how online food photos on instagram influence thai

HOW ONLINE FOOD PHOTOS ON INSTAGRAM

INFLUENCE THAI CUSTOMERS’ ATTITUDE AND

PERCEPTION TOWARDS OFFLINE THAI

RESTAURANTS

BY

MISS THANYAPHORN TANTHAPHRUEKPHON

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 2018

COPYRIGHT OF THAMMASAT UNIVERSITY

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Independent Study Title HOW ONLINE FOOD PHOTOS ON

INSTAGRAM INFLUENCE THAI

CUSTOMERS’ ATTITUDE AND

PERCEPTION TOWARDS OFFLINE THAI

RESTAURANTS

Author Miss Thanyaphorn Tanthaphruekphon

Degree Master of Science Program in Marketing

(International Program)

Major Field/Faculty/University Faculty of Commerce and Accountancy

Thammasat University

Independent Study Advisor Associate Professor James E. Nelson, Ph.D.

Academic Year 2018

ABSTRACT

The restaurant in Thailand is highly competitive due to low barrier to entry

and huge opportunity from increase trend of eating out. In the digitalization era, social

media play important role in the customer’s journey so most of businesses in Thailand

including restaurant industry are using these channels to market their products and

services.

Instagram is the social media application made for sharing photos and

videos from a smartphone. Consequently, A lot of businesses including restaurant

industry in Thailand use this application for marketing and revenue purposes.

Therefore, the purpose of this individual study is to investigate compositional elements

of good Thai food photos to help the restaurant owners choose the proper photos that

match the position of their restaurant and can attract the customers to visit their

restaurants.

Both qualitative and quantitative methods were used in this research. The

research was conducted by using Exploratory Research and Causal Research Design as

field experiment. The questionnaire was conducted using a (2x2x2) experimental

format, a total of eight treatments. 30 respondents were collected for each treatment

totaling to 240 respondents. The data analysis for questionnaire were done with the

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Statistical Package for the Social Sciences (SPSS) to analyze means, frequency,

regression and correlations between variables etc.

The Results showed no relationship between customers’ demographic and

price perception. But age and education level impact intention to visit the restaurants.

In terms of customer behavior, only eat out frequency impacts both price perception

and intention to visit the restaurant. In terms of compositional elements of food photo,

the most important factor is type of container. It impacts “customers’ belief in quality

of food”, “customers’ belief in healthiness of food” and “customers’ belief in good

service”. Fancy container is better than plain for all beliefs and price perception. The

second important factor is size of container. It affects “customers’ belief in healthiness

of food”. Small container is better than big container for this belief and price perception.

The least important factor is props in the photo. Although it does not impact customers’

beliefs, photo with food ingredients has more positive effects and is perceived slightly

higher price that without food ingredients.

Keywords: Online food photo, Instagram food photo, Intent to visit restaurant,

Perception on price of food, Experimental study.

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ACKNOWLEDGEMENTS

I would like to express my sincere gratitude to all those who supported me

and gave me suggestion for this Independent Study. I am heartily grateful to my dearest

advisor, Associate Professor James E. Nelson, Ph.D. for his closely supervision and

valuable discussion throughout the study course. Without his recommendations, I

would not have completed this Independent Study.

In addition, I would like to express my special thanks to all respondents who

gave me their valuable time for both in-depth interviews and filed experiment. All of

their supports largely contributed to the success of this research. I also deeply appreciate

my family members. Without them, I could not have accomplished this Master’s

Degree.

Lastly, I would like to express special thanks to Au, my classmate for

helping me process SPSS program. I also thank you the Master’s Degree Program in

Marketing (MIM program), all MIM Professors, MIM Director, MIM Office and my

classmates, MIM31, for their support and encouragement.

Miss Thanyaphorn Tanthaphruekphon

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TABLE OF CONTENTS

Page

ABSTRACT (1)

ACKNOWLEDGEMENTS (3)

LIST OF TABLES (6)

LIST OF FIGURES (7)

CHAPTER 1 INTRODUCTION 1

1.1 Introduction to the study 1

1.2 Research objectives 2

CHAPTER 2 REVIEW OF LITERATURE 4

2.1 Academic theory implication 4

2.2 Literature review 5

CHAPTER 3 RESEARCH METHODOLOGY 9

3.1 Research design 9

3.1.1 Exploratory research 9

3.1.2 Causal research design 10

CHAPTER 4 DATA ANALYSIS AND RESULTS 15

4.1 Secondary research analysis 15

4.2 In-depth interview analysis 15

4.3 Field experiment analysis 16

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4.3.1 Respondent profile 16

4.3.2 Questionnaire result 17

CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS 27

5.1 Conclusion 27

5.2 Recommendations 28

5.2.1. Instagram should be a part of restaurants’ marketing tool. 28

5.2.2. Compositional elements of good food photos. 28

5.3 Limitation of the study 29

REFERENCES 31

APPENDICES 34

Appendix A: Questions for in-depth interviews 35

Appendix B: Factor combination and treatment numbers 36

Appendix C: Questions for field experiment 37

Appendix D: Respondents’ profile based on demographic 44

Appendix E: Respondents’ profile based on behavior 45

Appendix F: Perception on price and type of container 46

Appendix G: Perception on price and size of container 47

Appendix H: Perception on price and prop in the photo 48

BIOGRAPHY 49

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LIST OF TABLES

Tables Page

3.1 Detail Sample size by data collection method 13

4.1 Correlation between Thai customers’ perception on price and

demographic 17

4.2 Correlation between Thai customers’ perception on price and Thai

customers’ behavior 18

4.3 Correlation between Thai customers’ perception on intention to visit

the restaurant and demographic 19

4.4 Correlation between customers’ perception on intention to visit

the restaurant and customers’ behavior 19

4.5 Sample means and standard deviation for dependent variable (customers’

beliefs) on size of container 20


beliefs) on type of container 21


beliefs) on prop in the photo 22

4.8 Three-Way ANOVA customers’ belief in quality of food and controlled

variables 23

4.9 Three-Way ANOVA customers’ belief in healthiness of food and

controlled variables 24

4.10 Three-Way ANOVA customers’ belief in cleanliness of the restaurant

and controlled variables 25

4.11 Three-Way ANOVA customers’ belief in good service and controlled

variables 26

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LIST OF FIGURES

Figure Page

2.1: Five stages of consumer buying decision process 5

3.1: Conceptual framework of the study 11

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CHAPTER 1

INTRODUCTION

1.1 Introduction to the study

Restaurant industry in Thailand is critical and highly competitive.

According to Food Service - Hotel Restaurant Institutional report (Sirikeratikul, 2018)

there are over 100,000 restaurant establishments across the country.

