time-saving Between

Groups 32.835 3 10.945 16.164 .000

Within

Groups 176.728 261 .677

Total 209.562 264

Restuarant data

completeness (No.of

restaurant in system)

Between

Groups 24.411 3 8.137 10.965 .000

Within

Groups 193.680 261 .742

Total 218.091 264 Payment option Between

Groups 14.712 3 4.904 5.902 .001

Within

Groups 216.873 261 .831

Total 231.585 264

Status tracking

(Ordering food from

App)

Between

Groups 5.820 3 1.940 2.362 .072

Within

Groups 214.353 261 .821

Total 220.174 264

Service

coverage (Ordering

food from App)

Between

Groups .467 3 .156 .196 .899

Within

Groups 207.398 261 .795

Total 207.864 264

Multiple comparison of usage decision attribute among

customer segments Mean Difference

(I-J) Std. Error Sig.

Ease of use Achiever Perfectionist -.07160 .16720 .974

Extrovert .46634* .15438 .015

Outdoor

enthusiast .58836* .16393 .002

Perfectionist Achiever .07160 .16720 .974

Extrovert .53793* .15360 .003

Outdoor

enthusiast .65996* .16320 .000

Extrovert Achiever -.46634* .15438 .015

Perfectionist -.53793* .15360 .003

Outdoor

enthusiast .12202 .15004 .848

Outdoor Achiever -.58836* .16393 .002

Ref. code: 25605902040483CGF

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enthusiast Perfectionist -.65996* .16320 .000

Extrovert -.12202 .15004 .848

Time-saving Achiever Perfectionist -.17329 .15215 .666

Extrovert .22824 .14048 .367

Outdoor

enthusiast .79149* .14918 .000

Perfectionist Achiever .17329 .15215 .666

Extrovert .40153* .13978 .023

Outdoor

enthusiast .96478* .14851 .000

Extrovert Achiever -.22824 .14048 .367

Perfectionist -.40153* .13978 .023

Outdoor

enthusiast .56324* .13653 .000

Outdoor

enthusiast

Achiever -.79149* .14918 .000

Perfectionist -.96478* .14851 .000

Extrovert -.56324* .13653 .000

Restuarant data

completeness (No.of

restaurant in system)

Achiever Perfectionist .09906 .15928 .925

Extrovert .32102 .14707 .131

Outdoor

enthusiast .81358* .15617 .000

Perfectionist Achiever -.09906 .15928 .925

Extrovert .22195 .14633 .429

Outdoor

enthusiast .71451* .15547 .000

Extrovert Achiever -.32102 .14707 .131

Perfectionist -.22195 .14633 .429

Outdoor

enthusiast .49256* .14293 .004

Outdoor

enthusiast

Achiever -.81358* .15617 .000

Perfectionist -.71451* .15547 .000

Extrovert -.49256* .14293 .004

Payment option Achiever Perfectionist .17066 .16855 .742

Extrovert .24754 .15562 .386

Outdoor

enthusiast .66272* .16526 .000

Perfectionist Achiever -.17066 .16855 .742

Extrovert .07688 .15484 .960

Outdoor

enthusiast .49206* .16452 .016

Extrovert Achiever -.24754 .15562 .386

Perfectionist -.07688 .15484 .960

Outdoor

enthusiast .41518* .15125 .033

Outdoor

enthusiast

Achiever -.66272* .16526 .000

Perfectionist -.49206* .16452 .016

Ref. code: 25605902040483CGF

61

Extrovert -.41518* .15125 .033

Status tracking

(Ordering food from

App)

Achiever Perfectionist .10549 .16757 .922

Extrovert .06938 .15472 .970

Outdoor

enthusiast .39601 .16429 .078

Perfectionist Achiever -.10549 .16757 .922

Extrovert -.03612 .15394 .995

Outdoor

enthusiast .29052 .16356 .287

Extrovert Achiever -.06938 .15472 .970

Perfectionist .03612 .15394 .995

Outdoor

enthusiast .32664 .15036 .134

Outdoor

enthusiast

Achiever -.39601 .16429 .078

Perfectionist -.29052 .16356 .287

Extrovert -.32664 .15036 .134

Service

coverage (Ordering food

from App)

Achiever Perfectionist .05582 .16483 .987

Extrovert .10263 .15219 .907

Outdoor

enthusiast .10560 .16161 .914

Perfectionist Achiever -.05582 .16483 .987

Extrovert .04681 .15142 .990

Outdoor

enthusiast .04979 .16089 .990

Extrovert Achiever -.10263 .15219 .907

Perfectionist -.04681 .15142 .990

Outdoor

enthusiast .00298 .14790 1.000

Outdoor

enthusiast

Achiever -.10560 .16161 .914

Perfectionist -.04979 .16089 .990

Extrovert -.00298 .14790 1.000

Ref. code: 25605902040483CGF

62

Appendix D-5: Mean comparison and standard deviation of restaurant selection

criteria via foodie application

Restaurant selection

criteria via foodie

application

Customer Segments

Achiever

(n=58)

