The effects of packaging attributes on Vietnamese consumers purchase intention

As a result, not only package elements design (i.e. color, graphic, font, shape, size), the informational elements (i.e. product information, nutritional information) also place a s[r]

VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY

š & ›

HO PHUONG HONG

THE EFFECTS OF PACKAGING ATTRIBUTES ON VIETNAMESE CONSUMERS PURCHASE INTENTION

MASTER’S THESIS BUSINESS ADMINISTRATION

VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY

š & ›

HO PHUONG HONG

THE EFFECTS OF PACKAGING ATTRIBUTES ON VIETNAMESE CONSUMERS PURCHASE INTENTION

MAJOR: BUSINESS ADMINISTRATION Code: 60340102

Research Supervisors Assoc Prof Kodo Yokozawa

Assoc Prof Pham Thi Lien

ACKNOWLEDGEMENT

First of all, I would like to I want to send my sincere thanks to my Vietnamese and Japanese professors, Associate Professor Pham Thi Lien and Associate Professor Kodo Yokozawa who constantly giving me advices and orientation during my thesis process

Secondly, I would like to warmly thank to Hanh sensei, Matsui Sensei, Morita Sensei and Hino Sensei for giving me many meaningful recommendations helping me to improve my thesis writing in our Research Proposal Reports (on 25th December,

2018)

Thirdly, I would like to thank to participants who took part in my online survey and gave me many valuable recommends to help me complete my questionnaire successfully

Additionally, I would like send a big thank to Huong san who always support and remind us all procedure we need to to complete your thesis smoothly

ABSTRACT

Purpose – This study aims to measure Vietnamese instant oats packaging and its

influences on Vietnamese consumer’s buying intention

Design/ methodology/ approach – A quantitative research was conducted using an

online survey to collect primary data for hypothesis testing The questionnaire was transferred successfully to 147 respondents

Findings – The findings indicated the positive relationship between packaging

elements (i.e graphic, structural and verbal attributes) and consumer purchase intention based on the literature reviews and data analysis discussed precisely in this research Additionally, the moderation role of involvement level to the interaction of visual elements and purchase intention was also proved in this paper

Research limitation/ implication – the main limitation of this study is excluding

other important factors impact purchase intention such as price, promotion, etc The findings provide knowledge related to consumer behavior for relevant companies to increase them to design an effective communication tool – package

Practical implication - The findings of this study can be used by managers and

marketers to create an effective packaging to ensure their products stand out among competitors

Originality/ value – This study is one of the few quantitative researches measure the

impacts of graphic, structural and verbal elements on purchase intention simultaneously Furthermore, it emphasizes the communication role graphic, structural and verbal attributes which has been ignore in previous studies Additionally, this study also indicated that involvement level positively strengthens the interaction between structural package design and purchase intention which did not quantitatively proved in previous studies

Keywords Consumer’s purchase decision, packaging design, graphic elements,

structural element, verbal elements, involvement level

Table of Contents

CHAPTER 1: INTRODUCTION

1.1 Research Motivation

1.2 Research Objectives

1.3 Research Scope

1.4 Research Structure

CHAPTER 2: LITERATURE REVIEW

2.1 Consumer Behavior

2.1.1 Customer behavior definition

2.1.2 Purchase decision making processes

2.2 Packaging 10

2.2.1 Package and packaging design 10

2.2.2 The role of packaging 11

2.2.3 Packaging Elements 13

2.2.4 Product involvement 13

2.3 Research Gap and Research Questions 14

2.4 Theoretical framework and research hypotheses 16

2.4.1 Variable definition 16

2.4.2 Measurement of Variables 18

2.4.3 Integration of literature review and hypothesis 19

CHAPTER 3: METHODOLYGY AND RESEARCH DESIGN 25

3.1 Research approach 25

3.2 Research design 26

3.3 Data selection 28

3.3.1 Secondary Data 28

3.3.2 Primary Data 28

3.3.2.1 Sampling design 28

3.3.2.2 Data Collection and participants characteristics 29

3.3.2.3 Questionnaire and Experimental Design 30

3.4 Data Analysis 33

3.4.1 Reliability Analysis 33

3.4.2 Continuous improvement cycle 33

3.4.3 Survey data analysis 34

3.4.3.1 Exploratory factor analysis (EFA) 34

3.4.3.2 Confirmatory factor analysis (CFA) 36

3.4.3.3 Structural equation modelling (SEM) 37

CHAPTER 4: DATA ANALYSIS RESULTS 40

4.1 Measurement Scale Test 40

4.1.1 Cronbach’s Alpha 40

4.1.2 Exploratory Factor Analysis (EFA) 41

4.1.3 Confirmatory Factor Analysis (CFA) 44

4.2 Research Model Test 47

4.2.1 Hypothesis testing without moderation of involvement level 47

4.2.2 Involvement Level effect testing by SEM 49

CHAPTER 5: CONCLUSION 52

5.1 Discussion of findings 52

5.2 Managerial implication 54

5.3 Practical implication 55

5.4 Limitation and further research 55

REFFERENCES 57

APPENDIXES 1: ONLINE SURVEY 63

APPENDIXES 2: Descriptive analysis 109

APPENDIXES 3: Cronbach’s Alpha 110

APPENDIXES 4: EFA 113

APPENDIXES 5: CFA 115

APPENDIXES 6: SEM 122

LIST OF TABLES

Table 1: Summarized literature review 14

Table 2: Measurement of variables 19

Z Table 1: Frequency of demographic information of respondents 30

Table 2: Study variable areas and corresponding section of the questionnaire 32

Table 3: Suggested procedure for improve measurement construct validity 34

Table 4: EFA requirement assumptions 35

Table 5: Model diagnostics in CFA 37

Table 6: Model fit indices 37

Z Table 1: Cronbach’s Alpha Results 40

Table 2: Removed variables 41

Table 3: Exploratory Factor Analysis Results 41

Table 4: Rotated components results 42

Table 5: The new latent variables 46

Table 6: Model fit indices 44

Table 7: Confirmatory Factor Analysis results 44

Table 8: Composite reliability and AVE results 45

Table 9: The model fit test of structural model 47

Table 10: Research model without moderator tested by SEM 47

Table 11: Hypotheses testing without moderator results 48

Table 12: Coding of variable computing 49

Table 13: Moderator effect model fit 50

LIST OF FIGURES

Figure 1: The factors impact customer purchase intention Figure 2: The proposed research framework 16 Z

Figure 1: The research process flow chart 27 Figure 2: Observed variables example 39 Z

LIST OF ABBREVIATIONS VS: Visual Elements

VB: Verbal Elements

SEM: Structural Equation Modeling

PI: Purchase intention

CHAPTER 1: INTRODUCTION

1.1 Research Motivation

consider “The effects of packaging attributes on Vietnamese consumers Instant Oats purchase intention” as personal thesis topic

1.2 Research Objectives

There exist very limited comprehensive practical studies have precisely analyzed packaging as a customer’s purchase communication tool in Vietnam from customer perspectives Hence, this paper aims to quantitatively analyze the impact of packaging design on consumer purchase intention then provide Vietnamese managers a better understanding about the importance of packaging in differentiating among competitors on the shelves Particularly, the study has the following sub-objectives: • To measure precisely the influences of packaging elements (i.e visual and verbal

elements) on buying intention under the moderation of involvement level • To identify which attributes should be concentrated while designing packaging

1.3 Research Scope

Due to the purpose of this research is focusing on the package influences only on purchase intention, to control the price influences and promotion impacts, author chose Vietnamese instant oat brands which have the same price range is 100.000 VND for a 400 grams package without any promotion at the moment There have three Vietnamese instant oats brands including Xuan An, Vinamit and Union were used to packaging assessment In addition, to determine the suitable targeted group, author chose participants who have already consumed Vietnamese instant oats before collecting their feedback about a certain product

1.4 Research Structure

Chapter 1: Introduction

This chapter provides an introduction about study motivation, research objectives, research scope and research structure

Chapter 2: Literature Review

This chapter provides the summary and reviews existing research related to literature of packaging design and customer behavior and their relationship under the influence of mediator “Involvement level” Based on literature reviews, author propose research conceptual framework, research question and research hypothesis

Chapter 3: Research Methodology

In this chapter, author discussed about research design, data collection process and data analysis methods were used to measure the conceptual framework proposed in chapter

Chapter 4: Analysis results

This chapter indicated research results was analyzed from data collection

Chapter 5: Conclusion

CHAPTER 2: LITERATURE REVIEW

2.1 Consumer Behavior

2.1.1 Customer behavior definition

According to AMA (American Marketing Association), customer behavior is defined as “The dynamic interaction of affect and cognition, behavior, and environmental events by which human beings conduct the exchange aspects of their lives" In the other words In other words, customer behavior is consideration, emotion and reaction of customer in the consumer process (Satish K Batra and S H H Kazmi, 2004) The behavior of customer is affected by many factors such as the opinions from family or friends, social media, advertising, product information, prices, packaging, product appearance can all affect the feelings, thoughts and behavior of customers

Research on consumer behavior is an important task that has a great influence in the decision-making process of marketing strategies (Philip Kotler, 2001, p 197-198) Previously, marketers could understand consumers through their exposure, transaction and daily sales experiences However, due to the growth of market size, marketing managers no longer have direct contact with their customers and the information from sales department is becoming subjective As a result, many managers started using consumer behavioral research to have an appropriate and accurate information in order to attract more customers According to Peter Drucker, who is considered the "father" of modern business management said: "The ultimate goal of all business activities is to create customers And only two business tools can this is marketing and creativity ”(Vneconomy)

2.1.2 Purchase decision making processes

Purchase decision making process is defined as the stage where the consumer actually purchase the product (Amstrong, 2012) The first purchase decision making processes, were introduced by Engel, Blackwell & Kollat, includes stages: “Problem recognition, Information search, Evaluation of alternatives, Purchase intention and Post-purchase behavior” These five stages are a good framework for assessing customer buying behavior However, customers not always go through these five stages, they may skip or reverse one For example, if a customer feels they need to buy chocolate to eat, they can go to a store immediately to buy a chocolate bar without searching any information or considering alternatives in advance Meanwhile, in case of car’s purchase decision making, customer will thoroughly research product information as well as comparing to others similar car brands before making final buying decision In conclusion, the purchase decision making processes is different among the amount of effort a consumer puts into a product while purchasing it