In the digitalization era, social media influences customers along their

journey more than before. Thailand becomes one of the top 10 ranking for mobile social

media penetration and top four for time spent on social media, according to social media

management platform Hootsuite and global agency (Suchit, 2018). Therefore, instead

of just waiting for their customers to reach their offline brick-and-mortar, a lot of

businesses including restaurant industries rather use social media to market their

products or services to the customers.

Instagram is one of the most popular social media platforms. It is a social

networking app made for sharing photos and videos from a smartphone. When you post

a photo or a video, it will be shown on your profile. Other users who follow you can

see your posts. In the same way, you can see others’ posts whom you follow on the new

feeds (Moreau, 2018). There are 600 million Instagram users worldwide and 51% of

those access the application daily. Instagram achieves the greatest engagement per

follower, at 50 interactions per post per 1,000 followers compared with Facebook’s six

(Spunky Trends, 2017). Through Instagram, customers can follow the brands they like

and receive message from the brands easily. Consequently, this creates huge

opportunities for marketing, branding and revenue purpose for businesses including

restaurant industry to use a visual marketing tool like Instagram to magnetize

customers’ attention, create awareness and attract customers to visit their restaurants.

However, due to high competitiveness in restaurant industry in Thailand, this is not a

piece of cake for the restaurant owners to do so. Therefore, it is crucial for the restaurant

owners to know what good photos in their customers’ mind can be.

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Despite a lot of studies and researches on social media and restaurant

industry, little is known about the factor influences people’s positive attitude towards

Thai restaurant in terms of Thai food photo on Instagram so the purpose of this research

is to investigate compositional elements of good food photos on Instagram for

restaurants. This research will directly benefit Thai restaurant owners to understand

how to apply the strategies of food photos on Instagram to induce stimulus to attract

customers to visit the restaurant effectively and use the right photos to position their

restaurants. This study is a contemporary topic in applied marketing focusing on

technology issue.

1.2 Research objectives

In order to achieve the purpose of the study which is to investigate

compositional elements of good food photos on Instagram for restaurants, both

qualitative and quantitative methods were conducted. This research was based on the

following objectives:

1.2.1. To determine Thai customers’ perception on price in order to help

restaurant owners in Thailand use food photos that match their restaurant positioning.

1.2.1.1.To determine Thai customers’ perception on price based on

demographic: age, gender, education, income

1.2.1.2.To determine Thai customers’ perception on price based on

behavior: Instagram usage duration, eating out frequency

1.2.2. To determine Thai customers’ perception on intent to visit the

restaurants in order to help the restaurant owners in Thailand use proper food photos to

attract the customers.

1.2.2.1.To determine Thai customers’ perception on intent to visit

the restaurants based on demographic: age, gender,

education, income

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1.2.2.2. To determine Thai customers’ perception on intent to visit

the restaurants based on behavior: Instagram usage

duration, eating out frequency

1.2.3. To identify compositional elements of food photos on Instagram that

impact Thai customers’ perception on other restaurant features as Halo effect. These

features include quality of food, healthiness of food, cleanliness of restaurant and good

service.

For the mutual understanding, the definitions of words used in this

research are listed below;

a. Composition means the arranging of elements within the frame of

a photograph (Goldby, 2018).

b. Instagram user means a person who has an Instagram account and

accesses it in the past three months.

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CHAPTER 2

REVIEW OF LITERATURE

2.1 Academic theory implication

A recently published book (Kotler & Keller, 2015, pp. 194-201) provides

a relevant model of the consumer buying decision process. This model can especially

be used with high involvement purchased product or service like choosing the

restaurants. It said that the consumers typically pass through five stages: problem

recognition, information search, evaluation of alternatives, purchase decision, and post-

purchase behavior (See Figure 2.1).

Problem recognition is the first state in the buying decision process. It

occurs when the customers recognize problem or need triggered by internal or external

stimuli. The marketer need to develop marketing strategies that increase consumer

motivation.

After the problem is recognized, the customers will search for the

information. The marketer must understand what kind of information customers seek

for at different time and place, so they can communicate to the target customers

effectively.

Once the customers collect all information, they will evaluate what fits them

the most and delivers them sought-after benefits. The marketer has to persuade

customers to attach more importance to the attributes in which the firm excels.

In purchase decision state, the customers may make decision based on value

they perceived and may afraid of making a wrong decision. Marketers must understand

the factors of feeling of risk and provide information to reduce it. After the purchase,

the consumer may have bad experience about the products or hear favorable things

about other brands. So, the marketer must monitor post-purchase satisfaction, post-

purchase actions, and post-purchase product uses and disposal.

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Figure 2.1: Five stages of consumer buying decision process (Kotler & Keller, 2015)

Another recent article provides relevant theory called Halo effects. Halo

effect is a type of cognitive bias in which our all impression of someone or something

influences how we feel or think about them (Cherry, 2018). People likely to rate

attractive people or things more favorably for their characteristics than those who or

which are less attractive. For example, a good looking person may be perceived smart

and intelligent. Therefore, when respondents rate an object on an attribute, they may

bias in their judgements.

2.2 Literature review

Food Service - Hotel Restaurant Institutional report (Sirikeratikul, 2018)

showed that the restaurant industry in Thailand has been growing and becomes more

competitive. As of 2017, there are over 100,000 restaurant establishments across the

country. According to the National Economic and Social Development Board,

Thailand’s hotel and restaurant sectors expanded by 6.7% in the third quarter of 2017.

This continues to grow due to increased spending on dining out by upper-income Thai

consumers and low barrier to entry for new comers. The restaurant industry remains

positive due to urbanization, higher income and a trend towards eating out especially

during the holiday. In order to attract more customers in this competitive industry, the

restaurants also implement new strategies such as improving food quality and

ambience, together with extending menu selections. The changing in lifestyles and

behavior also leads to the changes of marketing strategy of the restaurants in terms of

using online channels to introduce new brands, increase sales and manage relationship

with customers.

As social media provide opportunities to run businesses online, internet

penetration especially for the entrepreneur tends to increase. Thai people are the highest

mobile internet users with average of four and a half hours per day (Christopher, 2018)

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and according to National Statistical Office of Thailand, more than 90% of internet

users in Thailand go online via smartphone. In Bangkok, the capital city of Thailand,

more than 70% of overall population uses smartphones (Kressmann, 2017). That is the

reason why the heavy social media users are in this city. Top three online activities of

internet users via mobile are Facebook, Instagram and Line. The heavy internet users

are between 15 and 54 years old (Boonperm, Wayuparb, Mutraden, &

Tangpoolcharoen, 2016).

Instagram as mentioned above is one of the most popular online activities

among Thai people. It was established in October 2010. It became number one in the

App Store within 24 hours of launch and hold the record as quickest to reach 1 million

downloads occurring in 2 months after launch. Instagram is popular due to its functions.