Perfectionist

(n=59)

Extrovert

(n=84)

Outdoor Enthusiast

(n=64)

Mean Standard

Deviation Mean

Standard

Deviation Mean

Standard

Deviation Mean

Standard

Deviation

Beautiful photo 3.55 .86 3.32 .75 3.05 .79 2.89 .72

Good reviews 3.76 1.05 3.76 .82 3.00 .88 2.91 .94

Location (Near me) 3.67 1.02 3.54 .88 3.18 1.01 3.00 1.05

Variety of menus 3.95 1.19 3.86 .90 3.89 .93 3.58 .96

Appropriate price 4.26 .91 4.08 .95 4.23 .86 4.11 .86

Appendix D-6: ANOVA test on restaurant selection criteria via foodie application

ANOVA

Restaurant selection criteria via

foodie application

Sum of

Squares df Mean Square F Sig.

Beautiful photo

Between

Groups 16.036 3 5.345 8.759 .000

Within

Groups 159.270 261 .610

Total 175.306 264

Good reviews

Between

Groups 42.268 3 14.089 16.659 .000

Within

Groups 220.736 261 .846

Total 263.004 264

Location (Near me)

Between

Groups 18.349 3 6.116 6.194 .000

Within

Groups 257.741 261 .988

Total 276.091 264

Variety of menus

Between

Groups 5.259 3 1.753 1.777 .152

Within

Groups 257.405 261 .986

Total 262.664 264

Appropriate price Between 1.381 3 .460 .582 .628

Ref. code: 25605902040483CGF

63

Groups

Within

Groups 206.634 261 .792

Total 208.015 264

Multiple Comparisons

Dependent Variable

Mean Difference

(I-J) Std. Error Sig.