Problem recognition

At this stage, marketers need to identify what situations that often make consumers quickly recognize their problem They should study consumers behavior to find out what kinds of feelings have generated problems or needs, explain what makes them, and how they impact consumers to choose to buy a certain product

Information search

After recognizing needs stage, a consumer starts looking for information If the desire of buyers is strong enough and the products are available at that time, they tend to buy them immediately In contrast, if the desired products are not within the reach, the

If consumers' impulse is strong, and desired products are within reach, consumers will most likely buy immediately Otherwise, consumers will keep their needs in their subconscious Consumers may refuse to search for information, or find out basically products information, or actively seek information related to their needs In case they want to search for information, there are usually the following sources of information

• Family, friends, neighbors and acquaintances

• Commercial information collected through advertising, salesman, merchants, packaging or product displays

• Public information obtained from mass media and organizations

• Personal experience obtained through the interaction in daily life, survey or product usage

computer software products through commercial information sources, but discuss to programming experts about software products before final buying decision

Marketers need to understand the importance of the information source which is usually referred by their target customers to strategically format information sources As a result, in order to create a marketing content which is effectively communicates to target markets, marketers should interview consumers to collect their first opinion about products, brand image and what sources they received information and the how consumers react to the differences between each source

Evaluation of alternatives

Before making a purchase decision, buyers process the collected information and then evaluate others similar brand The evaluation process is usually done based on the following principles and sequences

Firstly, consumers consider a product with a set of attributes In particular, each attribute is assigned to a useful function that can bring satisfaction to consumers when they use it

Purchase intention

After the evaluation, the intention to purchase will be formed by the brand that received the highest rating and becoming to the purchase decision In addition, according to Aizen (1991), “intention represent moticational components of a behavior, that is, the degree of conscious effort that a person will exert to perform a behavior” There are two factors that can be intervened before consumers make the purchase intention as follows (Philip Kotler & Kevin Keller, 2011, Marketing management, p.170):

(Sources: Phillip Kotler & Kevin Keller, Marketing management, p.170)

Figure 1: The factors impact customer purchase intention

packaging draws attention primarily through color and shape (Khan, Lee and Lockshin, 2017); The appearance of packaging may also affect the evaluation of core products (Kumar and Kapoor, 2017); and product packaging also affects consumers' perceived quality and their purchase intentions (Rundh, 2016)

Post-purchase behavior

After purchasing, consumers can feel satisfied or dissatisfied with some products aspects and then take actions after buying or reacting to certain products or the using process If the characteristics and functions of the product meet their expectations, consumers will be likely to buy products again and introduce it to others On the contrary, dissatisfied customer tend to return or say bad thing about the products with others and switch to products of competitors

According to an overview of consumer behaviors, with the affecting of others factors, such as price, promotion, distributions, cultural context, etc, packaging place a main role in communicating to customer in the store (Gonzalez et al, 2007) Obviously, among five stage of purchase decision making process, packaging has significant influence on buyer purchase intention which is the fourth stage of the process

2.2 Packaging

2.2.1 Package and packaging design

Products package is known as a portion of the product itself as well as brand recognition Packaging has an important role in presenting products features and providing product information to customers From buyers’ perspectives, both package and products are the same on the shelves During the purchase decision making process, customer uses package as a supportive tool in evaluating products quality and its functions to make a right choice

have a significant role in product perception Meanwhile, verbal package design elements importantly detailed product information At the point of purchase, the fundamental role of package and packaging design is to make buyers’ attention and to be highlighted among compertitors in the store

Effective packaging and package design are the outcome of the collaboration between designers, marketers and customers Thus, packaging design is one of the marketing strategy tools for products itselfs According to Prone (1993), customer’s impulse buying is made by packaging which boosts products function, connects to company’s image and helps company brand stands out among competitors (Gaber & Jones, 2000) Hence, in the buying decision making process, packaging has an significant role in communicating with customers by providing goods-related information

Besides, packaging is also defined as product positioning which creates the company brand in the customers mind and emphasized the added value that differentiate products from alternatives Maggard (2976) claimed that product positioning induces marketing mix where the portions including pricing policy, place, products and promotion are involved” Considering positioning elements and competitors capability helps marketers to conduct an appropriate marketing strategy (Ampuero & Vila, 2006) The differences of the relevant element’s category depend on positioning strategy’s aim i.e globalization or localization As the result, packaging performs in different functions to reach the different goals, however, stimulating customer purchasing a particular products is the main role of positioning Hence, while marketers use positioning to place products in the market, packaging and package design are used as assistant of company to attract customers’s attention

2.2.2 The role of packaging

function but also psychological function i.e packaging communicate to customer Accrording to Bill Stewart (2004), there has three main functions of package is mentioned as below

To contain: Packaging acts in covering role which ensure the visual features,

original functions and the quality of product during the lead time A package, which has a good containing function, enhance the trust of customer about products

To protect: Packaging acts a protection part to keep actual products against

external effects including temperature, moisture, light, etc In this term, designers choose package material based on the characteristics of the goods, the transit process and the environmental risks that it will faced with Accordingly, if package performs this function well, the shelf life and the freshness of goods will be extended

To Identify: Packaging plays an important role in reminding customer about

the available of goods and providing products information to buyers Customers can easily access product information e.g ingredient, country of origin, production and expiry date, etc In addition, this function can stimulate customers actual purchasing and assist product standing out among alternatives

Marketing tool

Product design is described as an decisive tool to create marketing strategy for consumer goods (Rundh, 2009) To enhance a competitive marketing strategy, along with SWOT analyzing, the product design should involve the references from customers (Creusen et al, 2010) Packaging is an effective communication method helping marketers to show their product information and message to consumers (Silayoi & Speece, 2007)

packaging assist product in differentiating consumer good from alternative products (Holmes et al, 2012)

Additionally, as an element of product design, packaging design has a significant communitive role and a strong impact on consumer purchase intention (Salem, 2017), thus, packaging is an important instrument in marketing strategy

2.2.3 Packaging Elements

Based on previous related literature (Sonsino, 1990; Hine, 1995; Vila & Ampuero, 2007; Underwood, 2003), visual package design consists of two major attributes, such as graphic and structural elements Graphic component includes color, typographic, shape, image while the structural component consists of size and material used to cover actual products (Hine, 1995) Additionally, Silayoi and Speece (2004, 2007) added verbal component involving factors related to information or words such as brand name, product information, language used on package (Salem, 2017) In some aspects, three of them have influences customers purchase intention

2.2.4 Product involvement

2.3 Research Gap and Research Questions

Table 1: Summarized literature review

Author Review of article Research Gap

Underwood (2001) The theoretical framework was conducted to understand the communicative effects of package image on brand attention According to the virtual reality simulation results, package image positively influences consumer's brand attention in private label brand

These qualitative papers mainly aim to explore the initial understanding of the relationship between packaging and purchase intention with the considering involvement level as its moderator However, they did not have any precise measurement the interaction among them Additionally, most of these papers just focused on the impact of visual element only, did not mentioned much Underwood (2003) The exploratory qualitative study confirmed the role of packaging in enhancing the

relationship between consumer and brand in low involvement products

Silayoi (2004)

This is a qualitative approach research adopting a focus group method to understand consumer response to product packaging and packaging design influences on the consumer purchase decision The findings are that customers decision mostly affected by visual elements when they considered low involvement products under high time pressure

Ampuero (2007)

This paper study relationship between packaging graphic elements and positioning strategies from related previous study and found that the existing literatures mainly concentrated on the impacts of visual elements including color, shape, typography and image which is determined by corresponding positioning strategy

Butkeviciene et al

(2008) An empirical research found that non-verbal components enhances consumer impulsive purchasing while verbal components did not impact on repeated purchasing. Mutsikiwa (2014) This paper aims to evaluate the influences of aesthetics package design on buyer's purchase decision in daily products consuming The analysis focused on package color,

Ford et al (2015)

Collected information from in-depth interviews and observation of 11 older participants (in range 58 - 85 years old)'s behavior with fast-moving consumer products packaging The results indicated that customers aging has positive relationship with their perceived risk in packaging interaction

about the role of verbal elements

Sarker (2015)

A qualitative study examines the influences of package design and naming strategies on perceived quality and purchase intentions The results show that while packaging positively effects on perceived quality and purchase decision, the naming strategy did not have any significant impacts

These quantitative had objectively analyze the impacts of package attributes on purchase intention but there has no positive direct relationships among them

Muhammad (2014) This study used quantitative analysis to test the influence of verbal elements (i.e nutritional information, product information, country-of-origin) on consumer buying behavior The findings revealed that product information has negative impact

Imiru (2017) The paper used correlation and regression to analysis the relationship between packaging attributes on consumer purchase decision As the result, there has no relationship between package color and material

To fill these research gap, this study aims to investigate the positive relationship between packaging elements and purchase intention by adding involvement level as moderator of their interactions According to Quester and Smart (1998), involvement level strongly influences consumer buying decision making processes Additionally, in case of low involvement level, consumer normally affected by visual packaging design while buyers pay more intention to the product itself in case of high involvement (Grossman and Wiseblit, 1999) In general, this paper purpose to precisely measure the relationship between packaging design and purchase intention under moderated by involvement level with following research questions

