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
Ref. code: 25605902040483CGF
(1)
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. (กองข้อมูลธุรกิจ กรมพัฒนาธุรกิจการค้า กระทรวง
พาณิชย์, 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 Male 100 37.7% your Female 165 62.3% gender? 16-20 year 31 11.7% 21-28 year 73 27.5% AgeGroup 29-35 year 89 33.6% 36-60 year 72 27.2% What is Single 154 58.1%
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your Married 93 35.1% marital Divorced 14 5.3% status? Widowed 2 .8% Other 2 .8% Elementary school 1 .4% or lower What is High School 29 10.9% your Bachelor’s Degree 165 62.3% highest Master’s Degree or education? 69 26.0% higher Other 1 0.40% Student 37 14.0% Unemployed 8 3.0% Employees 106 40.0% What is Government your 44 16.6% employee occupation? Housewife/husband 5 1.90% Business Owner 43 16.2% Freelance 22 8.3% ≤ 10,000 baht 26 9.8% How much 10,001-15,000 baht 76 28.7% is your 15,001-30,000 baht 63 23.8% monthly 30,001-50,000 baht 59 22.3% income? More than 50,000 41 15.5% baht Home 141 53.2% Where do Condominium 64 24.2% you live? Apartment 60 22.6% Who do Alone 86 32.5% you live Relatives 24 9.10% with? Parents 59 22.3%
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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
3 Psychological factors 8 Psychological (1) Active (3) Outdoor attributes (2) Sociable lifestyle lover (1) I am an active person .817 (2) I am a perfectionist .823 (3) I always get things .789 done on time (4) I am very busy (5) I love to eat good food .524 (6) I always share my .842 opinion
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(7) I prefer to listen than .705 speak (8) I love Outdoor .874 activities
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 Count % in each segment (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
Outdoor Achiever Perfectionist Extrovert 4 Clusters' demographic Enthusiast (n=58) (n=59) (n=84) profile (n=64) n % n % n % n %
What is Male 19 32.8% 18 30.5% 38 45.2% 25 39.1% your Female 39 67.2% 41 69.5% 46 54.8% 39 60.9% gender? 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 Single 33 56.9% 31 52.5% 53 63.1% 37 57.8% your Married 20 34.5% 25 42.4% 26 31.0% 22 34.4% marital Divorced 3 5.2% 2 3.4% 4 4.8% 5 7.8% status? 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 Elementary school 0 0.0% 0 0.0% 0 0.0% 1 1.6% your or lower highest High School 2 3.4% 4 6.8% 12 14.3% 11 17.2% education? 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 Others 0 0.0% 0 0.0% 0 0.0% 0 0.0% your Student 6 10.3% 4 6.8% 17 20.2% 10 15.6% occupation? 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 10 17.2% 6 10.2% 15 17.9% 13 20.3% employee 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 ≤ 10,000 baht 4 6.9% 5 8.5% 9 10.7% 8 12.5% is your 10,001-15,000 baht 15 25.9% 10 16.9% 31 36.9% 20 31.3% monthly 15,001-30,000 baht 20 34.5% 10 16.9% 15 17.9% 18 28.1% income? 30,001-50,000 baht 12 20.7% 18 30.5% 16 19.0% 13 20.3% More than 50,000 7 12.1% 16 27.1% 13 15.5% 5 7.8% baht 58 100.0% 59 100.0% 84 100.0% 64 100.0%
Where do Other 0 0.0% 0 0.0% 0 0.0% 0 0.0% you live? 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 Other 0 0.0% 0 0.0% 0 0.0% 0 0.0% you live Alone 20 34.5% 12 20.3% 26 31.0% 28 43.8% with? 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
Outdoor Achiever Perfectionist Extrovert Restaurant selection behavior enthusiast Total (n=58) (n=59) (n=84) (n=64) How many time do 1-3 time 28 25 38 33 124 you go to the 4-6 time 25 20 27 19 91 restaurants per More than 7 5 12 19 12 48 month? time Never 0 2 0 0 2 Total 58 59 84 64 265 What meal of the day Breakfast 9 5 16 8 38 do you usually go to a Lunch 27 21 34 27 109 restaurant? Dinner 22 33 34 29 118 Total 58 59 84 64 265
Asymptotic Chi-Square Tests Value df Significance (2-sided) 13.436a 9 .144 Pearson Chi-Square 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 Standard attributes for foodie application Mean Deviation (n = 265) 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 Standard by means of foodie application Mean Deviation (n = 265) 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 Mean Standard Deviation (n = 265) 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 Grab UBER food Line online delivery application % % % % Food EATS panda man (n=265) Which online delivery application Selected 166 63% 191 72% 192 72% 207 78% do you know?