With Instagram, users can interact with other users from all over the world by pressing

like, commenting on photos or sending the direct message to the Instagram owner. The

Instagram users can also tag photos with location and manipulate their photos with

different filters which make boring photos look amazing (Lux, 2011). This is also

interesting that the brain processes images 60,000 times faster than it does text. And

it’s more accustomed to processing images—90% of the information sent to the brain

is visual, and 93% of all human communication is visual (Pant, 2015). With user-

friendly interface, Instagram becomes one of the most popular online channels.

In Thailand, the number of Instagram users totaled 11 million as of May

2017, up 41% year-on-year, putting the country at 13th by user numbers (Fredrickson,

2017). Instagram penetration in Bangkok is 19%, compared with the worldwide

average of 11% (Suchit, 2018). As of April 2018, more than 150 million Instagram

users were connecting with businesses each month. And 33% of these conversations

generally begins with an Instagram story (Christopher, 2018).

Therefore, Instagram becomes one of the effective communication channel

for restaurant owners to market their food and stimulate customers’ desire for food.

From the recent study (Petit, 2016, pp. 252-253), people who was shown a food photo

not only recall their past experience, they can also imagine the smell, texture of food

and taste. Therefore, when people see food photo on Instagram, their ability of self-

control reduced and that leads to demanding for food. Also, a relevant article (Olivera,

n.d.) talked about the blooming usage of Instagram among millennials. 18 to 35-year

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old spend five whole day a year browsing food image on Instagram and 30% will avoid

the restaurant if the photo is weak. In the past, word-of-mouth was a good way for the

companies to know whether they can retain their customers but now the amount of

Instagram followers, likes and good photo quality determine that. According to Tech

Crunch, 80% of users follow at least one business. 60% says that they learn about a

product or service on the Instagram and 70% of Instagram users take action, such as

visiting a website, after looking at an Instagram advertising post.

Consequently, in order to attract the customers in this high competitive

industry, a lot of restaurants try to make their Instagram aesthetic. Many of them take

high-quality photos of their meals for their Instagram. However, it is not easy to define

what is the criteria of good or bad food photo.

According to the relevant article (Menon, 2015) talking about the good

composition in food photo, there are six elements that are important to food photo

which are rule of thirds, background, props, the angle or orientation of the image, the

use of colors and the use of negative space or white space. There are also several

research groups that have analyzed various factors of food attractiveness. Piqueras-

Fiszman et al. (2012) studied whether the color of the plate influenced people’s taste

and flavor perception. Black and white plates were used in the experiment. The research

concluded that the more people perceived color intense of dessert on a plate of one

color, the more dessert is perceived as more appetizing and more intense in flavor on

the same plate. Kawanishi et al. (2016) studied the attractive of different type of

Japanese food photos. The research showed that the attractiveness of food was affected

by various factors including colors and shapes and varied for each food category. These

researches, however, neither consider the factors on the attractiveness of Thai food

photos nor effect of compositional elements of food photo on perception on price.

Summary of literature review

Restaurant industry has been growing and becomes more competitive due to the

low barrier to entry and changing in eating behavior. Thai people especially those in

the urban area eat out more often especially during holiday. To attract both existing and

new customers, restaurant owners are doing their best to implement new strategies such

as improving ambience and menu selections. Due to heavy usage of social media among

Thai people especially those aged between 15 and 54 years old, a lot of businesses

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including restaurant industry use online channel to market their products and services

to their customers.

Instagram become one of the most popular social media in Thailand. With

user friendly interface, Instagram has more than 11 million Thai users. A lot of

businesses start using Instagram to communicate and engage their customers.

Restaurant industry as well uses this application to market their food and stimulate

customers’ desire for food. People aged between 18 and 35 years old spend five whole

day a year browsing food image. It was proved by the recent research that when people

see food photo on Instagram, their ability of self-control reduced and that leads to

demanding for food however people will avoid the restaurant if they don’t like the

photo. Therefore, food industry takes high quality of their meals for their Instagram.

Despite lots of studies about good composition in food photography, little is known

about good compositional elements of Thai food photography and effect of those

elements on attitude on price and intent to visit. Therefore, the purpose of this study is

to address these gaps.

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CHAPTER 3

RESEARCH METHODOLOGY

3.1 Research design

To deeply understand the compositional elements of good food photos on

Instagram for restaurants, both qualitative and quantitative methods were conducted by

using two designs which were Exploratory research and Causal research.

3.1.1 Exploratory research

Secondary data

The purpose of Secondary Data research is to understand the

definition and overall food industry and Instagram, the situation of food industry and

Instagram, marketing model that can be applied for this industry and compositional

elements of good food photo. The information was obtained from websites, books,

blogs, reports etc.

In-depth interview

Due to the plenty of compositional elements of photo obtained from

secondary data and insufficient information on Thai food photos, pre-study using in-

depth interviews was conducted to confirm the compositional elements obtained from

secondary data and to find any other compositional elements of photo that influent

customers’ perception and attitude towards Thai food in terms of intent to visit the

restaurants and price.

Three In-depth interviews were conducted on 25th and 26th October.

First respondent is a 25-year-old female, a fashion cloth shop owner. Second respondent

is a 29-year-old male, a coffee shop owner. And the last one is a 32-year-old male, a

food designer. All respondents live in Bangkok and go to the restaurants due to food

photos on the Instagram. They were interview face-to-face separately in the coffee

shops at Rachatewee and Victory monument. Each interview took around 20 minutes.

Lists of questions were provided to the interviewer (See Appendix A). The interviewer

was taking note during the interviews. The objectives of in-depth interviews were to

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confirm the independent variables gained from secondary data and to find other

independent variables which are compositional elements of food photo that effect

customers’ attitude towards price and intent to visit the restaurants.

All respondents said in common about the compositional elements of

good photo which were size of container (big or small), type of container (Plain or

fancy) and props in the photo (with or without food ingredients)

3.1.2 Causal research design

Causal research design as a field experiment was conducted to

determine Thai customers’ perception on price in order to help restaurant owners in

Thailand use food photos that match their restaurant positioning (Objective 1.2.1), to

determine Thai customers’ perception on intent to visit the restaurants in order to help

the restaurant owners in Thailand use proper food photos to attract the customers

(Objective 1.2.2) and to identify compositional elements of food photos on Instagram

that impact Thai customers’ perception on other restaurant features as Halo effect

(Objective 1.2.3). The information was obtained from 240 respondents from online

survey and screened out the error responded, uncompleted questionnaires and Non-

Instagram users. The study was done as a (2x2x2) experiment which was a total of eight

treatments. Each treatment contained a unique combination of independent variables

(IVs) found from in-depth interviews which were “size of the container”, “type of the

container” and “prop in the photo” (See Appendix B). Other four mediating variables to

test Halo effect were “customers’ perception on quality of food”, “customers’

perception on healthiness of food”, “customers’ perception on cleanliness of restaurant”

and “customers’ perception on good service”. Questions for each scenario were exactly

the same (See Appendix C).