Beautiful photo Achiever Perfectionist .22969 .14444 .386

Extrovert .50411* .13336 .001

Outdoor

enthusiast .66110* .14162 .000

Perfectionist Achiever -.22969 .14444 .386

Extrovert .27441 .13269 .166

Outdoor

enthusiast .43141* .14099 .013

Extrovert Achiever -.50411* .13336 .001

Perfectionist -.27441 .13269 .166

Outdoor

enthusiast .15699 .12961 .620

Outdoor

enthusiast

Achiever -.66110* .14162 .000

Perfectionist -.43141* .14099 .013

Extrovert -.15699 .12961 .620

Good reviews Achiever Perfectionist -.00409 .17005 1.000

Extrovert .75862* .15700 .000

Outdoor

enthusiast .85237* .16672 .000

Perfectionist Achiever .00409 .17005 1.000

Extrovert .76271* .15621 .000

Outdoor

enthusiast .85646* .16598 .000

Extrovert Achiever -.75862* .15700 .000

Perfectionist -.76271* .15621 .000

Outdoor

enthusiast .09375 .15259 .927

Outdoor

enthusiast

Achiever -.85237* .16672 .000

Perfectionist -.85646* .16598 .000

Extrovert -.09375 .15259 .927

Location (Near me) Achiever Perfectionist .13004 .18375 .894

Extrovert .49384* .16965 .020

Outdoor

enthusiast .67241* .18016 .001

Perfectionist Achiever -.13004 .18375 .894

Ref. code: 25605902040483CGF

64

Extrovert .36380 .16880 .139

Outdoor

enthusiast .54237* .17935 .014

Extrovert Achiever -.49384* .16965 .020

Perfectionist -.36380 .16880 .139

Outdoor

enthusiast .17857 .16488 .700

Outdoor

enthusiast

Achiever -.67241* .18016 .001

Perfectionist -.54237* .17935 .014

Extrovert -.17857 .16488 .700

Variety of menus Achiever Perfectionist .08387 .18363 .968

Extrovert .05542 .16954 .988

Outdoor

enthusiast .37015 .18004 .171

Perfectionist Achiever -.08387 .18363 .968

Extrovert -.02845 .16869 .998

Outdoor

enthusiast .28628 .17924 .382

Extrovert Achiever -.05542 .16954 .988

Perfectionist .02845 .16869 .998

Outdoor

enthusiast .31473 .16477 .226

Outdoor

enthusiast

Achiever -.37015 .18004 .171

Perfectionist -.28628 .17924 .382

Extrovert -.31473 .16477 .226

Appropriate price Achiever Perfectionist .17387 .16453 .716

Extrovert .03243 .15190 .997

Outdoor

enthusiast .14925 .16131 .791

Perfectionist Achiever -.17387 .16453 .716

Extrovert -.14144 .15114 .786

Outdoor

enthusiast -.02463 .16059 .999

Extrovert Achiever -.03243 .15190 .997

Perfectionist .14144 .15114 .786

Outdoor

enthusiast .11682 .14763 .858

Outdoor

enthusiast

Achiever -.14925 .16131 .791

Perfectionist .02463 .16059 .999

Extrovert -.11682 .14763 .858

Ref. code: 25605902040483CGF

65

APPENDIX E

USER’S PERCEPTION ON FOODIE APPLICATION

Appendix E-1: Mean and standard deviation of key application features by

customer segments

Key application

features

Customer Segments

Achiever

(n=58)

Perfectionist

(n=59)

Extrovert

(n=84)

Outdoor Enthusiast

(n=64)

Mean

Standard

Deviation Mean

Standard

Deviation Mean

Standard

Deviation Mean

Standard

Deviation

Menus & Price 3.60 .86 3.93 .76 3.33 .73 3.05 .65

Restaurant business

hour 3.59 .86 3.88 .74 3.43 .76 3.02 .72

Restaurants database 3.66 .91 3.58 .83 3.50 .78 3.17 .86

Review & Rating 3.84 .83 3.73 .85 3.64 .83 3.58 .75

Original content from

application 3.91 1.00 3.42 .77 3.93 .89 3.66 .93

Restaurant Booking

system 4.21 .74 3.51 1.01 4.01 .96 4.08 .90

Payment Option 4.21 .79 3.59 .89 3.96 1.02 4.03 .85

Promotion information 4.02 1.03 3.93 .85 3.75 1.05 3.77 1.05

Appendix E-2: ANOVA test on key application features

ANOVA

Sum of

Squares df Mean Square F Sig.

Menus & Price

Between

Groups 26.617 3 8.872 15.738 .000

Within

Groups 147.134 261 .564

Total 173.751 264

Restaurant business

hour

Between

Groups 24.115 3 8.038 13.467 .000

Within

Groups 155.794 261 .597

Total 179.909 264

Restaurants database

Between

Groups 8.418 3 2.806 3.946 .009

Within

Groups 185.620 261 .711

Total 194.038 264

Ref. code: 25605902040483CGF

66

Review & Rating

Between

Groups 2.467 3 .822 1.232 .298

Within

Groups 174.160 261 .667

Total 176.626 264

Original content from

application

Between

Groups 11.076 3 3.692 4.567 .004

Within

Groups 210.985 261 .808

Total 222.060 264

Restaurant Booking

system

Between

Groups 16.683 3 5.561 6.662 .000

Within

Groups 217.860 261 .835

Total 234.543 264

Payment Option

Between

Groups 11.777 3 3.926 4.775 .003

Within

Groups 214.585 261 .822

Total 226.362 264

Promotion

information

Between

Groups 3.314 3 1.105 1.092 .353

Within

Groups 263.946 261 1.011

Total 267.260 264

Multiple Comparisons

Dependent Variable

Mean Difference

(I-J) Std. Error Sig.