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
Std. Respondents' perception toward delivery service N Mean Deviation
Restaurant 155 3.68 0.83 availability Service area 155 3.81 0.78 LINEMAN coverage Payment option 155 3.97 0.81 Application 155 4.23 0.77 interface
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
Std. Respondents' perception toward delivery service N Mean Deviation Restaurant 83 3.65 0.88 availability Service area 83 3.76 0.84 GrabFood coverage Payment option 83 3.73 0.93 Application 83 4.22 0.87 interface
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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
Std. Respondents' perception toward delivery service N Mean Deviation Restaurant 72 3.56 0.87 availability Service area 72 3.67 0.87 UBER EATS coverage Payment option 72 3.82 0.81 Application 72 3.99 0.94 interface
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
Std. Respondents' perception toward delivery service N Mean Deviation Restaurant 78 3.62 0.74 availability Service area 78 3.72 0.72 foodpanda coverage Payment option 78 3.83 0.80 Application 78 4.14 0.80 interface
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
Std. Respondents' perception toward service fee N Mean Deviation
LINEMAN 155 3.41 0.81
GrabFood 83 3.52 0.79 Service fee 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 Mean Standard Deviation promotion Price promotion will make you use more 4.22 .71 online food delivery service
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/fe6c335d6157498afd1b7691b6eddd7b95 1c.pdf
กองข้อมูลธุรกิจ กรมพัฒนาธุรกิจการค้า กระทรวงพาณิชย์. (2017, May). Restaurant Business. Retrieved October 10, 2017, from Restaurant Business: http://www.dbd.go.th/download/document_file/Statisic/2560/T26/T26_20170 3.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, [email protected].
If you have any questions or clarifications about this survey, please feel free to contact me at [email protected].
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)
Not at all Slightly Moderate Very Extremely Important Factors Important (2) Important (3) Important (4) Important (5) (1)
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)
Not at all Slightly Moderate Very Extremely Factors Important (1) Important (2) Important (3) Important (4) 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 Not at all Slightly Moderate Very Extremely restaurant by Important (1) Important (2) Important (3) Important (4) Important (5) application
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 Not at all Slightly Moderate Very Extremely Feature Important (1) Important (2) Important (3) Important (4) 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 Slightly Slightly Inappropriate Appropriate evaluation Inappropriate Neutral (3) appropriate (1) (5) criteria (2) (4)
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 Slightly Slightly Inappropriate Appropriate Inappropriate Neutral (3) appropriate evaluation (1) (5) criteria (2) (4)
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 Slightly Slightly Inappropriate Appropriate evaluation Inappropriate Neutral (3) appropriate (1) (5) criteria (2) (4) 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 Slightly Slightly Inappropriate Appropriate evaluation Inappropriate Neutral (3) appropriate (1) (5) criteria (2) (4)
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
4 Customer segments