Key variables

According to the objectives of this research, the conceptual

framework shown in Figure 2. demonstrated relationships between independent

variables which were customers’ demographic (age, gender, education level and

monthly income), customers’ behavior (Instagram usage and eat out frequency) and

compositional elements of food photo (size of container, type of container and prop in

the photo) and dependent variables which were “Thai customers’ perception on price”

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and “Thai customers’ perception on intent to visit the restaurants”. And, variables for

Halo effect was the mediating variable that strengthens the relationship between

independent and dependent variables.

This conceptual framework was created from the information that

researcher gained from secondary data and in-depth interviews. The obtained

information could be concluded into key variables as shown in Figure 3.1.

Independent variables Dependent variables

Figure 3.1: Conceptual framework of the study

Customers’ Demographic

- Age

- Gender

- Education level

- Monthly income

Customers’ behavior

- Instagram usage

- Frequency

- Time spent on Instagram

- Eat out frequency

Elements of food photo

-Size of container

- Big

- small

-Type of container

-Plain

-Fancy

-Prop in the photo

-With food ingredients

-Without food ingredients

Halo effect: Customers’ belief in

-Quality of food

-Healthiness of food

-Cleanliness of restaurant

-Good service

- Thai customers’

perception on price

- Thai customers’

perception on intent to

visit the restaurants

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Target group This research focused on Thai people both male and female aged

between 15 and 54 as those group are the heavy internet users reported by Thailand

Internet user profile 2015 (Boonperm, Wayuparb, Mutraden, & Tangpoolcharoen,

2016). All of them must be Instagram users and eat out at least once a month.

Recruiting methodology Because of time constraint, convenience sampling was used for both

Exploratory and Causal research. The respondents of both in-depth interviews and field

experiment had to pass the screening questions.

For in-depth interviews, three respondents were recruited from

friends’ references. All respondents met the target group criteria which were between

15 and 54 years old, an Instagram user and went to the restaurant because of food photo

on the Instagram. They live in Bangkok and can have face-to-face interviews.

For field experiment, timeframe of the survey was within a month.

A pilot test was launched to 10 respondents in order to validate the understanding of

questionnaire and time spent per person. Then, the online questionnaire was launched

to total number of 240 respondents. Each of eight treatments was launched to 30 people

by online social network applications such as Facebook and Line. All respondents

needed to pass the screening questions. In order to get the high response rate, three

Starbucks gift cards valued 200 Baht each were given as prizes for the lucky draw

among the respondents. Details of sample size are demonstrated in Table 3.1.

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Table 3.1: Detail Sample size by data collection method

Methodology Data

collection

method

Pilot study Sample size Details

1.Qualitative In-depth

interviews

- 3 respondents -a 29-year-old

male, a coffee

shop owner

-a 25-year-old

female, a fashion

cloth shop owner

- a 32-year-old

male, a food

designer

2.Quantitative Questionnaire

(Field

experiment)

10

respondents

240

respondents

30 respondents

per treatment for

total of 8

treatments

Data analysis plan

This research was conducted in both qualitative and quantitative

analysis. Qualitative information was obtained from Secondary Data and in-depth

interviews. Quantitative information was obtained from Causal research as field

experiment analyzed by using Statistical Package for the Social Sciences (SPSS) to

analyze means, frequency and correlations between variables and etc.

Field experiment using (2x2x2) experimental format was to test on

independent variables which are size of container (big or small), type of container (Plain

or fancy) and props (with or without food ingredients). These variables were obtained

from secondary data and in-depth interviews. Those independent variables were tested

on how they affected two dependent variables which are “customers’ perception on

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pricing” and “customers’ perception on intent to visit the restaurants”. In addition, they

were test on other dependent variables for Halo effect which are “customers’ belief in

quality of food”, “customers’ belief in healthiness of food”, “customers’ belief in

cleanliness of the restaurant” and “customers’ belief in good service”.

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CHAPTER 4

DATA ANALYSIS AND RESULTS

4.1 Secondary research analysis

Restaurant industry is one of the most competitive industries in Thai market

due to low barrier to entry. Instead of attracting customers on the point of purchase, a

lot of restaurants implement new strategy to attract the customers via online channels.

Since Thai people dramatically spend time online, it creates opportunity for the

restaurants to expand the communication channel. Not only the restaurants can create

awareness, it can also use photo content to attract customers. However, food photos can

be double-edged sword. Because good food photos can help induce and stimulate

customers’ demand for food. On the other hand, bad ones can drive out the customers.

So, it is crucial for the restaurants to know what compositional elements of good food

photo are.

4.2 In-depth interview analysis

In-depth interviews were conducted on 25th and 26th October 2018. The

questions were listed to confirm the independent variables gained from secondary data

and to find other independent variables that affect dependent variables which are price

and intent to visit the restaurants.

All respondents said that photo with food ingredients looked attractive and

expensive. One of them said “food ingredients show that the restaurant made an effort

to photograph food. Ingredients make photo look good, tasty and expensive.” All of

them also said that little portion of food on the big plate or container made food look

expensive. Fancy container also made food look interesting and expensive. One of them

said that “While I scroll down on my Instagram feeds, I will stop to look at food photo

if I see a fancy container especially, the weird one. I feel like the restaurant sells both

food and idea of food presentation, so price must be higher but that’s ok for me. I will

go there and try that dish”. Two respondents said that the photo of food with nice

restaurant ambience attracted them to the restaurant. One of them said “I love taking

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photo with food and post it on my Facebook and Instagram so I will go to the restaurant

if I see the nice ambience of it”. Another said something similar “When I go to the

restaurant, I do not only photograph food, I also photograph the restaurant ambience.

So, if I see the nice restaurant on Instagram, I’ll go there”. Only one respondent said

that his intension to visit depends on the surrounding temperature at that moment. He

said “If that day is really hot, spicy-kind-of-food photo will not attract me to the

restaurant”. Two of respondents said that vivid color did not make any difference as

long as other compositional elements like props and container looked good.

As a result, only three compositional elements which are size of container

(big or small), Type of container (plain or fancy) and props (with or without food

ingredients) affect respondents’ perception and attitude towards price and intent to visit

restaurant. So, they were used as independent variables in this research.

4.3 Field experiment analysis

4.3.1 Respondent profile

There were 240 valid respondents randomly selected at 30

participants per each treatment. In terms of demographic, the majority of 240 qualified

respondents were female accounting for 70 percent, and 70 percent aged between 25

and 34, around 27 percent had income from 30,001 to 45,000 baht per month, around

50 percent had education in Bachelor’s degree and 40 percent had education in Master’s

degree or higher (See Appendix D). However, there was a small portion of respondents

whose age were between 45 and 54 years old accounted for only one percent. In

addition, for education level, the small portion of respondents had education level lower

than high school.