1 Is there a positive relationship between packaging design and consumer purchase intention?

2.4 Theoretical framework and research hypotheses

Figure 2: The proposed research framework 2.4.1 Variable definition

2.4.1.1 Packaging Elements

There have many different definitions about the packaging elements In the related research of Smith and Taylor (2004), they divided packaging design into six parts including “color, size, form, materials, graphic and flavor” Besides, Kotler (2003) claims that product package involves similar six elements, such as “color, size, form, material, text and brand” These elements should be considered in package design process (Vila and Ampuero, 2007) Differently, Underwood (2003) separate packaging into two main attributes i.e structural attributes (“form, size of the containers and materials”) and graphic attributes (“color, shape, typography and image”) Obviously, similar to Underwood (2003), Smith & Taylor (2004) not mention about verbal elements of packaging which has decisive influences on actual purchase of high-educated consumer (Mutsikiwa et al, 2013) To fill this gap, Rettie and Brew (2000) mentioned about positioning function of packaging, thus, they separated packaging to main groups: visual (e.g color, image, shape, font, size) and verbal (e.g brand name, slogans) elements Additionally, according to Silayoi and Speece (2004), packaging has two major elements: visual and informational elements Visual elements, includes graphics, size and shape of packaging, influence the affective aspect of purchase decision making of consumer Meanwhile, informational elements, which impact cognitive aspects, consists of product information (e.g the name of the firm, address, country of origin, production and expiry date)

Though both Rettie & Brew (2000) and Silayoi & Speece (2004) considered the importance of informational elements, they did not mention about the environmental effects of package materials which significant influence on customer’s food purchase intention (Rundh, 2005) due to the growth of environmental concerned consumers Not only that, they also did not indicate the role of printed language which may affect on the willingness to purchase of buyers (Salem, 2017)

color, shape, font, picture), structural element (e.g size, material) and verbal elements (e.g brand name, product information, language)

Graphic elements: Graphics elements, are factors can be seen, comprised of

color combination, shape, background image and the font style used on packaging (Hine, 1995)

Structural elements: structural elements, are factors simultaneously designed

to display and protect the products effectively, consist of product size design and material should be used

Verbal elements: Verbal elements, are factors related to information or words,

include brand name, the information of product and the language used on packaging In the decision-making process, the verbal attributes influence the cognitive

2.4.1.2 Product involvement

Product involvement, is the level of interest or effort that consumers put in purchasing a certain product, divided into two type including low and high involvement High involvement products, are perceived as having high value with high cost and provide long term benefits and buyers tend to carefully evaluation before purchasing it Meanwhile, low involvement products have cheaper cost, thus, buyers not need much time to deeply research or consider before chasing its

2.4.1.3 Purchase Intention

According to Sproles & Kendall (1986), purchase intention is a “mental orientation characterizing a customer’s approach to making choice” Purchase intention associates with cognitive and affective process in decision making process Additionally, consumer purchase intention is likely influenced by three main packaging design components including graphic, structural and verbal elements

2.4.2 Measurement of Variables

Table 2: Measurement of variables

Variable No of questions Measurement sources Graphic

elements

Color Olawepo (2015) Shape Olawepo (2015) Font Olawepo (2015) Picture Olawepo (2015)

Structural elements

Size Salem (2017) Material Salem (2017)

Verbal elements

Brand name Salem (2017) Product information Salem (2017) Language Salem (2017)

Involvement level Mittal (1989)

Purchase intention Weisstein (2017) Pei (2014) and

2.4.3 Integration of literature review and hypothesis 2.4.3.1 Graphic package elements and purchase intention Color

Color has significant influence on consumer emotion (Salem, 2017), thus, color selection process is very importance to design an attractive package (Cheskin, 1957) Package color assists product differentiate from other brand and enhance consumer’s long-lasting memories about products Color is an effective design tool without cost, product attributes and function adjustments (Garber et al, 2000)

According to Steward (2004), each packaging color of a particular product has a transferred message to consumers Especially, for food products, package color has strong impact on customers perception about the food taste (Kauppinen, 2010; Koch&Koch, 2003; Gaber et al, 2000)

Therefore, to make the right choice of color, marketers should fully understand the meanings of each colors in different cultural context (Salem, 2017)

Shape

Interestingly, many customers confirm that they have potential to purchase a certain product without reading the label or product information (Salem, 2017) Previous marketing literature proved that the shape of package associating with message affect consumer feeling and perceived quality (Abdelsamie et al, 2013; Ruumpol, 2014) For instance, while male is impressed by linear angular shapes, female prefer curving line and round shape (Shimp, 1990) When considering two products with the same weight, buyers tend to choose the product which has taller shape because in buyer’s mindset, the higher has larger volume (Silayoi et al, 2007) Additionally, an innovative package helps products enhance the attractiveness and stand out among similar brands (Salem, 2017) Unique packaging is a competitive tool used for differentiating and consequently increase products sales volume (Sherwood, 1999)

Background picture

According to Salem (2017), pictures and graphics affect consumer sensory the pictures, are printed on product packaging, describes the information related to the goods, such as products usage instructions and its functions where consumer can generally imagine what the product is (Pensasitorn, 2015) Hence, pictures on package places an important role in communicating to customer through transferring products information and imagined stimuli about products (Salem, 2017) In the other words, packaging image is an effective instrument to convey the functions of product and assist goods to be different from alternatives (Meyers and Lubliner, 1998)

Font Style

have an innovative font style, many companies hire experts to design a creative and attractive font style used for their logos, slogans and product package (Imiru, 2017) Therefore, font style, is a powerful tool to draw consumers attention, has positive impact on consumers purchase decision (Imiru, 2017)

In general, the graphic elements have significant impacts because they have a capability to influence the emotion and feeling of targeted customers (Silayoi and Speece, 2004) Thus, graphic elements positively impact on buyer’s purchase intention perceived quality (Saker, 2015) Based on discussion above, the first hypothesis was established as below:

H1: Graphic packaging elements positively influence consumer’s instance oats purchase intention

2.4.3.2 Structural package elements and purchase intention Size

As package shape, buyers consider package sizes to make volume perception Hence, the size design should meet consumer’s demand (Makanjuola and Enujiugha, 2015) According to Benedetti et al (2014), marketers should understand target’s customers behavior before making product sizes decision Additionally, package sizes strongly influence on consumer buying choice when buyers cannot clearly evaluate product quality, thus, they are potential to buying smaller one for trial usage (Ksenia, 2013) Meanwhile, some buyers prefer the large size of products for saving cost Therefore, to meet different type of consumer demands, it would be better if goods are sold in various packaging size (Rundh, 2005)

Material

products (Silayoi & Speece, 2004) In addition, many today consumers more concern about environmental issues (Rundh, 2005) Accordingly, buyers potentially choose products which have environment friendly, recycle and ease-reuse packaging (Rundh, 2005)

Based on previous literature review, to understand more the influences of structural elements will be thoroughly analyzed in the following section; thus, the second hypothesis was conducted as following:

H2: Structural packaging elements positively influence consumer’s instance oats purchase intention

2.4.3.3 Verbal package elements and purchase intention Product information

Packaging places a major role in conveying the information related to products which enhance the cognitive process of consumers (Salem, 2017) In some country, there exist the regulations about packaging printed content, such as the name of the firm, address, country of origin, production and expiry date (Ahmed et al, 2014) Due to the increase of healthy eating trend, customer concern more about food ingredients and nutrition (Coulson, 2000), thus, they more carefully consider product information available on package The package has appropriate products information assist enhance consumers’ reliability and boost purchase decision making process (Silayoi and Speece, 2004) In the store, package maybe the only instruments helping product communicate with buyers (Gonzalez et al, 2007) Accordingly, packaging is considered as the fundamental elements in consumer purchase intention

Brand elements

elements of product information (Silayoi and Speece, 2004) Brand identification helps buyers to reduce searching time when they purchase food products (Bassin, 1988) With other information-related-to products, brand elements visibly evoke consumers interests and boost their purchase decision process (Mutsikiwa et al, 2013)

Language

De Run and Fah (2006) indicated that using mother language on package assists buyer easily understand products functionalities and usage instructions, thus, buyers are likelihood to purchase products has printed information in mother language Hence, to globalize effectively, marketers should eliminate the language barrier which is one of the major of cultural barriers (Hall & Hall, 1987) Additionally, under cultural context, the ways to describe a sentences or words are different among local languages (Doole & Lowe, 1999) Local people will have a positive sense that the certain foreign firm is seriously doing business in targeted country if their language is used on package (Hollensen, 1998) Adopting a suitable language for information contents or packages is a very necessary mission to increase consumer purchase intention (Salem, 2017)

Whilst visual elements (e.g color, shape, size, font, picture, material) evoke buyer’s emotion and feeling, verbal elements (e.g products information, brand elements, language) significant impact on the cognitive process of customer (Silayoi & Speece, 2004) As a result, verbal elements also have impacts on buyer’s purchase intention (Salem, 2017) This led to the establishment of the third hypothesis:

H3: Verbal packaging elements positively influence consumer’s instance oats purchase intention

2.4.3.4 The effect of involvement level

process shows its limitation Recognizing that limitation, based on Kotler’s literature review, Patty & Cacioppe (1981) proposed two accessing contexts for high and low involvement products With high involvement products, buyers are likelihood to carefully search the information related to products, which follows the Kotler’s purchase decision making process (Patty and Cacioppe, 1981) For low involvement level, buyers easily are drawn by packaging design (Solomon, 2002; Silayoi & Speece, 2004)

On one hand, according to Silayoi and Speece (2004), packaging assist low involvement products evoking customer’s emotional action The expected outcome of purchase decision and the element evaluation of low involvement products are less important, thus, the role of packaging graphic and structural elements become more critical (Grossman & Wisenblit, 1999) Thus, it is possible the level of involvement has influenced the interaction between graphic or structural package elements and purchase intention, based on it, the fourth and fifth hypothesis were established as below:

H4: Involvement level is the moderator of the relationship between graphic package elements and purchase intention

H5: Involvement level is the moderator of the relationship between structural package elements and purchase intention

On the other hand, buyers not care much about visual aspects when they are considering to purchase high involvement products (Kupiec & revell, 2001) Accordingly, they pay more attention with package informational elements (Silayoi & Speece, 2004) Obviously, level of involvement has impacted the relationship between verbal package elements and purchase intention, based on it, authors hypothesize the following:

CHAPTER 3: METHODOLYGY AND RESEARCH DESIGN

Research methodology is the detailed procedures used in identifying, selecting, processing and analyzing data for answering research questions Research methodology is the fundamental chapter of research writing This chapter describe specifically the study processes to address research issues and examine research hypotheses For instance, in this chapter, author discussed the methods related to research methodology determination as well as collecting data and analyzing instruments to conduct this research