3 Psychological factors Achiever Perfectionist Extrovert Outdoor (n=58) (n=59) (n=84) enthusiast (n=64)
Active Lifestyle .78246 .76468
Sociable .68507 .65492
Outdoor Lover .91019 .83392
Appendix C-2: Each customer segments by behavioral profile
Customer Segments Outdoor Achiever Perfectionist Extrovert Behavioral attributes Enthusiast (n=58) (n=59) (n=84) (n=64) Count % Count % Count % Count % How many 0-2 Hour 18 16.7% 17 15.7% 40 37.0% 33 30.6% hours do you 3-4 Hour 20 22.7% 17 19.3% 29 33.0% 22 25.0% approximately 5-6 Hour 12 27.9% 14 32.6% 8 18.6% 9 20.9% spend on the Internet in a More than 7 hour 8 30.8% 11 42.3% 7 26.9% 0 0.0% day? How many time 1-2 time 26 22.6% 36 31.3% 35 30.4% 18 15.7% do you use 3-4 time 21 17.5% 16 13.3% 45 37.5% 38 31.7% online food 5-6 time 10 41.7% 4 16.7% 2 8.3% 8 33.3% delivery application in > 6 time 1 16.7% 3 50.0% 2 33.3% 0 0.0% the last month? Devices use to Mobile 49 21.5% 50 21.9% 74 32.5% 55 24.1% access Computer 39 25.8% 24 15.9% 53 35.1% 35 23.2% application iPad/Tablet 30 22.1% 19 14.0% 51 37.5% 36 26.5% How many time 1-3 time 28 22.6% 25 20.2% 38 30.6% 33 26.6% do you go to the 4-6 time 25 27.5% 20 22.0% 27 29.7% 19 20.9% restaurants per More than 7 time 5 10.4% 12 25.0% 19 39.6% 12 25.0% month? Never 0 0.0% 2 100.0% 0 0.0% 0 0.0% What meal of Breakfast 9 23.7% 5 13.2% 16 42.1% 8 21.1% the day do you Lunch 27 24.8% 21 19.3% 34 31.2% 27 24.8% usually go to a Dinner 22 18.6% 33 28.0% 34 28.8% 29 24.6% restaurant?
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Appendix C-3: Mean comparison and standard deviation on psychological attributes among customer’s segments
Achiever Perfectionist Extrovert Outdoor Enthusiast (n=58) (n=59) (n=84) (n=64) 8 Psychological attributes
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 3.86 .76 4.00 .74 3.25 .82 2.56 .53 time 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 Sum of Psychographic by customer segments Squares df Mean Square F Sig. I am an active person Between 57.787 3 19.262 45.877 .000 Groups Within 109.586 261 .420 Groups Total 167.374 264 I am a perfectionist Between 62.730 3 20.910 44.510 .000 Groups Within 122.614 261 .470 Groups Total 185.343 264 I always get things Between 80.343 3 26.781 50.506 .000 done on time Groups Within 138.397 261 .530 Groups Total 218.740 264 I am very busy Between 52.069 3 17.356 25.762 .000 Groups Within 175.841 261 .674 Groups Total 227.909 264 I love to eat good food Between 88.606 3 29.535 53.385 .000 Groups
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Within 144.398 261 .553 Groups Total 233.004 264 I always share my Between 69.878 3 23.293 52.655 .000 opinion Groups Within 115.458 261 .442 Groups Total 185.336 264 I prefer to listen than Between 91.634 3 30.545 72.273 .000 speak Groups Within 110.306 261 .423 Groups Total 201.940 264 I love Outdoor Between 123.875 3 41.292 82.154 .000 activities Groups Within 131.182 261 .503 Groups 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
Customer Segments
Restaurant Achiever Perfectionist Extrovert Outdoor Enthusiast selection (n=58) (n=59) (n=84) (n=64) attributes Standard Standard Standard Standard Mean Deviation Mean Deviation Mean Deviation Mean 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 Sum of Mean Restaurant selection criteria Squares df Square F Sig. Convenience Between 10.014 3 3.338 5.438 .001 Groups Within 160.212 261 .614 Groups Total 170.226 264 Food taste Between 15.160 3 5.053 6.083 .001 Groups Within 216.825 261 .831 Groups Total 231.985 264 Cleanliness Between 16.617 3 5.539 6.228 .000 Groups Within 232.122 261 .889 Groups Total 248.740 264 Value for Money Between 13.708 3 4.569 6.063 .001 Groups Within 196.707 261 .754 Groups Total 210.415 264