In terms of eat out behavior, it was slightly different between three

groups. Majority of respondents (around 37 percent) ate out one to four times a month.

However, the other two groups were slightly different from majority one accounting

for around 32 percent each. In terms of Instagram usage behavior, more than half of

respondents (59.17 percent) accessed Instagram more than 14 times a week. Only five

percent accessed less than once a week. In addition, more than half of respondents

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(53.33 percent) spent one to ten minutes on Instagram per time. Only six percent spent

more than 30 minutes per time (See Appendix E).

4.3.2 Questionnaire result

4.3.2.1 Thai customers’ perception on price

According to the research objective 1.2.1, this research is to

determine customers’ perception on price (DV) based on demographic and behavior

(IVs). From correlation analysis, the researcher concluded no significant relationship

between dependent variable which is “Thai customers’ perception on price” and

independent variables which are demographic: age, gender, education level and

monthly income (See Table 4.1).

Table 4.1: Correlation between Thai customers’ perception on price and demographic

Thai customers’

perception on

price of food

Pearson

Correlation

Sig. (2-tailed)

Age -.07 .25

Gender -.06 .35

Education level .06 .32

Monthly income .07 .28

However, in terms of correlation between dependent

variable which is “Thai customers’ perception on price of food” and independent

variables which are Thai customers’ behavior: “frequency of eating out per month”,

“frequency of accessing Instagram per week” and “time spent on Instagram per time”,

only “frequency of eating out per month” had significant relationship with “Thai

customers’ perception on price of food” with a p-value less than 0.05 (See Table 4.2).

With positive Pearson Correlation, it implied that the more people eat out, the higher

price of food they perceive.

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Table 4.2: Correlation between Thai customers’ perception on price and Thai

customers’ behavior

Thai customers’

perception on

price of food

Pearson

Correlation

Sig. (2-tailed)

Frequency of eating

out per month

.15 .02

Frequency of

accessing

Instagram per week

.00 .97

Time spent on

Ingram per time

-.02 .81

4.3.2.2 Thai customers’ perception on intent to visit the

restaurant

To answer the research objective 1.2.2, this research was to

determine “Thai customers’ perception on intent to visit the restaurants (DV)” based

on demographic and behavior (IVs). In terms of correlation between dependent

variable: intention to visit the restaurant and independent variables: age, gender,

education level and monthly income, there were high relationship with age and

education level at p-value less than 0.01 and 0.05 respectively. Monthly income had

close relationship at p-value of 0.07. All of the dependent variables had negative

Pearson correlation which means that the older people and the higher education level,

the less intention to visit the restaurant (See Table 4.3).

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Table 4.3: Correlation between Thai customers’ perception on intention to visit the

restaurant and demographic

Intension to visit

the restaurant

Pearson

Correlation

Sig. (2-tailed)

Age -.20 .00

Gender -.08 .25

Education level -.15 .02

Monthly income -.12 .07

Table 4.4 demonstrated relationship between dependent variable:

intention to visit and independent variables: Thai customers’ behavior. Only one

variable which is “frequency of eating out per month” had significant relationship with

intention to visit the restaurant at p-value less than 0.05. With negative Pearson

Correlation, it means the more people eat out, the less they want to visit the restaurant.

Table 4.4: Correlation between customers’ perception on intention to visit the

restaurant and customers’ behavior

Intension to visit

the restaurant

Pearson

Correlation

Sig. (2-tailed)

Frequency of

eating out per

month

-.16 .01

Frequency of

accessing

Instagram per week

-.09 .19

Time spent on

Ingram per time

.02 .74

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4.3.2.3. Controlled variables and dependent variables

validation

To answer the objective 1.2.3 which is to identify

compositional elements of food photos on Instagram that impact Thai customers’

perception on other restaurant features as Halo effect, Sample means and standard

deviation and Three-way ANOVA were done using controlled variables to test the

dependent variables. The results were stated below.

Sample means and standard deviation were demonstrated

relationship between variables and dependent variables for Halo effect. The control

variables are “size of the container (big or small)”, “type of the container (fancy or

plain)” and “prop in the photo (with or without prop in food ingredients)”. The

dependents variables are “customers’ belief in cleanliness of the restaurant”,

“customers’ belief in healthiness of food”, “customers’ belief in quality of food” and

“customers’ belief in good service”.

In terms of size of container, although small container had

lower mean on “customers’ belief in cleanliness of the restaurant”, the means were

slightly different at 0.03. It will not hurt the restaurant if the restaurant uses small

container. For the rest three beliefs which are “customers’ belief in healthiness of food”,

“customers’ belief in quality of food” and “customers’ belief in good service”, small

container had much higher mean with the differences in means of .40, .16 and .18

respectively (See Table 4.5).

Table 4.5: Sample means and standard deviation for dependent variable (customers’

beliefs) on size of container

Size of

container N Mean Std. Deviation

Std. Error

Mean

Customers’ belief in

cleanliness of the

restaurant

Small 120 4.71 1.15 .11

Big 120 4.74 1.32 .12


healthiness of food

Small 120 4.13 1.19 .11

Big 120 3.73 1.35 .12

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Size of


Std. Error

Mean


quality of food

Small 120 4.29 1.27 .12

Big 120 4.13 1.16 .11


good service

Small 120 4.13 1.27 .12

Big 120 3.95 1.22 .11

In terms of “type of container”, fancy container had higher

mean than plain container in all beliefs which are “cleanliness of the restaurant”,

“healthiness of food”, “quality of food” and “good service”. This means using fancy

container is better.

The biggest difference in means was “customers’ belief in

good service” which was 0.44. The smallest difference in mean was “customers’ belief

in cleanliness of the restaurant” which was 0.10 (See Table 4.6). That means fancy

container affects “customers’ belief in good service” the most and affects “customers’

belief in cleanliness of the restaurant” the least.


beliefs) on type of container

Type of


Std.

Error

Mean


cleanliness of the

restaurant

Plain 120 4.68 1.22 .11

Fancy 120 4.78 1.25 .11


healthiness of food

Plain 120 3.72 1.19 .11

Fancy 120 4.14 1.34 .12


quality of food

Plain 120 4.03 1.14 .10

Fancy 120 4.38 1.27 .12

Plain 120 3.82 1.14 .10

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good service

Fancy 120 4.26 1.31 .12

In terms of prop in the photo, photo with food ingredients

had higher mean for “customers’ belief in cleanliness of the restaurant” and “customers’

belief in quality of food”. The differences in means were 0.10 and 0.16 respectively.

Both control variables had equal mean for “customers’ belief in healthiness of food”.

However, without props in the photo had slightly higher mean for “customers’ belief in

good service” which was only 0.02 (See Table 4.7).

That means using food ingredients in the photo has a positive

effect on “customers’ belief in cleanliness of the restaurant” and “customers’ belief in

quality of food”. Even it gained lower mean for “customers’ belief in good service”, it

would not hurt the restaurant because the means were slightly different.


beliefs) on prop in the photo

Prop in the

photo N Mean

Std.