3.1 Research approach

3.2 Research design

According to Souna (2007), research design is defined as “the framework or guide used for the planning, implementation, and analysis of a study” The different of research questions or hypotheses lead to the different of research design, thus, understanding and distinguishing exactly the types of research design is an important mission while conducting a study Based on the researcher’s variables controlling level, quantitative research design is divided into main types: descriptive, correlational, causal-comparative and experimental design

Causal-comparative design is adopted to create the cause-effect relationship among variables In this research design, independent variables are considered as causal factors while dependent variables are affected factors Causal-comparative design is a popular design used in social science to analyze human behavior through assessing the cause-effect relationship among groups

Figure 1: The research process flow chart L ite tur e R ev ie w

Research problem definition - Question

- Objectives - Hypotheses

Theory exploration - Theory framework - Model building

Sampling (survey)

Questionnaires developing

Pilots study

Refinement questionnaire

Data collection

Selection of basic research methods: Questionnaires/ survey

Editing/ coding data

Quantitative analysis - EFA - CFA - SEM

Interpretation of results and findings

3.3 Data selection

Research data has been collected from both secondary and primary sources According to Saunders (2009), secondary data is the information collected from existing sources including company reports, academic journals, scientific articles and media information Otherwise, primary data is gathered by researchers themselves to answers their research questions (Hox & Boeije, 2005) Saunders (2009) claimed that the reliability of data collection is very important to have a valuable result

3.3.1 Secondary Data

Secondary data has been gathered mainly from academic journals and scientific articles The secondary is used as references for gathering primary data and research questions Author purpose to find the suitable secondary data related to packaging design and its impacts on consumer purchase intentions In addition, to solve research problem, author used key words such as packaging elements, purchase decision and consumer involvement level to search expected secondary data The used database is belonging to Yokohama National University, Vietnam National University and Google Scholar

3.3.2 Primary Data

According to Churchill and Lacobucci (2010), researchers should consult the secondary research first then conduct the primary data to achieve a general knowledge about research topic The primary data was collected according to specific research purpose and research question (Mark Saunder, 2009) To gather primary data, this study used online survey built by google forms

3.3.2.1 Sampling design

each of them consists of more than items with high item communalities (> 0.6); 150 in case of model with at most constructs have modest communalities (0.5); 300 in case of models involves at most constructs with low item communalities (0.45) and over 500 for models consists a large number of constructs with low item communalities and under measurements items Generally, 100 is the practical and acceptable size for SEM (Hair et al., 2010)

Furthermore, for SEM in using AMOS, Pallant (2005) claimed that the sample size should be “at least five times the number of question items Accordingly, the proposal model involves independent variables, moderate variable, dependent variable containing 30 question items Specifically, 10 for “Graphic elements” variable, for “Structural elements”, for “Verbal elements”, for “Involvement level” and for “Purchase intention” Hence, there has 30 items in total (30 x = 150) which mean that 150 is the minimum acceptable sample size Therefore, this study considered 150 for minimum sample size estimation

3.3.2.2 Data Collection and participants characteristics

Due to the conveniences and objectivity, survey became a popular tool to gather information from respondents Especially, along with the spreading of internet, online survey tends to be the more cost-effective than traditional method such as paper surveys or face-to-face interviews Online survey assists researchers reduce the geographical dependence and connect to more hard-to-reach respondents in less developing time and money

Therefore, data of this study was collected by online surveys from March 29th to

April 20th, 2019 The criteria for participating in this study was that the respondents

Table 1: Frequency of demographic information of respondents Items Number of

respondents Percentages

Total 147 100

1 Gender

- Male 45 30.6 - Female 102 69.4

2 Age

- Under 18 - 18 – 30 101 68.7 - 30 – 50 40 27.2

- Over 50

3 Income

- Under million/ month 18 12 - – million/ month 37 24.7 - Over million/ month 92 61.3

4 Living City

- Hanoi 88 58.7

- Hue 20 13.3

- Danang 3.3

- Ho Chi Minh 23 15.3 - Others 11 7.3

3.3.2.3 Questionnaire and Experimental Design

the reliability of questionnaire, research used Cronbach’s alpha test As a result, the Cronbach’s alpha coefficients each variable was greater than 0.7 meaning that the questionnaire is significant and high reliable coefficients Therefore, the questionnaire was adjusted to avoid the probabilities of misinterpretation, distraction and partiality through ensure the design of questionnaire assists to collect the expected responses from participants The final questionnaire is shown at appendixes section

Table 2: Study variable areas and corresponding section of the questionnaire

No Variables Items Sources

1

Graphic Packaging

Design

The color combination on the packaging draws my attention

Olawepo (2015)

2 The color combination can easily be remembered

3 The color combination makes product stands out among another brand

4 The shape of packaging is unique compared to another brand

5 The shape of packaging is comfortable to use

6 The Font used on the product is legible and can be understood

7 The Font used in writing Ingredient composition is legible and could be interpreted

8 The Font used on the product attracts attention from distance

9 The picture quality of the product packaging draws my attention

10 The picture of the product packaging reflects the fact that it is healthy

11 Structural

Packaging Design

The size of packaging meets my demand

Salem (2017)

12 Packaging material is made from recycle materials

13 Packaging material has high quality

14 Packaging material is environmentally friendly

15

Verbal Packaging

Design

Brand name on packaging draws my attention

Salem (2017)

16 Brand name on packaging is unique compared to another brand

17 Brand name on packaging is easy to remember

18 Product information on packaging is described clearly

19 Product information on packaging effects trust for the product

20 Storage information on packaging is easy to follow

21 I react more favorably to product packaging imprinted in Vietnamese

22 Product information on packaging (such as: the name of the firm, address, country of origin, production and expiry date) is clearly printed 23

Involvement Level

In selecting from the many types and brands of Instant Oats available in the market, I will care a great deal as to which one I buy

Mittal (1989) 24 I think that the various types and brands of Instant Oats available in the market are all very different

25 To me, making a right choice of instant oats is very important

26 In making my selection of Instant Oats, I concern about the outcome of my choice

27

Purchase Intention

I would be willing to buy Instant Oats of this brand

Weisstein (2017) & Pei (2014) 28 If I were going to buy Instant Oats, the probability of this brand is high

29 The probability that I would consider buying the instant oats of this brand is high

3.4 Data Analysis

3.4.1 Reliability Analysis

The Cronbach’s alpha value is a popular tool to purify research measurements Nunnally and Bemstein (1994) suggested that the Cronbach’s Alpha value of each variable should be greater than 70 threshold To gain the possible highest reliability coefficient, the variables are purified by deleting items which have the lowest item-to-total correlation or items which have the value “Cronbach’s alpha if item deleted” is greater than total Cronbach’s Alpha value In this study, there has variables with 30 items were measured in this section When the Cronbach’s Alpha coefficient reaches to expected value, the analysis goes to the continuous improvement cycle stage

3.4.2 Continuous improvement cycle

Table 3: Suggested procedure for improve measurement construct validity Test Procedure

Internal

consistency Cronbach’s Alpha Construct validity

(EFA approach)

Unidimensional: Factor loadings

Convergent Validity: Eigen value, Variance Extracted-VE, Reliability

Construct validity (CFA approach)

Convergent Validity: t-values, squared correlations Fits and unidimensional assessment: Fits and indices Discriminant Validity: constrained model pairs; Variance Extracted versus squared correlation between factors Composite Reliability; Variance Extracted

3.4.3 Survey data analysis

The research data was analyzed through statistical techniques including descriptive statistics, quantitative data analysis (e.g EFA, CFA, SEM, etc) This study adopted CFA to validate construct measurements while structural equation modeling (SEM) for assess research hypotheses

3.4.3.1 Exploratory factor analysis (EFA)

Exploratory factor analysis (EFA) aim to explore the underlying structure of a certain set of variables In the other words, EFA clarified the pattern of correlations among variables by discovering underlying factors According to Gorsuch (1983), EFA is adopted to following below reasons:

• To narrow down the large quantity of items to smaller one for modelling purposes where larger group of items may interrupt modelling process of all measurements individually

• To select a subgroup of factors from larger group by identifying highest correlations with the principal component factors

• To identify uncorrelated items to avoid multicollinearity while adopting multiple regression

EFA includes three fundamental stages: “(1) assessment of suitability of data for factor analysis, (2) factor extractions and (3) factor rotation” Hence, preliminary analysis should involve some assumptions (indicated in table 3.3) to tested data suitability before conducting EFA To check the linear relationship between variables, Hair et al (2010) recommend the usage of Plotted-Point (P-P plots) corresponding with the ideal line for linearity to exist In addition, multicollinearity occurs when among independent variable has high intercorrelation level and leads to unreliable probability values (P-value) and larger confidence intervals of independent variables The value of variance inflating factor (VIF) is used to identify multicollinearity occurrences (VFI > 10)

Table 4: EFA requirement assumptions

Condition Requirement References

Outliers No outliers accepted (Hair et al., 2010) Linearity No multicollinearity (Hair et al., 2010) Normality Should be Normally distributed (Hair et al., 2010)

Sample size Minimum: cases to each study items (Pallant, 2005; Tabachnick&Fidell, 2007) Bartlett’s test

of sphericity Be significant (p < 0.5) (Tabachnick&Fidell, 2007)

Kaiser-Meyer-Olkin (KMO)

Index ≥ 0.5

(Hair et al., 2010; Malhotra, 2007)

The Kaiser-Myer-Olkin (KMO) is used to measure the adequacy of sampling The KMO, is considered as the best method for determining the acceptability of data for subsequent factor analysis Tabachnick & Fidell (2007) indicated that if the KMO values is too small or accounts in the range to 1.0, the factor analysis should not be operated The KMO need to be 0.6 or higher to run factor analysis

Factor extraction is the important analysis way of EFA Researchers use factor extraction to identify what factor can summarize the interrelationship among variables Hair et al (2010) indicated that tactor extraction consists of methods which are “principal components analysis (PCA), unweighted least squares, generalized least squares, maximum likelihood, principal axis factoring, alpha factoring, and image factoring” This study used principle component analysis which is the most popular used in EFA Tabachnick & Fidell (2007) claimed that PCA is the good choice for researchers who are interested in empirical summary rather than theoretical solution