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Location Between 13.374 3 4.458 4.809 .003 Groups Within 241.962 261 .927 Groups Total 255.336 264 Speed of services Between 20.902 3 6.967 7.025 .000 Groups Within 258.856 261 .992 Groups Total 279.758 264
Mean Restaurant selection criteria Difference (I- J) Std. Error Sig. Convenience Achiever Perfectionist -.12449 .14487 .826 Extrovert .26232 .13376 .206 Outdoor .37392* .14204 .044 enthusiast Perfectionist Achiever .12449 .14487 .826 Extrovert .38680* .13309 .021 Outdoor .49841* .14140 .003 enthusiast Extrovert Achiever -.26232 .13376 .206 Perfectionist -.38680* .13309 .021 Outdoor .11161 .13000 .826 enthusiast Outdoor Achiever -.37392* .14204 .044 enthusiast Perfectionist -.49841* .14140 .003 Extrovert -.11161 .13000 .826 Food taste Achiever Perfectionist -.32788 .16853 .212 Extrovert .16708 .15561 .706 Outdoor .34267 .16524 .164 enthusiast Perfectionist Achiever .32788 .16853 .212 Extrovert .49496* .15482 .008 Outdoor .67055* .16450 .000 enthusiast Extrovert Achiever -.16708 .15561 .706 Perfectionist -.49496* .15482 .008 Outdoor .17560 .15123 .652 enthusiast Outdoor Achiever -.34267 .16524 .164 enthusiast Perfectionist -.67055* .16450 .000 Extrovert -.17560 .15123 .652 Cleanliness Achiever Perfectionist -.32788 .17438 .239
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Extrovert .04803 .16100 .991 Outdoor .40517 .17097 .086 enthusiast Perfectionist Achiever .32788 .17438 .239 Extrovert .37591 .16019 .090 Outdoor .73305* .17021 .000 enthusiast Extrovert Achiever -.04803 .16100 .991 Perfectionist -.37591 .16019 .090 Outdoor .35714 .15647 .105 enthusiast Outdoor Achiever -.40517 .17097 .086 enthusiast Perfectionist -.73305* .17021 .000 Extrovert -.35714 .15647 .105 Value for Achiever Perfectionist .03302 .16052 .997 Money Extrovert .09113 .14821 .927 Outdoor .57328* .15739 .002 enthusiast Perfectionist Achiever -.03302 .16052 .997 Extrovert .05811 .14747 .979 Outdoor .54025* .15668 .004 enthusiast Extrovert Achiever -.09113 .14821 .927 Perfectionist -.05811 .14747 .979 Outdoor .48214* .14404 .005 enthusiast Outdoor Achiever -.57328* .15739 .002 enthusiast Perfectionist -.54025* .15668 .004 Extrovert -.48214* .14404 .005 Location Achiever Perfectionist .32408 .17804 .266 Extrovert .00164 .16438 1.000 Outdoor .52694* .17455 .015 enthusiast Perfectionist Achiever -.32408 .17804 .266 Extrovert -.32244 .16355 .201 Outdoor .20286 .17378 .648 enthusiast Extrovert Achiever -.00164 .16438 1.000 Perfectionist .32244 .16355 .201 Outdoor .52530* .15975 .006 enthusiast Outdoor Achiever -.52694* .17455 .015 enthusiast Perfectionist -.20286 .17378 .648 Extrovert -.52530* .15975 .006 Speed of Achiever Perfectionist .23904 .18415 .565 services Extrovert -.17036 .17002 .748
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Outdoor .55657* .18054 .012 enthusiast Perfectionist Achiever -.23904 .18415 .565 Extrovert -.40940 .16917 .076 Outdoor .31753 .17974 .292 enthusiast Extrovert Achiever .17036 .17002 .748 Perfectionist .40940 .16917 .076 Outdoor .72693* .16524 .000 enthusiast Outdoor Achiever -.55657* .18054 .012 enthusiast 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