Deviation

Std. Error

Mean


cleanliness of the restaurant

No 120 4.68 1.18 .11

Yes 120 4.78 1.29 .12


healthiness of food

No 120 3.93 1.34 .12

Yes 120 3.93 1.22 .11

Customers’ belief in quality

of food

No 120 4.13 1.23 .11

Yes 120 4.29 1.20 .11

Customers’ belief in good

service

No 120 4.05 1.17 .11

Yes 120 4.03 1.32 .12

Three-Way ANOVA was conducted to test relationship

between each dependent variable and control variables. Table 4.8 demonstrated the

relationship between dependent variable: “customers’ belief in quality of food” and

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control variables: “size of the container”, “type of the container” and “prop in the

photo”.

The researcher concluded that at R Squared of .04, only type

of container was significant at p-value less than 0.05. It can be inferred that only type

of container impacts “customers’ belief in quality of food”.

Table 4.8: Three-Way ANOVA customers’ belief in quality of food and controlled

variables

For “customers’ belief in healthiness of food”, with R square

of .08, “type of container” was significant at p-value less than 0.01 and “size of

container” was significant at p-value less than 0.05. It can be inferred that “type of

container” and “size of container” impact “customers’ belief in healthiness of food”

(See Table 4.9).


quality of food

Type III Sum

of Squares df

Mean

Square F Sig.

Corrected Model 14.98a 7 2.14 1.47 .18

Intercept 4250.41 1 4250.417 2912.28 .00

Size 1.67 1 1.67 1.14 .29

Type 7.35 1 7.35 5.04 .03

Prop 1.67 1 1.67 1.14 .29

Size * type .07 1 .07 .05 .83

Size * Prop 1.35 1 1.35 .93 .34

type * Prop .07 1 .07 .05 .83

Size * type * Prop 2.82 1 2.82 1.93 .17

Error 338.60 232 1.46

Total 4604.00 240

Corrected Total 353.58 239

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Table 4.9: Three-Way ANOVA customers’ belief in healthiness of food and

controlled variables


healthiness of food

Type III

Sum of

Squares df

Mean

Square F Sig.


Intercept 3705.20 1 3705.20 2371.33 .00

Size 9.20 1 9.20 5.89 .02

Type 10.84 1 10.84 6.94 .01

Prop .00 1 .00 .00 .96

Size * Type .34 1 .34 .22 .64

Size * Prop 2.60 1 2.60 1.67 .20

Type * Prop 5.10 1 5.10 3.27 .07

Size * Type * Prop 1.20 1 1.20 .77 .38

Error 362.50 232 1.56

Total 4097.00 240


Table 4.10 demonstrated the relationship between

“customers’ belief in cleanliness of the restaurant” and controlled treatments. The

researcher concluded no significant relationship between them.

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Table 4.10: Three-Way ANOVA customers’ belief in cleanliness of the restaurant and

controlled variables


cleanliness of the

restaurant

Type III

Sum of

Squares df

Mean

Square F Sig.

Corrected Model 3.58a 7 .51 .33 .94

Intercept 5358.15 1 5358.150 3450.474 .000

Size .07 1 .07 .04 .84

Type .60 1 .60 .39 .54

Prop .60 1 .60 .39 .54

Size * Type 1.35 1 1.35 .87 .35

Size * Prop .15 1 .15 .10 .76

Type * Prop .82 1 .82 .53 .47

Size * Type * Prop .00 1 .00 .00 1.00

Error 360.27 232 1.55

Total 5722.00 240


In terms of relationship between “customers’ belief in good

service” and controlled treatments, only “type of container” was significant at p-value

less than 0.01. It means that only “type of container” impacts “customers’ belief in good

service” (See Table 4.11).

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Table 4.11: Three-Way ANOVA customers’ belief in good service and controlled

variables


good service

Type III Sum of

Squares df

Mean

Square F Sig.


Intercept 3912.34 1 3912.33 2583.23 .00

Size 1.83 1 1.83 1.21 .27

Type 11.70 1 11.70 7.72 .01

Prop .03 1 .03 .02 .87

Size * Type .10 1 .10 .06 .79

Size * Prop .10 1 .10 .07 .79

Type * Prop 1.50 1 1.50 .99 .32

Size * Type * Prop 4.00 1 4.00 2.64 .11

Error 351.36 232 1.51

Total 4283.00 240


t-test was conducted to test the relationship between

“customers’ perception on price of food” and controlled treatments which are “size of

container”, “type of container” and “prop in the photo”. Only “type of container” was

significant at p-value less than 0.01. Moreover, as shown in Appendix F fancy container

was perceived to be higher price at mean of 94.23 compared to plain container at mean

of 60.23. However, Appendix G and H “size of container” and “prop in the photo” did

not have significant relationship. Even though they were not significant, smaller

container was perceived to be higher price at mean of 78.74 compared to bigger

container at 75.72 (See Appendix G). Also with prop in the photo, the mean was a bit

higher than without pro in the photo at 78.73 compared to 75.73 (See Appendix H).

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CHAPTER 5

CONCLUSIONS AND RECOMMENDATIONS

5.1 Conclusion

As conclusion for the first objective which is to determine Thai customers’

perception on price, the researcher concluded no significant relationship between price

perception and customers’ demographic which are age, gender, monthly income and

education level. That means demographic of respondents does not impact price

perception. However, there is significant relationship between frequency of eat out per

month and price perception with positive Pearson Correlation. In other words, the more

people eat out, the higher price they perceive on food.

The second objective which is to determine Thai customers’ perception on

intent to visit the restaurants can be concluded that intention to visit the restaurant and

demographic, age and education level have significant relationship with negative

Pearson Correlation. It means the older and higher education, the less people want to

visit the restaurant. In terms of relationship between intention to visit and behavior, eat

out frequency has significant relationship with negative Pearson Correlation. In other

words, the more people eat out, the less they want to visit the restaurant.

The last objective which is to identify compositional elements of food

photos on Instagram that impact Thai customers’ perception on other restaurant features

as Halo effect can be concluded that the most important factor for independent variables

which impacts dependent variables is “type of container”. It has an effect on

“customers’ belief in quality of food”, “customers’ belief in healthiness of food” and

“customers’ belief in good service” at p-value less than 0.05, 0.01 and 0.01 respectively.

Among those beliefs, fancy container had higher mean than plain container. Moreover,

in terms of price perception, fancy container was perceived much higher price than

plain container. The second most important factor is “size of container”. It had

significant relationship with “customers’ belief in healthiness of food” at p-value less

than 0.05. Small container was perceived healthier than big container. In addition, in

terms of price perception, small container was perceived higher price. Finally, the least

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important factor is “prop in the photo”. It has no effect on dependent variables. In terms

of price perception, with food ingredients in the photo was perceived higher price.