Meanwhile, Hair et al (2010) indicated that the items which have factor loading greater than 0.50 are valuable for further analysis To select which factors remained in scale, Hair et al (2010) also suggests that researchers should keep factors have eigenvalue greater than 1.0 to use in further examination When the quantity of retained factors were addressed, researcher aim to determine the pattern of loading for interpretation by rotation There exist direction for rotation including orthogonal (varimax) and oblique rotation This study adopted varimax rotation which purpose to “simplify factors by maximizing the variance of the loadings within factors, across variables” (Tabachnick & Fidell, 2007)

3.4.3.2 Confirmatory factor analysis (CFA)

Table 5: Model diagnostics in CFA

Model Diagnostic Requirement References

Modification Index (MI) ≥

(Hair et al., 2010) Standardized Residual < 2.5 no problem

> possible problem Path Estimate (Construct

to Indicator) ≥ ; ideally ≥ 0.7; and be significant Square Multiple

correlations (SMC) or reliability

≥ 0.3

Table 6: Model fit indices

Fit Indexes Acceptable level

Chi-square Value with non-significant p-value Normed Chi-square (CMIN/df) ≤

Goodness-of-fit Index (GFI) < GFI ≤ Adjusted Goodness-of-fit Index (AGFI) < AGFI ≤ Tucker-Lewis Index (TLI) < TLI ≤ Comparative Fit Index (CFI) < CFI ≤ Root Mean Square of Error of Estimation

(RMSEA) < RMSEA ≤

3.4.3.3 Structural equation modelling (SEM)

Author used structural equation modelling (SEM) to describe specifically the research model and analyze the relationship between independent and dependent variables as well as interaction effect of moderator addressed in this study

• Identify the coeficient values in the linear model framework • Test the model acceptability for representing the studied processes

• According to model acceptability testing, conclude that the reliability of variables relationship

Particularly, SEM assist researches conduct the hypothesis models of marker behavior and assess research model statistically Furthermore, SEM predicts the unknown coefficients in a group of linear structural equation system which normally involves observed variables and related latent variables (unobserved variables) In addition, during analysis process, SEM assumes there exists a causal-relation between a certain number of observed variables and latent variables and observed variables play as indicators role

Latent Variables

Latent variable defined as unobserved or unmeasured variable, which has theoretical constructs and can be directly measurable, normally be referred as “factors” or “common factors” which are assumed than can be observed when they have the significant influence on the outcome resulted by observed variables Additionally, latent variables can directly affect other latent

Observed Variables

Figure 2: Observed variables example

In the figure 3.2, the linking between latent and observed variables was connected by the single-headed arrow indicating the influences of latent variables on the outcome from observed variables under a regression relationship

Structural equation modelling does not only include latent and observed variables but also has the “residual” and “error” elements, attached to each variable, are the key elements of the general model In conclusion, SEM is “a complex interplay between a large number of observed and latent variables with residual and error terms” (Maclean, 1998)

3.4.3.4 Hypothesis testing by SEM

According to Kaplan (2001), there has two stage in SEM analysis including structural part and measurement part

Structural stage: connecting the constructs of each variables This stage shows

the dependent constructs as linear functions of the independent constructs (Kaplan, 2001)

Measurement stage: connecting the constructs of observed measurements

This stage is similar to CFA model and uses the same cut-off points value and P-value (> 0.05) to assess the significant relationship among variables (Kaplan, 2001)

CHAPTER 4: DATA ANALYSIS RESULTS

4.1 Measurement Scale Test 4.1.1 Cronbach’s Alpha

Cronbach’s Alpha coefficients are coefficients in statistical tests that are used to check the correlation between observed variables, in order to analyze the scale reliability assessment To purify measurement, this method allows analyst to remove unsuitable variables and unreliable variables in the research measurement The variables which have corrected items-total correlation values greater than 0.3 and Cronbach's Alpha coefficient greater than 0.6 are acceptable and appropriately used in the next stage (Nunnally and Burnstein, 1994) Many researchers agree that when Cronbach’s Alpha is between 0.7 and 1, the scale of measurement is good and the correlation will be higher

Table 1: Cronbach’s Alpha Results No Variables Number

of Items

Cronbach’s Alpha Coefficient

1 Graphic Element 10 0.718 Structural Element 0.924 Verbal Element 0.837 Involvement Level 0.795 Purchase Intention 0.964

(Data analysis by SPSS 20)

Table 2: Removed variables Variables Deleted items

Verbal Elements

VB2: “Brand name on packaging is unique compared to another brand”

VB3: “Brand name on packaging is easy to remember”

Overall, after eliminating item, the satisfied variables with 28 items were used for exploratory factor analysis step

4.1.2 Exploratory Factor Analysis (EFA)

In this research, factor analysis will help us consider the possibility of reducing 28 observed variables down to a smaller number to reflect particularly the impact of packaging elements on the consumer buying intention The original model has latent variables with 28 observed variables which affected the intention to buy Vietnamese brand of instant oats, is used for EFA stage

As mentioned in the previous chapter, remained items were analyzed by exploratory factor analysis (EFA) with Principal components method for extraction and Varimax method for rotation The results (table 4.3) indicated that factors were extracted from measurement scales with extraction sum of squared loadings being about 63.24% (greater than 50%) The KMO index was significant at 0.853 and the Bartlett’s Test of Sphericity had chi- square= 2540.660, df= 378 and sig= 000

Table 3: Exploratory Factor Analysis Results

Condition Value Requirement

KMO index 0,853 0,5 < 0,853 <1 Sig (Bartlett’s Test) 0,000 0,000 < 0,05 Total Variance Explained 63,240 63,240 > 50% Eigenvalue 1,146 1,146 >

(Data analysis by SPSS 20)

Accordingly,

• KMO = 0.801 proved that factor analysis is appropriate;

• Sig (Bartlett’s Test) = 0,000 (Sig <0.05) indicates that observed variables are correlated in the overall;

• Eigenvalue = 1.168> represented the variation explained by each variable and the collected variables has the best summary information;

Table 4: Rotated components results

Component

1 2 3 4 5

The color combination on the packaging draws my

attention 713

The color combination can easily be remembered 803

The color combination makes product stands out

among another brand 828

The shape of packaging is unique compared to another

brand 809

The shape of packaging is comfortable to use 682

The Font used on the product is legible and can be

understood 767

The Font used in writing Ingredient composition is

legible and could be interpreted .772

The Font used on the product attracts attention from

distance 644

The picture quality of the product packaging draws my

attention 764

The picture of the product packaging reflects the fact

that it is healthy 786

Brand name on packaging draws my attention 675

Product information on packaging is described clearly 733

Product information on packaging effects trust for the

product 738

Storage information on packaging is easy to follow 748

I react more favorably to product packaging imprinted

in Vietnamese 747

Product information on packaging (such as: the name of the firm, address, country of origin, production and

expiry date) is clearly 733

I would be willing to buy Instant Oats of this brand 854

If I were going to buy Instant Oats, the probability of

this brand is high 846

The probability that I would consider buying the instant

oats of this brand is high 820

The probability that I would purchase the instant oats of

this brand is high 819

In selecting from the many types and brands of Instant Oats available in the market, I will care a great deal as to which one I buy

.844 I think that the various types and brands of Instant Oats

available in the market are all very different .812

To me, making a right choice of instant oats is very

important 753

In making my selection of Instant Oats, I concern about

the outcome of my choice 702

The size of packaging meets my demand 753

Packaging material is made from recycle materials 738

Packaging material has high quality 699

Packaging material is environmentally friendly 618

According to the table “Rotated components results”, after exploratory factors analysis, all of 28 items were sorted in to groups with factor loading greater than 0.5

Accordingly, the latent variables were renamed as below:

• LATENT VARIABLES 1, named as “GRAPHIC ELEMENT”, has eigenvalue equal to 7.992 > There have 10 observed variables were used to measure this latent variable

• LATENT VARIABLES 2, named as “VERBAL ELEMENT”, has eigenvalue equal to 3.480 > There have observed variables were used to measure this latent variable

• LATENT VARIABLES 3, named as “PURCHASE INTENTION”, has eigenvalue equal to 3.054 > There have observed variables were used to measure this latent variable

• LATENT VARIABLES 4, named as “INVOLVEMENT LEVEL”, has eigenvalue equal to 2.432 > There have 10 observed variables were used to measure this latent variable

4.1.3 Confirmatory Factor Analysis (CFA)

Along with purifying observed variable discussed in chapter 3, confirmatory factor analysis is also used to measure the relevance of the model to primary data Chi-square (CMIN); Normed Chi-square (CMIN /df); CFI - Comparative Fit Index; TLI - Tucker & Lewis index); RMSEA index - Root Mean Square Error Approximation are the values used for test the fitness of research model

Additionally, according to Nguyen Khanh Duy (2009): The model is considered suitable for primary data if the Chi-square test has P-value > 0.05; If the model receives a probability value of Chi-square greater than 0.08 or GFI and CFI index close to and RMSEA index below 0.08 (Browne and Cudek, 1992) In the research which has CMIN/df < (with sample n <200), the model is considered to be a good fit (Kettinger and Lee, 1995) The above rules are summarized in the table as below:

Table 5: Model fit indices

(Source: Nguyen Khanh Duy, 2009)

In CFA analysis, based on standardized weights of the variables, there exit no variables were removed because the standardized weight of the observed variables had the allowable weights (> = 0.5) and statistically significant p is equal to 0.000 The specific results are shown in the following table:

Table 6: Confirmatory Factor Analysis results

CMIN/df RMSEA GFI TLI CFI

1.413 0,053 0,826 0,934 0,941

(Data analysis by AMOS 22)

Convergent validity

The standardized regression weights of 28 measurements were used to check this criterion As a result, the loadings are greater than 0.05 – recommended values by Anderson & Gerbing (1884) with the highest and the lowest values corresponding to 0.955 and 0.528 Additionally, the composite reliability (CR) and average variance extracted (AVE) were calculated All latent variables had CR values greater than 0.6 (Bagozzi & Yi, 1988) and AVE value above 0.5 (Fornell & Larcker, 1981) Particularly, the AVE values of verbal element and structural element were less than 0.5 at 0.462 and 0.401 respectively, however, according to SheuFen et al (2012) that values were acceptable In general, all those values of variables achieved the convergent validity requirements The specific results were indicated in following table