Customer Segments Achiever Perfectionist Extrovert Outdoor Enthusiast Usage decision attributes (n=58) (n=59) (n=84) (n=64) of foodie application Standard Standard Standard Standard Mean Deviation Mean Deviation Mean Deviation Mean 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 3.84 .95 3.75 .98 3.52 .81 3.03 .71 restaurant in system) Payment option 4.07 .92 3.90 .96 3.82 .88 3.41 .90 Status tracking (Ordering food from 4.22 .88 4.12 .81 4.15 .95 3.83 .95 App) Service coverage (Ordering food 4.29 .88 4.24 .75 4.19 .96 4.19 .92 from App)
Appendix D-4: ANOVA test on key usage decision attributes for foodie application
ANOVA Sum of Key usage decision attributes Squares df Mean Square F Sig. Ease of use Between 20.833 3 6.944 8.493 .000 Groups
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Within 213.416 261 .818 Groups Total 234.249 264
Time-saving Between 32.835 3 10.945 16.164 .000 Groups Within 176.728 261 .677 Groups Total 209.562 264
Restuarant data Between 24.411 3 8.137 10.965 .000 completeness (No.of Groups restaurant in system) Within 193.680 261 .742 Groups Total 218.091 264
Payment option Between 14.712 3 4.904 5.902 .001 Groups Within 216.873 261 .831 Groups Total 231.585 264
Status tracking Between 5.820 3 1.940 2.362 .072 (Ordering food from Groups App) Within 214.353 261 .821 Groups Total 220.174 264
Service Between .467 3 .156 .196 .899 coverage (Ordering Groups food from App) Within 207.398 261 .795 Groups Total 207.864 264
Multiple comparison of usage decision attribute among Mean Difference customer segments (I-J) Std. Error Sig. Ease of use Achiever Perfectionist -.07160 .16720 .974 Extrovert .46634* .15438 .015 Outdoor .58836* .16393 .002 enthusiast Perfectionist Achiever .07160 .16720 .974 Extrovert .53793* .15360 .003 Outdoor .65996* .16320 .000 enthusiast Extrovert Achiever -.46634* .15438 .015 Perfectionist -.53793* .15360 .003 Outdoor .12202 .15004 .848 enthusiast Outdoor Achiever -.58836* .16393 .002
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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 .79149* .14918 .000 enthusiast Perfectionist Achiever .17329 .15215 .666 Extrovert .40153* .13978 .023 Outdoor .96478* .14851 .000 enthusiast Extrovert Achiever -.22824 .14048 .367 Perfectionist -.40153* .13978 .023 Outdoor .56324* .13653 .000 enthusiast Outdoor Achiever -.79149* .14918 .000 enthusiast Perfectionist -.96478* .14851 .000 Extrovert -.56324* .13653 .000 Restuarant data Achiever Perfectionist .09906 .15928 .925 completeness (No.of Extrovert .32102 .14707 .131 restaurant in system) Outdoor .81358* .15617 .000 enthusiast Perfectionist Achiever -.09906 .15928 .925 Extrovert .22195 .14633 .429 Outdoor .71451* .15547 .000 enthusiast Extrovert Achiever -.32102 .14707 .131 Perfectionist -.22195 .14633 .429 Outdoor .49256* .14293 .004 enthusiast Outdoor Achiever -.81358* .15617 .000 enthusiast Perfectionist -.71451* .15547 .000 Extrovert -.49256* .14293 .004 Payment option Achiever Perfectionist .17066 .16855 .742 Extrovert .24754 .15562 .386 Outdoor .66272* .16526 .000 enthusiast Perfectionist Achiever -.17066 .16855 .742 Extrovert .07688 .15484 .960 Outdoor .49206* .16452 .016 enthusiast Extrovert Achiever -.24754 .15562 .386 Perfectionist -.07688 .15484 .960 Outdoor .41518* .15125 .033 enthusiast Outdoor Achiever -.66272* .16526 .000 enthusiast Perfectionist -.49206* .16452 .016