Menus & Price Achiever Perfectionist -.32876 .13883 .086

Extrovert .27011 .12818 .153

Outdoor

enthusiast .55657* .13612 .000

Perfectionist Achiever .32876 .13883 .086

Extrovert .59887* .12754 .000

Outdoor

enthusiast .88533* .13551 .000

Extrovert Achiever -.27011 .12818 .153

Perfectionist -.59887* .12754 .000

Outdoor

enthusiast .28646 .12458 .101

Outdoor

enthusiast

Achiever -.55657* .13612 .000

Perfectionist -.88533* .13551 .000

Extrovert -.28646 .12458 .101

Ref. code: 25605902040483CGF

67

Restaurant business

hour

Achiever Perfectionist -.29515 .14286 .167

Extrovert .15764 .13190 .630

Outdoor

enthusiast .57058* .14007 .000

Perfectionist Achiever .29515 .14286 .167

Extrovert .45278* .13124 .004

Outdoor

enthusiast .86573* .13944 .000

Extrovert Achiever -.15764 .13190 .630

Perfectionist -.45278* .13124 .004

Outdoor

enthusiast .41295* .12819 .008

Outdoor

enthusiast

Achiever -.57058* .14007 .000

Perfectionist -.86573* .13944 .000

Extrovert -.41295* .12819 .008

Restaurants database Achiever Perfectionist .07890 .15594 .958

Extrovert .15517 .14397 .703

Outdoor

enthusiast .48330* .15289 .009

Perfectionist Achiever -.07890 .15594 .958

Extrovert .07627 .14325 .951

Outdoor

enthusiast .40440* .15220 .041

Extrovert Achiever -.15517 .14397 .703

Perfectionist -.07627 .14325 .951

Outdoor

enthusiast .32813 .13992 .091

Outdoor

enthusiast

Achiever -.48330* .15289 .009

Perfectionist -.40440* .15220 .041

Extrovert -.32813 .13992 .091

Review & Rating Achiever Perfectionist .11601 .15104 .869

Extrovert .20197 .13946 .470

Outdoor

enthusiast .26670 .14809 .275

Perfectionist Achiever -.11601 .15104 .869

Extrovert .08596 .13876 .926

Outdoor

enthusiast .15069 .14743 .737

Extrovert Achiever -.20197 .13946 .470

Perfectionist -.08596 .13876 .926

Outdoor

enthusiast .06473 .13554 .964

Outdoor

enthusiast

Achiever -.26670 .14809 .275

Perfectionist -.15069 .14743 .737

Extrovert -.06473 .13554 .964

Original content Achiever Perfectionist .49006* .16625 .018

Ref. code: 25605902040483CGF

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from application Extrovert -.01478 .15350 1.000

Outdoor

enthusiast .25754 .16300 .392

Perfectionist Achiever -.49006* .16625 .018

Extrovert -.50484* .15272 .006

Outdoor

enthusiast -.23252 .16227 .480

Extrovert Achiever .01478 .15350 1.000

Perfectionist .50484* .15272 .006

Outdoor

enthusiast .27232 .14918 .264

Outdoor

enthusiast

Achiever -.25754 .16300 .392

Perfectionist .23252 .16227 .480

Extrovert -.27232 .14918 .264

Restaurant Booking

system

Achiever Perfectionist .69842* .16894 .000

Extrovert .19499 .15598 .596

Outdoor

enthusiast .12877 .16563 .865

Perfectionist Achiever -.69842* .16894 .000

Extrovert -.50343* .15519 .007

Outdoor

enthusiast -.56965* .16489 .004

Extrovert Achiever -.19499 .15598 .596

Perfectionist .50343* .15519 .007

Outdoor

enthusiast -.06622 .15159 .972

Outdoor

enthusiast

Achiever -.12877 .16563 .865

Perfectionist .56965* .16489 .004

Extrovert .06622 .15159 .972

Payment Option Achiever Perfectionist .61368* .16766 .002

Extrovert .24261 .15480 .399

Outdoor

enthusiast .17565 .16438 .709

Perfectionist Achiever -.61368* .16766 .002

Extrovert -.37107 .15402 .078

Outdoor

enthusiast -.43803* .16365 .039

Extrovert Achiever -.24261 .15480 .399

Perfectionist .37107 .15402 .078

Outdoor

enthusiast -.06696 .15045 .971

Outdoor

enthusiast

Achiever -.17565 .16438 .709

Perfectionist .43803* .16365 .039

Extrovert .06696 .15045 .971

Promotion

information

Achiever Perfectionist .08504 .18595 .968

Extrovert .26724 .17168 .405

Ref. code: 25605902040483CGF

69

Outdoor

enthusiast .25162 .18231 .513

Perfectionist Achiever -.08504 .18595 .968

Extrovert .18220 .17082 .710

Outdoor

enthusiast .16658 .18150 .795

Extrovert Achiever -.26724 .17168 .405

Perfectionist -.18220 .17082 .710

Outdoor

enthusiast -.01563 .16685 1.000

Outdoor

enthusiast

Achiever -.25162 .18231 .513

Perfectionist -.16658 .18150 .795

Extrovert .01563 .16685 1.000

Ref. code: 25605902040483CGF

70

APPENDIX F

PRICE PERCEPTION TOWARD ONLINE ORDER FEE

Appendix F-1: Mean and standard deviation of price promotion impact on

purchase intent by customer segments

Impact of promotion on

purchase intent

Customer segments

Achiever

(n=58)

Perfectionist

(n=59)

Extrovert

(n=84)

Outdoor Enthusiast

(n=64)

Mean Sd Mean Sd Mean Sd Mean Sd

Price promotion will make

you use more online food

delivery service

4.21 .77 4.31 .84 4.29 .61 4.06 .61

Appendix F-2: ANOVA test on price promotion impact on purchase intent

ANOVA

Price promotion will make you use more online food delivery

service

Sum of

Squares df

Mean

Square F Sig.

Between Groups 2.387 3 .796 1.611 .187

Within Groups 128.919 261 .494

Total 131.306 264

Ref. code: 25605902040483CGF

71

BIOGRAPHY

Name Mr.Anusorn Phopipat

Date of Birth October 12,1986

Education Attainment 2008 : Bachelor’s degree in

Business administration, Assumption University