Table 7: Composite reliability and AVE results Latent Variables Composite reliability AVE

Graphic Elements 0.925 0.553 Structural Elements 0.726 0.401 Verbal Elements 0.837 0.462 Involvement Level 0.826 0.545 Purchase Intention 0.965 0.873

Discriminant validity

Table 8: The new latent variables

No Observed variables Latent variables

1 The color combination on the packaging draws my attention

GRAPHIC ELEMENT

2 The color combination can easily be remembered

3 The color combination makes product stands out among another brand

4 The shape of packaging is unique compared to another brand

5 The shape of packaging is comfortable to use

6 The Font used on the product is legible and can be understood

7 The Font used in writing Ingredient composition is legible and could be interpreted

8 The Font used on the product attracts attention from distance

9 The picture quality of the product packaging draws my attention

10 The picture of the product packaging reflects the fact that it is healthy

1 The size of packaging meets my demand

STRUCTURAL ELEMENT

2 Packaging material is made from recycle materials

3 Packaging material has high quality

4 Packaging material is environmentally friendly

1 Brand name on packaging draws my attention

VERBAL ELEMENT

2 Product information on packaging is described clearly

3 Product information on packaging effects trust for the product

4 Storage information on packaging is easy to follow

5 I react more favorably to product packaging imprinted in Vietnamese

6 Product information on packaging (such as: the name of the firm,

address, country of origin, production and expiry date) is clearly

1

In selecting from the many types and brands of Instant Oats available in the market, I will care a great deal as to which one I buy

INVOLVELMENT LEVEL

2 I think that the various types and brands of Instant Oats available in the market are all very different. 3 To me, making a right choice of instant oats is very important

4 In making my selection of Instant Oats, I concern about the outcome of my choice 1 I would be willing to buy Instant Oats of this brand

PURCHASE INTENTION

2 If I were going to buy Instant Oats, the probability of this brand

is high

3 The probability that I would consider buying the instant oats of

this brand is high

4 The probability that I would purchase the instant oats of this brand is high

4.2 Research Model Test

This study analyzed two times including (1) the model without moderator and (2) the model with moderator effects

4.2.1 Hypothesis testing without moderation of involvement level

The structural model contained all of three fundamental interaction had degree of freedom = 245 The results consist of Chi-Square = 388.004, Chi-square/df = 1.584, p-value = 0.000, GFI = 0.831, TLI = 0.926, CFI = 0.934, RMSEA = 0.063 (Table 4.9) The Chi-square/df (CMIN/df) was less than and GFI, TLI, CFI indices achieved satisfied values Hence, the structural model has satisfactory fit to collected data

Table 9: The model fit test of structural model

CMIN/df RMSEA GFI TLI CFI

1.584 0,063 0,831 0,926 0,934

(Data analysis by AMOS 22)

The influences of packaging element on consumer purchase intention tested by SEM had analyzed results as following table

Table 10: Research model without moderator tested by SEM

Interaction Estimate S.E C.R P-value

INTENTION ß GRAPHIC 0.634 0.115 5.509 0.000 INTENTION ß STRUCTURE 1.332 0.221 6.025 0.000 INTENTION ß VERBAL 0.470 0.227 2.072 0.038

(Data analysis by AMOS 22)

verbal elements at , were 1.332 and 0.470, respectively The hypothesis testing results was summarized in the table as below

Table 11: Hypotheses testing without moderator results

H1 Graphic packaging element positively influence consumer’s instance oats purchase intention Supported H2 Structural packaging element positively influence consumer’s instance oats purchase intention Supported H3 Verbal packaging element positively influence

consumer’s instance oats purchase intention Supported In general, visual package elements have stronger impact on customer purchase intention than verbal elements

Figure 1: Research Hypothesis structural equation modeling

4.2.2 Involvement Level effect testing by SEM

To measure the moderation impacts of involvement level, three interactions were calculated by multiplying independent variables and moderators (Sauer&Dick, 1993) They were presented as ZMGRxZMIL, ZMTRxZMIL and ZMVBxZMIL

Table 12: Coding of variable computing

Coding Equation

ZMGR The mean value of variable “Graphic Element”

ZMST The mean value of variable “Structural Element”

ZMVB The mean value of variable “Verbal Element”

ZMIL The mean value of variable “Involvement Level”

ZMPI The mean value of variable “Purchase Intention”

ZMVBxZIL The multiple value between the mean value of independent variable

“Verbal element” and moderator “Involvement Level”

ZMSTxZIL The multiple value between the mean value of independent variable

“Structural element” and moderator “Involvement Level”

ZMGRxZIL The multiple value between the mean value of independent variable

“Graphic element” and moderator “Involvement Level” The moderator effect on three interaction between packaging elements and purchase intention were tested by SEM in AMOS as the following figure

Figure 2: Moderator Effect

In this model, ZMGRxZMIL, ZMTRxZMIL and ZMVBxZMIL were considered as the multiple results of moderator “Involvement level” and graphic, structural, verbal elements, respectively To investigate the moderator effects on each package elements on purchase intention, researcher tested the interaction of above multiple variables and purchase intention by P-value consideration

The results of SEM analysis, indicated that research model achieved the requirement of model fit, specifically shown in the table 4.12

Table 13: Moderator effect model fit

CMIN/df RMSEA GFI TLI CFI

1.824 0,075 0,960 0,796 0,896

(Data analysis by AMOS 22)

Thereafter, the moderator effects of involvement level on the interaction between packaging elements and purchase intention was tested through SEM in AMOS had findings as below

Interaction Estimate S.E C.R P-value

INTENTION ß ZMVBxZMIL 0.043 0.062 0.683 0.495

INTENTION ß ZMSTxZMIL 0.114 0.057 1.998 0.045

INTENTION ß ZMGRxZMIL -0.042 0.055 -0.756 0.450

(Data analysis by AMOS 22)

Generally, the results of hypothesis testing are shown in following table:

Table 14: Hypothesis testing results

H1 Graphic packaging element positively influences consumer’s instance oats purchase intention Supported H2 Structural packaging element packaging positively

influences consumer’s instance oats purchase intention Supported

CHAPTER 5: CONCLUSION

5.1 Discussion of findings

Based on the analysis results from gathered data, this chapter indicated the findings discussion and some recommendation for future related study Researcher has considered two main elements of packaging design (i.e visual and verbal attributes) to advance the argument in this study The visual elements consist of can be seen attributes such as color, shape, size, picture, material, whilst verbal elements associated with words including brand name, product information and language

The analysis results proved that graphic attributes have a positive impact on consumer’s buying intention for following reasons: the graphic package design has attractive and easily memorable color These findings are similar to previous researches (e.g Salem, 2017; Pensasitorn, 2015; Krimi et al., 2013; etc) which claimed that package color and image evoke consumer attention and easy to remember Thus, understanding buyer’s response to package color assist marketers and designers enhance their product’s competitiveness on the shelves Besides, package shape can make product more attractive and additionally enhance the convenience in usage, thus, convenience package shape can boost buyers purchase a certain product Besides, for the font style used on package, it can make product more reliable and highlighted among alternative, thus, the font needs to be legible and have the appropriate size to attract buyers from distance The findings also proved that the picture on the package has ability to evoke buyer’s feeling and play a main role in transferring product’s usage instruction as well as its function

package Today, consumers concern more about environmental issues, thus, they prefer environment friendly packaging along with requiring a package material is good enough to protect products itself from external attacks

The findings also indicated that verbal elements have positive influences on consumer purchase intention due to following reasons: the brand name draw buyer’s attention; the products information is clearly described; the printed information storage is easily to follow and consumers prefer package is printed in local language, thus, selecting a suitable language for product label plays an important role in transferring effectively message to consumers These findings align to existing studies (e.g Salem, 2017; Adam & Ali, 2014; Mutsikiwa, 2013; etc) indicated that informational element is a decisive factor while making a buying decision Buyers often make purchase intention based on printed package information Reading information on package makes buyers evaluate product quality even though visual elements draw their attention at the beginning

Differently, involvement level is found that have no influence on the interaction between graphic element and purchase intention and the relationship of verbal element and purchase intention This result, is in contrast with existing study (Silayoi and Speece, 2004, 2007; Imiru, 2017), which caused by the different in products brand collection methods Specifically, related previous study quantitatively did research in total food industry with various kinds of product, differently, this study focused on a certain product type – instant oats with a certain number of selected brands Besides, participants were asked to evaluate only one brand they selected leading to the inconsistency of data which distorted the research accuracy which caused the contrary results to previous research Additionally, the different of respondent demographic characteristics might be another reason For instance, in Vietnamese’s mindset, package is the products image which reflect quality and the thoroughness manufacturers put into products, thus, there is no difference to the degree of graphic package design requirement for low or high involvement products In the other words, Vietnamese consumers always pay attention to graphic package design no matter how much involvement level is; thus, their purchase intention is easily affected by them (graphic design) Similarly, in Vietnam, most of buyers, purchase instant oats, have high health consciousness and safe consciousness, obviously, reading product information becomes their positive habits during shopping Their purchase intention is likelihood to be influenced by verbal element in case of consuming both low and high involvement products

5.2 Managerial implication

implication resulted from this study to communicate with their target customers and help their products stand out in highly competitive market Last but not least, in term of marketing implication, this study provides the understanding of instant oats consumer’s response to graphic, structural and verbal packaging design Thus, it may help the relevant companies to increase their knowledge about consumer behavior to design an effective communication tool – package

5.3 Practical implication

The findings of this study can be used by managers and marketers to design an effective packaging to ensure their products stand out among competitions Today consumers are becoming more careful when purchasing food products, thus, graphic, structural and verbal elements should be designed accurately and informatively provide ingredient contents Specifically, based on findings, this study provides some practical implication to marketers and designers in designing an effective package: • The package design (color, shape, font, picture, brand name) should be different

and memorable For example, we easily recognize the red can of Coke or the blue can of Pepsi on the shelf without reading its brand name