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Extrovert -.41518* .15125 .033 Status tracking Achiever Perfectionist .10549 .16757 .922 (Ordering food from Extrovert .06938 .15472 .970 App) Outdoor .39601 .16429 .078 enthusiast Perfectionist Achiever -.10549 .16757 .922 Extrovert -.03612 .15394 .995 Outdoor .29052 .16356 .287 enthusiast Extrovert Achiever -.06938 .15472 .970 Perfectionist .03612 .15394 .995 Outdoor .32664 .15036 .134 enthusiast Outdoor Achiever -.39601 .16429 .078 enthusiast Perfectionist -.29052 .16356 .287 Extrovert -.32664 .15036 .134 Service Achiever Perfectionist .05582 .16483 .987 coverage (Ordering food Extrovert .10263 .15219 .907 from App) Outdoor .10560 .16161 .914 enthusiast Perfectionist Achiever -.05582 .16483 .987 Extrovert .04681 .15142 .990 Outdoor .04979 .16089 .990 enthusiast Extrovert Achiever -.10263 .15219 .907 Perfectionist -.04681 .15142 .990 Outdoor .00298 .14790 1.000 enthusiast Outdoor Achiever -.10560 .16161 .914 enthusiast Perfectionist -.04979 .16089 .990 Extrovert -.00298 .14790 1.000
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Appendix D-5: Mean comparison and standard deviation of restaurant selection criteria via foodie application
Customer Segments Restaurant selection Achiever Perfectionist Extrovert Outdoor Enthusiast criteria via foodie (n=58) (n=59) (n=84) (n=64) application Standard Standard Standard Standard Mean Mean Mean Mean Deviation Deviation Deviation 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 Sum of df Mean Square F Sig. foodie application Squares Between 16.036 3 5.345 8.759 .000 Groups Beautiful photo Within 159.270 261 .610 Groups Total 175.306 264
Between 42.268 3 14.089 16.659 .000 Groups Good reviews Within 220.736 261 .846 Groups Total 263.004 264
Between 18.349 3 6.116 6.194 .000 Groups Location (Near me) Within 257.741 261 .988 Groups Total 276.091 264
Between 5.259 3 1.753 1.777 .152 Groups Variety of menus Within 257.405 261 .986 Groups Total 262.664 264
Appropriate price Between 1.381 3 .460 .582 .628
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Groups
Within 206.634 261 .792 Groups Total 208.015 264
Multiple Comparisons
Mean Difference Dependent Variable (I-J) Std. Error Sig. Beautiful photo Achiever Perfectionist .22969 .14444 .386 Extrovert .50411* .13336 .001 Outdoor .66110* .14162 .000 enthusiast Perfectionist Achiever -.22969 .14444 .386 Extrovert .27441 .13269 .166 Outdoor .43141* .14099 .013 enthusiast Extrovert Achiever -.50411* .13336 .001 Perfectionist -.27441 .13269 .166 Outdoor .15699 .12961 .620 enthusiast Outdoor Achiever -.66110* .14162 .000 enthusiast Perfectionist -.43141* .14099 .013 Extrovert -.15699 .12961 .620 Good reviews Achiever Perfectionist -.00409 .17005 1.000 Extrovert .75862* .15700 .000 Outdoor .85237* .16672 .000 enthusiast Perfectionist Achiever .00409 .17005 1.000 Extrovert .76271* .15621 .000 Outdoor .85646* .16598 .000 enthusiast Extrovert Achiever -.75862* .15700 .000 Perfectionist -.76271* .15621 .000 Outdoor .09375 .15259 .927 enthusiast Outdoor Achiever -.85237* .16672 .000 enthusiast Perfectionist -.85646* .16598 .000 Extrovert -.09375 .15259 .927 Location (Near me) Achiever Perfectionist .13004 .18375 .894 Extrovert .49384* .16965 .020 Outdoor .67241* .18016 .001 enthusiast Perfectionist Achiever -.13004 .18375 .894
Ref. code: 25605902040483CGF 64