5.2 Recommendations

5.2.1. Instagram should be a part of restaurants’ marketing

tool. In the digital era, customers’ eating behavior has changed. Instead

of thinking about the food at the point of purchase, customers now use social media to

help them make decision. As of now, Instagram users have been increasing dramatically

because of its user-friendly interface. People spend plenty of time on Instagram

browsing photos especially food photos. It was proved by recent research that when

people see food photo on Instagram, their ability of self-control reduced and that leads

to demanding for food however people will avoid the restaurant if they don’t like the

photo. As a result, Instagram can be one of powerful marketing tools for the restaurant

not only to create awareness but also to attract the customers and reflect the image of

the restaurant to match the restaurant price positioning.

5.2.2. Compositional elements of good food photos.

In order to attract the customer to the restaurant and price the meal

to match the restaurant positioning, restaurants should carefully select the

compositional elements of food photo. In addition, apart from the photo itself,

customers also evaluate the restaurant based on other aspects such as quality of food,

healthiness of food, cleanliness of restaurant and good service. Therefore, to have good

food photo, the following recommendations could be made.

a. Type of container

Restaurants should carefully select type of container as it impacts

“customers’ belief in quality of food”, “customers’ belief in healthiness of food” and

“customers’ belief in good service”. In this study, fancy container gained higher mean

than plain container in all beliefs. Moreover, fancy container is perceived higher price.

Therefore, fancy container can be used to add value to the meal and improve customers’

beliefs.

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b. Size of container

Size of container affects “customers’ belief in healthiness of

food”. For this aspect, small container gained higher mean than big container. Even

though it does not impact other beliefs, small container had higher mean than the big

container. The small container had lower mean only on “customers’ belief in

cleanliness of the food”. But the difference is only 0.03. It would not hurt the restaurant

for using small container. In terms of price, small container was perceived a bit higher

price than big container. Therefore, small container is recommended to increase price

and customer’s beliefs in the restaurants.

c. Prop in the photo

Restaurants should use food photo with food ingredients for the

following reasons. Although it does not have significant relationship with the

customers’ beliefs, photo with food ingredients has more positive effects. It gained

lower mean only on “customers’ belief in good service” but the difference in mean is

only 0.02. It would not hurt the restaurants if they use food ingredients in the photo.

Moreover, the photo with food ingredients was perceived slightly higher price than the

photo without food ingredients.

5.3 Limitation of the study

Despite well plan and study, this research faced a lot of limitations. The

reasons were stated below.

Firstly, because time and budget for this research were constraint, the

respondents were recruited based on personal connection. Therefore, the research

findings cannot be generalized to the entire population because the convenience

sampling method were used.

Secondly, sample size was limited with only 30 respondents per treatment

which might not fully represent target customers of the restaurants. Moreover, the ratio

of gender did not align with the population. In other words, 30 percent of the

respondents were male and 70 percent were female which could not generalize the

results of data and treat of data bias.

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Finally, only one type of Thai food was used in this study. The information

gained might not generalize the result to other types of food.

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REFERENCES

Boonperm, J., Wayuparb, S., Mutraden, A., & Tangpoolcharoen, J. (2016). Thailand

Internet User Profile 2015. Ministry of Information and Communication

Technology, Survey and Indices Section Office of Strategy. Electronic

Transactions Development Agency. Retrieved from

http://unctad.org/meetings/en/Contribution/dtl_eweek2016_ETDA_IUP_en.pd

f

Cherry, K. (2018, September 25). Retrieved from Verywell mind :

https://www.verywellmind.com/what-is-the-halo-effect-2795906

Christopher, M. (2018, August 3). Opengov Asia. Retrieved December 5, 2018, from

Opengov : https://www.opengovasia.com/social-media-boom-in-thailand-

leads-to-a-rise-in-social-commerce-in-thailand/

Fredrickson, T. (2017, May 24). Retrieved October 06, 2018, from

www.bangkokpost.com:

https://www.bangkokpost.com/learning/advanced/1255403/thailand-in-social-

media-worlds-top-10

Goldby, D. (2018, March 21). Retrieved December 4, 2018, from creativelive.:

https://www.creativelive.com/blog/photography-basics-composition/

Kawanishi, Y., Takahashi, K., Doman, K., Hirayama, T., Ide, I., Deguchi, D., &

Murase, H. (2016). A Study on Estimating the Attractiveness of Food

Photography. Nagoya University.

Kotler, P., & Keller, K. L. (2015). Marketing Management (15 ed.). Pearson

Education Limited.

Kressmann, J. (2017, February 13). eMarketer. Retrieved December 5, 2018, from

https://www.emarketer.com/Article/More-than-90-of-Internet-Users-Thailand-

Use-Smartphones-Go-Online/1015217

Ref. code: 25616002040811UKW

32

Leesa-nguansuk, S. (2018, February 6). Bangkokpost. Retrieved December 5, 2018,

from https://www.bangkokpost.com/business/news/1408158/thailand-tops-

internet-usage-charts

Lehmann, R. D., Kassarjian, H. H., & Beckwith, N. E. (1978). Advances in Consumer

Research.

Lux, K. (2011, December 15). What is Instagram and Why Is It So Popular? (the

Syracuse University iSchool) Retrieved November 22, 2018, from Infospace:

https://ischool.syr.edu/infospace/2011/12/15/what-is-instagram-and-why-is-it-

so-popular/

Menon, R. (2015, October 28). Cucumbertown. Retrieved December 6, 2018, from

Medium: https://medium.com/cucumbertown-magazine/the-beginners-guide-

to-composition-in-food-photography-how-to-transform-your-food-photos-

from-good-39613ab78bf2

Moreau, E. (2018, November 19). Social media: What Is Instagram, Anyway?

Retrieved December 5, 2018, from Lifewire: https://www.lifewire.com/what-

is-instagram-3486316

Olivera, K. (n.d.). Abasto. Retrieved 11 22, 2018, from

https://abasto.com/en/news/instagram-influence-food-industry/

Pant, R. (2015, January 16). business2community. Retrieved 10 07, 2018, from

https://www.business2community.com/digital-marketing/visual-marketing-

pictures-worth-60000-words-01126256

Petit, O. (2016). Can Food Porn Make Us Slim? How Brains of Consumers React to

Food in Digital Environments.

Piqueras-Fiszman, B., Schwartz, C., Giboreau, A., & Spence, C. (2012). Is it the plate

or is it the food? Assessing the influence of the color (black or white) and

shape of the plate on the perception of the food placed on it.

Ref. code: 25616002040811UKW

33

Sirikeratikul, S. (2018). Thailand Food Service - Hotel Restaurant Institutional 2017.

Bangkok: USDA Foreign Agricultural Service.