• The package design should truly reflect the product information with clear font style and easy-to-follow storage information

• Product should be sold in varied package size to meet different quantity demand of buyers

• Package material should be appropriate with product shelf life and transportation condition Due to the increased number of environmental consciousness consumers, designers should consider the environmentally friendly condition of package material

5.4 Limitation and further research Limitations

part of the target group Besides, convenient random sampling method will also reduce the accuracy of research results

Limitations on research subjects: This paper only analyzes the impact of packaging design of three Vietnamese brands of instant oats, so it does not reflect results objectively

Limitation on research brand selection: due to the participants were asked to choose then evaluate different brands, thus, there exist the inconsistency of collected data which might affect research accuracy

Limitations on the scope of the study: The study is only conducted based on cultural, economic and social factors in some city in Vietnam with very small quantity, thus, the objectivity of the topic is also limited due to each area has different buying intentions

In addition to the above limitations, this study excluded other important factors affecting consumers' buying intention (such as price, promotion, taste, experiences etc.,) which may affect more or less the accuracy of the research topic

Recommend for future research

Future studies need to have a more general view of the market and understand the related research demand to provide more detailed and accurate factors that are likelihood to influence intention buying, avoiding the omission of the influencing factor that reduces the accuracy of the study

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APPENDIXES

APPENDIXES 1: ONLINE SURVEY

Link: https://forms.gle/riQj19YgVC176n9RA

DATA ANALYSIS

APPENDIXES 2: Descriptive analysis

Gender

Frequency Percent Valid Percent Cumulative Percent Valid

Male 45 30.0 30.6 30.6 Female 102 68.0 69.4 100.0 Total 147 98.0 100.0

Missing System 2.0 Total 150 100.0

Age

Frequency Percent Valid Percent Cumulative Percent

Valid

Under 18 2.0 2.0 2.0 18-30 101 67.3 68.7 70.7 30-50 40 26.7 27.2 98.0 Over 50 2.0 2.0 100.0 Total 147 98.0 100.0

Missing System 2.0 Total 150 100.0

Income

Frequency Percent Valid Percent

Cumulative Percent

Valid

Under millions/

month 18 12.0 12.2 12.2

3-7 million/ month 37 24.7 25.2 37.4 Over trieu/

month 92 61.3 62.6 100.0

Total 147 98.0 100.0

Missing System 2.0

City

Frequency Percent Valid Percent Cumulative Percent

Valid

Hanoi 88 58.7 59.9 59.9

Hue 20 13.3 13.6 73.5

Danang 3.3 3.4 76.9

Ho Chi Minh 23 15.3 15.6 92.5

Khac 11 7.3 7.5 100.0

Total 147 98.0 100.0 Missing System 2.0

Total 150 100.0

APPENDIXES 3: Cronbach’s Alpha GRAPHIC ELEMENTS

Reliability Statistics

Cronbach's Alpha

N of Items 924 10

Item-Total Statistics

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Cronbach's Alpha if Item

STRUCTURAL ELEMENT Reliability Statistics

Cronbach's Alpha

N of Items 718

Item-Total Statistics

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Cronbach's Alpha if Item

Deleted ST1 10.85 3.717 440 698 ST2 10.75 3.573 497 662 ST3 10.61 4.062 516 657 ST4 10.61 3.403 588 603

VERBAL ELEMENTS TIME

Reliability Statistics

Cronbach's Alpha

N of Items 664

Item-Total Statistics

Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item-Total Correlation Cronbach's Alpha if Item

TIME

Reliability Statistics

Cronbach's

Alpha N of Items 837

Item-Total Statistics

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Cronbach's Alpha if Item

Deleted VB1 19.86 6.740 560 821 VB4 19.89 6.495 623 808 VB5 19.98 6.595 607 811 VB6 19.84 6.425 634 806 VB7 19.84 6.503 614 810 VB8 19.95 6.216 632 807

INVOLVEMENT LEVEL Reliability Statistics

Cronbach's Alpha

N of Items 795

Item-Total Statistics

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Cronbach's Alpha if Item

Deleted IL1 11.96 3.341 507 789 IL2 11.95 2.868 657 718 IL3 11.94 2.784 703 694 IL4 12.11 2.961 564 766

PURCHASE INTENTION Reliability Statistics

Cronbach's Alpha

Item-Total Statistics

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Cronbach's Alpha if Item

Deleted PI1 10.26 17.714 918 952 PI2 10.04 18.505 930 947 PI3 10.01 19.390 912 953 PI4 9.94 19.647 892 959

APPENDIXES 4: EFA

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .853 Bartlett's Test of Sphericity

Approx Chi-Square 2540.660

df 378

Sig .000

Total Variance Explained

Component Initial Eigenvalues Extraction Sums of

Squared Loadings

Rotation Sums of Squared Loadings Tota l % of Varianc e Cumulativ e %

Total % of Varianc

e

Cumulat ive %

Total % of Varianc

e

Cumulati ve %

1 7.99

2 28.545 28.545

7.99

2 28.545 28.545 6.22

0 22.216 22.216

2 3.48

0 12.428 40.973

3.48

0 12.428 40.973 3.52

8 12.601 34.817

3 3.05

4 10.907 51.879

3.05

4 10.907 51.879 3.36

9 12.031 46.848

4 2.43

2 8.687 60.566

2.43

2 8.687 60.566

2.59

1 9.255 56.103

5 1.14

6 4.094 64.660

1.14

6 4.094 64.660

2.39

6 8.557 64.660

6 910 3.251 67.912

7 878 3.136 71.048

8 820 2.930 73.978

9 710 2.535 76.513

11 665 2.376 81.295

12 565 2.017 83.312

13 506 1.807 85.120

14 496 1.770 86.889

15 466 1.663 88.553

16 428 1.529 90.082

17 382 1.364 91.445

18 347 1.241 92.686

19 336 1.202 93.888

20 315 1.126 95.014

21 298 1.066 96.080

22 252 899 96.979

23 217 777 97.756

24 169 604 98.360

25 147 527 98.886

26 140 501 99.388

27 111 397 99.785

28 060 215 100.000

Extraction Method: Principal Component Analysis Component Matrixa

Component

1 GR3 775

PI1 768 PI3 756 PI4 733 PI2 733 GR4 726 GR1 717 GR7 716 GR2 689 GR5 678 GR6 677 GR9 675 GR10 657 GR8 595

ST4 599 ST3 516 ST2

VB7 655 VB5 641 VB8 620 VB4 612 VB1 569

IL3 758

IL2 700

IL4 692

IL1 638

ST1

Extraction Method: Principal Component Analysis a components extracted

APPENDIXES 5: CFA

Notes for Model (Default model)

Computation of degrees of freedom (Default model)

Number of distinct sample moments: 406 Number of distinct parameters to be estimated: 67 Degrees of freedom (406 - 67): 339

Result (Default model)

Minimum was achieved Chi-square = 478.959 Degrees of freedom = 339 Probability level = 000

Model Fit Summary CMIN

Model NPAR CMIN DF P CMIN/DF Default model 67 478.959 339 000 1.413 Saturated model 406 000

Independence model 28 2730.820 378 000 7.224

Model RMR GFI AGFI PGFI Default model 057 826 791 689 Saturated model 000 1.000

Independence model 375 285 232 265

Baseline Comparisons

Model Delta1 NFI rho1 RFI Delta2 IFI rho2 TLI CFI Default model 825 804 941 934 941 Saturated model 1.000 1.000 1.000 Independence model 000 000 000 000 000

Parsimony-Adjusted Measures

Model PRATIO PNFI PCFI Default model 897 740 843 Saturated model 000 000 000 Independence model 1.000 000 000

NCP

Model NCP LO 90 HI 90 Default model 139.959 86.069 201.877 Saturated model 000 000 000 Independence model 2352.820 2190.616 2522.433

FMIN

Model FMIN F0 LO 90 HI 90 Default model 3.281 959 590 1.383 Saturated model 000 000 000 000 Independence model 18.704 16.115 15.004 17.277

RMSEA

Model RMSEA LO 90 HI 90 PCLOSE Default model 053 042 064 311 Independence model 206 199 214 000

Model AIC BCC BIC CAIC Default model 612.959 646.172 813.318 880.318 Saturated model 812.000 1013.265 2026.116 2432.116 Independence model 2786.820 2800.700 2870.552 2898.552

ECVI

Model ECVI LO 90 HI 90 MECVI Default model 4.198 3.829 4.622 4.426 Saturated model 5.562 5.562 5.562 6.940 Independence model 19.088 17.977 20.250 19.183

HOELTER

Model HOELTER 05

HOELTER 01 Default model 117 123 Independence model 23 24

Estimates (Group number - Default model)

Scalar Estimates (Group number - Default model) Maximum Likelihood Estimates

Regression Weights: (Group number - Default model)

Estimate S.E C.R P Label GR3 < - GRAPHIC 1.000

GR4 < - GRAPHIC 870 073 11.975 *** GR2 < - GRAPHIC 852 078 10.887 *** GR10 < - GRAPHIC 739 068 10.826 *** GR7 < - GRAPHIC 864 076 11.335 *** GR6 < - GRAPHIC 861 083 10.336 *** GR9 < - GRAPHIC 815 083 9.808 *** GR1 < - GRAPHIC 701 070 9.996 *** GR5 < - GRAPHIC 712 076 9.342 *** GR8 < - GRAPHIC 711 095 7.484 *** VB6 < - VERBAL 1.000

Estimate S.E C.R P Label VB5 < - VERBAL 936 130 7.203 *** VB1 < - VERBAL 849 128 6.611 *** PI2 < - INTENTION 1.000

PI1 < - INTENTION 1.056 043 24.423 *** PI4 < - INTENTION 893 042 21.046 *** PI3 < - INTENTION 925 039 23.856 *** IL3 < - INVOLVEMENT 1.000

IL2 < - INVOLVEMENT 744 082 9.062 *** IL1 < - INVOLVEMENT 626 081 7.755 *** IL4 < - INVOLVEMENT 747 092 8.083 *** ST4 < - SRUCTURE 1.000