Extrovert .36380 .16880 .139 Outdoor .54237* .17935 .014 enthusiast Extrovert Achiever -.49384* .16965 .020 Perfectionist -.36380 .16880 .139 Outdoor .17857 .16488 .700 enthusiast Outdoor Achiever -.67241* .18016 .001 enthusiast Perfectionist -.54237* .17935 .014 Extrovert -.17857 .16488 .700 Variety of menus Achiever Perfectionist .08387 .18363 .968 Extrovert .05542 .16954 .988 Outdoor .37015 .18004 .171 enthusiast Perfectionist Achiever -.08387 .18363 .968 Extrovert -.02845 .16869 .998 Outdoor .28628 .17924 .382 enthusiast Extrovert Achiever -.05542 .16954 .988 Perfectionist .02845 .16869 .998 Outdoor .31473 .16477 .226 enthusiast Outdoor Achiever -.37015 .18004 .171 enthusiast Perfectionist -.28628 .17924 .382 Extrovert -.31473 .16477 .226 Appropriate price Achiever Perfectionist .17387 .16453 .716 Extrovert .03243 .15190 .997 Outdoor .14925 .16131 .791 enthusiast Perfectionist Achiever -.17387 .16453 .716 Extrovert -.14144 .15114 .786 Outdoor -.02463 .16059 .999 enthusiast Extrovert Achiever -.03243 .15190 .997 Perfectionist .14144 .15114 .786 Outdoor .11682 .14763 .858 enthusiast Outdoor Achiever -.14925 .16131 .791 enthusiast 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
Customer Segments Achiever Perfectionist Extrovert Outdoor Enthusiast Key application (n=58) (n=59) (n=84) (n=64) features Standard Standard Standard Standard Mean Deviation Mean Deviation Mean Deviation Mean Deviation Menus & Price 3.60 .86 3.93 .76 3.33 .73 3.05 .65 Restaurant business 3.59 .86 3.88 .74 3.43 .76 3.02 .72 hour 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 3.91 1.00 3.42 .77 3.93 .89 3.66 .93 application Restaurant Booking 4.21 .74 3.51 1.01 4.01 .96 4.08 .90 system 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 df Mean Square F Sig. Squares Between 26.617 3 8.872 15.738 .000 Groups Menus & Price Within 147.134 261 .564 Groups Total 173.751 264
Between 24.115 3 8.038 13.467 .000 Groups Restaurant business Within hour 155.794 261 .597 Groups Total 179.909 264
Between 8.418 3 2.806 3.946 .009 Groups Restaurants database Within 185.620 261 .711 Groups Total 194.038 264
Ref. code: 25605902040483CGF 66
Between 2.467 3 .822 1.232 .298 Groups Review & Rating Within 174.160 261 .667 Groups Total 176.626 264
Between 11.076 3 3.692 4.567 .004 Groups Original content from Within application 210.985 261 .808 Groups Total 222.060 264
Between 16.683 3 5.561 6.662 .000 Groups Restaurant Booking Within system 217.860 261 .835 Groups Total 234.543 264
Between 11.777 3 3.926 4.775 .003 Groups Payment Option Within 214.585 261 .822 Groups Total 226.362 264
Between 3.314 3 1.105 1.092 .353 Groups Promotion Within information 263.946 261 1.011 Groups Total 267.260 264
Multiple Comparisons
Mean Difference Dependent Variable (I-J) Std. Error Sig. Menus & Price Achiever Perfectionist -.32876 .13883 .086 Extrovert .27011 .12818 .153 Outdoor .55657* .13612 .000 enthusiast Perfectionist Achiever .32876 .13883 .086 Extrovert .59887* .12754 .000 Outdoor .88533* .13551 .000 enthusiast Extrovert Achiever -.27011 .12818 .153 Perfectionist -.59887* .12754 .000 Outdoor .28646 .12458 .101 enthusiast Outdoor Achiever -.55657* .13612 .000 enthusiast Perfectionist -.88533* .13551 .000 Extrovert -.28646 .12458 .101
Ref. code: 25605902040483CGF 67