Spunky Trends. (2017, April 24). Retrieved December 2018, 5, from Spunky digital:

https://www.spunkydigital.com/instagram-matters-thailand/

Suchit, L.-n. (2018, February 6). Bangkok Post. Retrieved December 5, 2018, from

https://www.bangkokpost.com/business/news/1408158/thailand-tops-internet-

usage-charts

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APPENDICES

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Appendix A: Questions for in-depth interviews

Screening questions

1. What year were you born?

2. Do you live in Bangkok?

3. Do you have an Instagram account?

4. Do you access your Instagram account in the past three month?

5. Have you been to the restaurant because of food photo on the Instagram?

Questions

1. What was in the photo that attract you to the restaurant?

2. What compositional elements of Thai food photo on Instagram do you think is

attractive?

3. Can background of Thai food photo make it attractive? If yes, what kind of

background is that?

4. Can food container make Thai food photo attractive? If yes, what kind of

container is that?

5. Can vivid color make Thai food look attractive?

6. Can props make Thai food photo look attractive? If yes, what kind of props

are they?

7. For Thai food photo, apart from food, what else can help photo look

attractive?

8. What make Thai food photo on Instagram look expensive?

9. Can background of Thai food photo make it expensive? If yes, what kind of

background is that?

10. Can food container make Thai food photo expensive? If yes, what kind of

container is that

11. Can vivid color make Thai food look expensive?

12. Can props make Thai food look expensive? If yes, what kind of props are

they?

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Appendix B: Factor combination and treatment numbers

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Appendix C: Questions for field experiment

Screening questions

1. How old are you? (Single select)

- Less than 15 (End the survey)

- 15-24

- 25-34

- 35-44

- 45-54

- more than 54 (End the survey)

2.Do you have an Instagram account? (Single select)

- Yes

- No (End the survey)

3.Did you access your Instagram account in the past 3 month? (Single select)

- Yes

- No (End the survey)

Questions

1.How many times do you eat out per month? (Single select)

- Never (End the survey)

- 1-4 times

- 5-8 times

- More than 8 times

2. How many times do you access Instagram per week? (Single select)

- Less than once

- 1- 3 times

- 4-7 times

- 8-14 times

- More than 14 times

3. How long do you normally spend on an Instagram per time?

_________minutes

Show the picture (one of eight treatments)

See the picture carefully and answer the questions below

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4. Please score the features based on the picture above (Likert scale)

Features Poor

(1)

(2)

(3)

(4)

(5)

(6)

Excellent

(7)

Cleanliness

Healthy

Quality of food

Good service

5. Do you want to visit this restaurant? (Likert scale)

Absolutely not

visit this

restaurant

Absolutely visit

this restaurant

1 2 3 4 5 6 7

6.How much do you think food in the picture would cost?

________THB

7. How important are the following features to you when choosing a restaurant? (Likert

scale)

Features Not important

at all

(1)

(2)

(3)

(4)

(5)

(6)

Very

important

(7)

Cleanliness

Healthy

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Features Not important

at all

(1)

(2)

(3)

(4)

(5)

(6)

Very

important

(7)

Quality of food

Good service

8.Gender (Single select)

- Male

- Female

9.Education (Single select)

- Elementary school or lower

- Middle school

- High school

- Bachelor’s degree

- Master’s Degree or above

10.Monthly income (Single select)

- 15,000 or less

- 15,001-30,000

- 30,001-45,000

- 45,001-60,000

- More than 60,001

END OF QUESTIONNAIRE

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The pictures used in the survey (only one of these pictures will be shown

before question No.4. in the questionnaire)

Treatment 1.

Size of plate: Small

Type of container: Fancy

Props: With ingredients

Treatment 2.



Props: without ingredients

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Treatment 3.

Size of plate: Big


Props: with ingredients

Treatment 4.

Size of plate: Big



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Treatment 5.


Type of container: Plain


Treatment 6.




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Treatment 7.

Size of plate: Big



Treatment 8.

Size of plate: Big



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Appendix D: Respondents’ profile based on demographic (n=240)

Demographic

Frequency

Percentage

Gender

Male 72 30

Female 168 70

Age

15-24 31 12.92

25-34 168 70

35-44 38 15.83

45-54 3 1.25

Income per month

less than 15,000 33 13.75

15,001-30,000 47 19.58

30,001- 45,000 64 26.67

45,001-60,000 39 16.25

More than 60,001 57 23.75

Education level

Middle school or lower 6 2.5

High school 17 7.08

Bachelor’s degree 121 50.42

Master’s degree or

higher 96 40

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Appendix E: Respondents’ profile based on behavior (n=240)

Behavior

Frequency

Percentage

Frequency of eating out in

the restaurant per month

1-4 times 88 36.67

5-8 times 76 31.67

More than 8 times 76 31.67

Frequency of accessing

Instagram per week

Less than once 12 5

1-3 times 25 10.42

4-7 times 34 14.17

8-14 times 27 11.25

More than 14 times 142 59.17

Time spent on Instagram

per time

Light user

(1-10 minutes) 128 53.33

Medium user

(11-30 minutes) 97 40.42

Heavy user

(More than 30

minutes)

15 6.25

Ref. code: 25616002040811UKW

46

Appendix F: Perception on price and type of container

Type of

container N Mean

Std.

Deviation

Std.

Error

Mean

How much do you think

this food is?

Plain 120 60.23 21.04 1.92

Fancy 120 94.23 40.87 3.73

Price & Type F Sig. t df Sig.

(2-tailed)

Equal

variances

assumed

53.52 .000 -8.11 238 .000

Equal

variances not

assumed

-8.11 177.93 .000

Ref. code: 25616002040811UKW

47

Appendix G: Perception on price and size of container

Size of

container N Mean

Std.

Deviation

Std.

Error

Mean

How much do you think this

food is?

Small 120 78.74 35.19 3.21

Big 120 75.72 38.11 3.48

Price &

Size

F Sig. t df Sig. (2-

tailed)

Equal

variances

assumed

.00 .96 .64 238 .52

Equal

variances

not assumed

.64 236.50 .52

Ref. code: 25616002040811UKW

48

Appendix H: Perception on price and prop in the photo

Prop in

the photo N Mean

Std.

Deviation

Std.

Error

Mean

How much do you think this

food is?

No 120 75.73 33.47 3.06

Yes 120 78.73 39.63 3.62

Price & Prop F Sig. t df Sig. (2-

tailed)

Equal

variances

assumed

1.79 .18 -.63 238 .53

Equal

variances not

assumed

-.63 231.51 .53

Ref. code: 25616002040811UKW

49

BIOGRAPHY

Name Miss Thanyaphorn Tanthaphruekphon

Date of Birth May 27th ,1990

Educational Attainment 2012: Bachelor of Liberal Arts, majoring

Linguistics Thammasat University of Thailand

Ref. code: 25616002040811UKW

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