Restaurant business Achiever Perfectionist -.29515 .14286 .167 hour Extrovert .15764 .13190 .630 Outdoor .57058* .14007 .000 enthusiast Perfectionist Achiever .29515 .14286 .167 Extrovert .45278* .13124 .004 Outdoor .86573* .13944 .000 enthusiast Extrovert Achiever -.15764 .13190 .630 Perfectionist -.45278* .13124 .004 Outdoor .41295* .12819 .008 enthusiast Outdoor Achiever -.57058* .14007 .000 enthusiast Perfectionist -.86573* .13944 .000 Extrovert -.41295* .12819 .008 Restaurants database Achiever Perfectionist .07890 .15594 .958 Extrovert .15517 .14397 .703 Outdoor .48330* .15289 .009 enthusiast Perfectionist Achiever -.07890 .15594 .958 Extrovert .07627 .14325 .951 Outdoor .40440* .15220 .041 enthusiast Extrovert Achiever -.15517 .14397 .703 Perfectionist -.07627 .14325 .951 Outdoor .32813 .13992 .091 enthusiast Outdoor Achiever -.48330* .15289 .009 enthusiast Perfectionist -.40440* .15220 .041 Extrovert -.32813 .13992 .091 Review & Rating Achiever Perfectionist .11601 .15104 .869 Extrovert .20197 .13946 .470 Outdoor .26670 .14809 .275 enthusiast Perfectionist Achiever -.11601 .15104 .869 Extrovert .08596 .13876 .926 Outdoor .15069 .14743 .737 enthusiast Extrovert Achiever -.20197 .13946 .470 Perfectionist -.08596 .13876 .926 Outdoor .06473 .13554 .964 enthusiast Outdoor Achiever -.26670 .14809 .275 enthusiast Perfectionist -.15069 .14743 .737 Extrovert -.06473 .13554 .964 Original content Achiever Perfectionist .49006* .16625 .018
Ref. code: 25605902040483CGF 68
from application Extrovert -.01478 .15350 1.000 Outdoor .25754 .16300 .392 enthusiast Perfectionist Achiever -.49006* .16625 .018 Extrovert -.50484* .15272 .006 Outdoor -.23252 .16227 .480 enthusiast Extrovert Achiever .01478 .15350 1.000 Perfectionist .50484* .15272 .006 Outdoor .27232 .14918 .264 enthusiast Outdoor Achiever -.25754 .16300 .392 enthusiast Perfectionist .23252 .16227 .480 Extrovert -.27232 .14918 .264 Restaurant Booking Achiever Perfectionist .69842* .16894 .000 system Extrovert .19499 .15598 .596 Outdoor .12877 .16563 .865 enthusiast Perfectionist Achiever -.69842* .16894 .000 Extrovert -.50343* .15519 .007 Outdoor -.56965* .16489 .004 enthusiast Extrovert Achiever -.19499 .15598 .596 Perfectionist .50343* .15519 .007 Outdoor -.06622 .15159 .972 enthusiast Outdoor Achiever -.12877 .16563 .865 enthusiast Perfectionist .56965* .16489 .004 Extrovert .06622 .15159 .972 Payment Option Achiever Perfectionist .61368* .16766 .002 Extrovert .24261 .15480 .399 Outdoor .17565 .16438 .709 enthusiast Perfectionist Achiever -.61368* .16766 .002 Extrovert -.37107 .15402 .078 Outdoor -.43803* .16365 .039 enthusiast Extrovert Achiever -.24261 .15480 .399 Perfectionist .37107 .15402 .078 Outdoor -.06696 .15045 .971 enthusiast Outdoor Achiever -.17565 .16438 .709 enthusiast Perfectionist .43803* .16365 .039 Extrovert .06696 .15045 .971 Promotion Achiever Perfectionist .08504 .18595 .968 information Extrovert .26724 .17168 .405
Ref. code: 25605902040483CGF 69
Outdoor .25162 .18231 .513 enthusiast Perfectionist Achiever -.08504 .18595 .968 Extrovert .18220 .17082 .710 Outdoor .16658 .18150 .795 enthusiast Extrovert Achiever -.26724 .17168 .405 Perfectionist -.18220 .17082 .710 Outdoor -.01563 .16685 1.000 enthusiast Outdoor Achiever -.25162 .18231 .513 enthusiast 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
Customer segments Impact of promotion on Achiever Perfectionist Extrovert Outdoor Enthusiast purchase intent (n=58) (n=59) (n=84) (n=64)
Mean Sd Mean Sd Mean Sd Mean Sd
Price promotion will make you use more online food 4.21 .77 4.31 .84 4.29 .61 4.06 .61 delivery service
Appendix F-2: ANOVA test on price promotion impact on purchase intent
ANOVA
Price promotion will make you use more online food delivery Sum of Mean df F Sig. service Squares Square
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
Ref. code: 25605902040483CGF