ST1 < - SRUCTURE 766 144 5.319 *** ST3 < - SRUCTURE 724 117 6.181 *** ST2 < - SRUCTURE 929 149 6.249 ***

Standardized Regression Weights: (Group number - Default model)

Estimate IL1 < - INVOLVEMENT 648 IL4 < - INVOLVEMENT 673 ST4 < - SRUCTURE 712 ST1 < - SRUCTURE 528 ST3 < - SRUCTURE 635 ST2 < - SRUCTURE 644

Covariances: (Group number - Default model)

Estimate S.E C.R P Label GRAPHIC < > VERBAL 120 043 2.756 006 GRAPHIC < > INTENTION 647 133 4.876 *** GRAPHIC < > INVOLVEMENT -.003 078 -.040 968 GRAPHIC < > SRUCTURE 045 056 801 423 VERBAL < > INTENTION 220 069 3.206 001 VERBAL < > INVOLVEMENT -.023 043 -.544 587 VERBAL < > SRUCTURE 029 031 948 343 INTENTION < > INVOLVEMENT 230 124 1.864 062 INTENTION < > SRUCTURE 532 107 4.967 *** INVOLVEMENT < > SRUCTURE 092 059 1.553 121 e7 < > e10 278 065 4.273 *** Correlations: (Group number - Default model)

Estimate GRAPHIC < > VERBAL 277 GRAPHIC < > INTENTION 486 GRAPHIC < > INVOLVEMENT -.004 GRAPHIC < > SRUCTURE 081 VERBAL < > INTENTION 320 VERBAL < > INVOLVEMENT -.053 VERBAL < > SRUCTURE 101 INTENTION < > INVOLVEMENT 173 INTENTION < > SRUCTURE 603 INVOLVEMENT < > SRUCTURE 165 e7 < > e10 410

Variances: (Group number - Default model)

Estimate S.E C.R P Label VERBAL 224 050 4.502 *** INTENTION 2.113 271 7.783 *** INVOLVEMENT 840 144 5.847 *** SRUCTURE 369 086 4.288 *** e1 342 049 6.978 *** e2 323 044 7.291 *** e3 432 057 7.647 *** e4 331 043 7.663 *** e5 388 052 7.516 *** e6 520 067 7.783 *** e7 544 069 7.888 *** e8 381 049 7.856 *** e9 477 060 7.978 *** e10 840 102 8.209 *** e11 231 033 6.975 *** e12 248 034 7.241 *** e13 263 038 6.889 *** e14 227 032 7.016 *** e15 228 032 7.149 *** e16 260 035 7.546 *** e17 203 037 5.492 *** e18 308 049 6.300 *** e19 362 050 7.204 *** e20 258 040 6.494 *** e21 298 073 4.076 *** e22 351 056 6.292 *** e23 456 062 7.398 *** e24 567 079 7.218 *** e25 359 061 5.926 *** e26 558 074 7.575 *** e27 287 042 6.834 *** e28 448 067 6.742 ***

Squared Multiple Correlations: (Group number - Default model)

APPENDIXES 6: SEM

Computation of degrees of freedom (Default model)

Number of distinct sample moments: 300 Number of distinct parameters to be estimated: 55 Degrees of freedom (300 - 55): 245

Result (Default model)

Minimum was achieved Chi-square = 388.004 Degrees of freedom = 245 Probability level = 000

Model Fit Summary CMIN

Model NPAR CMIN DF P CMIN/DF Default model 55 388.004 245 000 1.584 Saturated model 300 000

Independence model 24 2457.623 276 000 8.904

RMR, GFI

Model RMR GFI AGFI PGFI Default model 060 831 793 678 Saturated model 000 1.000

Independence model 434 267 203 245

Baseline Comparisons

Model Delta1 NFI rho1 RFI Delta2 IFI rho2 TLI CFI Default model 842 822 935 926 934 Saturated model 1.000 1.000 1.000 Independence model 000 000 000 000 000

Parsimony-Adjusted Measures

Model PRATIO PNFI PCFI Saturated model 000 000 000 Independence model 1.000 000 000

NCP

Model NCP LO 90 HI 90 Default model 143.004 93.328 200.611 Saturated model 000 000 000 Independence model 2181.623 2026.825 2343.813

FMIN

Model FMIN F0 LO 90 HI 90 Default model 2.658 979 639 1.374 Saturated model 000 000 000 000 Independence model 16.833 14.943 13.882 16.054

RMSEA

Model RMSEA LO 90 HI 90 PCLOSE Default model 063 051 075 037 Independence model 233 224 241 000

AIC

Model AIC BCC BIC CAIC Default model 498.004 520.732 662.478 717.478 Saturated model 600.000 723.967 1497.130 1797.130 Independence model 2505.623 2515.540 2577.393 2601.393

ECVI

Model ECVI LO 90 HI 90 MECVI Default model 3.411 3.071 3.806 3.567 Saturated model 4.110 4.110 4.110 4.959 Independence model 17.162 16.102 18.273 17.230

Model HOELTER .05 HOELTER .01 Default model 107 113 Independence model 19 20

Scalar Estimates (Group number - Default model) Maximum Likelihood Estimates

Regression Weights: (Group number - Default model)

Estimate S.E C.R P Label INTENTION < - GRAPHIC 634 115 5.509 *** INTENTION < - VERBAL 470 227 2.072 038 INTENTION < - STRUCTURE 1.332 221 6.025 *** GR3 < - GRAPHIC 1.000

GR4 < - GRAPHIC 870 073 11.979 *** GR2 < - GRAPHIC 852 078 10.885 *** GR10 < - GRAPHIC 739 068 10.825 *** GR7 < - GRAPHIC 863 076 11.318 *** GR6 < - GRAPHIC 862 083 10.339 *** GR9 < - GRAPHIC 816 083 9.813 *** GR1 < - GRAPHIC 701 070 9.992 *** GR5 < - GRAPHIC 712 076 9.326 *** GR8 < - GRAPHIC 712 095 7.496 *** VB6 < - VERBAL 1.000

VB7 < - VERBAL 944 133 7.118 *** VB8 < - VERBAL 1.094 146 7.509 *** VB4 < - VERBAL 973 132 7.358 *** VB5 < - VERBAL 928 129 7.189 *** VB1 < - VERBAL 846 128 6.626 *** PI2 < - INTENTION 1.000

PI1 < - INTENTION 1.057 043 24.469 *** PI4 < - INTENTION 893 042 21.037 *** PI3 < - INTENTION 925 039 23.765 *** ST4 < - STRUCTURE 1.000

Standardized Regression Weights: (Group number - Default model)

Estimate INTENTION < - GRAPHIC 399 INTENTION < - VERBAL 153 INTENTION < - STRUCTURE 555 GR3 < - GRAPHIC 842 GR4 < - GRAPHIC 814 GR2 < - GRAPHIC 765 GR10 < - GRAPHIC 762 GR7 < - GRAPHIC 785 GR6 < - GRAPHIC 738 GR9 < - GRAPHIC 712 GR1 < - GRAPHIC 721 GR5 < - GRAPHIC 686 GR8 < - GRAPHIC 580 VB6 < - VERBAL 704 VB7 < - VERBAL 669 VB8 < - VERBAL 712 VB4 < - VERBAL 695 VB5 < - VERBAL 677 VB1 < - VERBAL 618 PI2 < - INTENTION 955 PI1 < - INTENTION 941 PI4 < - INTENTION 908 PI3 < - INTENTION 935 ST4 < - STRUCTURE 710 ST1 < - STRUCTURE 530 ST3 < - STRUCTURE 634 ST2 < - STRUCTURE 645

Covariances: (Group number - Default model)

Estimate S.E C.R P Label GRAPHIC < > VERBAL 120 044 2.758 006 GRAPHIC < > STRUCTURE 045 056 802 423 VERBAL < > STRUCTURE 029 031 940 347 e7 < > e10 277 065 4.263 ***

Estimate GRAPHIC < > VERBAL 277 GRAPHIC < > STRUCTURE 081 VERBAL < > STRUCTURE 100 e7 < > e10 410

Variances: (Group number - Default model)

Squared Multiple Correlations: (Group number - Default model)

APPENDIXES 7: MODERATOR

Notes for Model (Default model)

Computation of degrees of freedom (Default model)

Number of distinct sample moments: 36 Number of distinct parameters to be estimated: 22 Degrees of freedom (36 - 22): 14

Result (Default model)

Minimum was achieved Chi-square = 25.536 Degrees of freedom = 14 Probability level = 030

Model Fit Summary CMIN

Model NPAR CMIN DF P CMIN/DF Default model 22 25.536 14 030 1.824 Saturated model 36 000

Independence model 138.533 28 000 4.948

RMR, GFI

Model RMR GFI AGFI PGFI Default model 069 960 897 373 Saturated model 000 1.000

Independence model 151 828 778 644

Baseline Comparisons

Model Delta1 NFI rho1 RFI Delta2 IFI rho2 TLI CFI Default model 816 631 907 791 896 Saturated model 1.000 1.000 1.000 Independence model 000 000 000 000 000

Model PRATIO PNFI PCFI Default model 500 408 448 Saturated model 000 000 000 Independence model 1.000 000 000

NCP

Model NCP LO 90 HI 90 Default model 11.536 1.114 29.755 Saturated model 000 000 000 Independence model 110.533 77.518 151.081

FMIN

Model FMIN F0 LO 90 HI 90 Default model 175 079 008 204 Saturated model 000 000 000 000 Independence model 949 757 531 1.035

RMSEA

Model RMSEA LO 90 HI 90 PCLOSE Default model 075 023 121 171 Independence model 164 138 192 000

AIC

Model AIC BCC BIC CAIC Default model 69.536 72.426 135.325 157.325 Saturated model 72.000 76.730 179.656 215.656 Independence model 154.533 155.584 178.457 186.457

ECVI

HOELTER

Model HOELTER 05

HOELTER 01 Default model 136 167 Independence model 44 51

Scalar Estimates (Group number - Default model) Maximum Likelihood Estimates

Regression Weights: (Group number - Default model)

Estimate S.E C.R P Label ZMPI < - ZMGR 409 060 6.843 *** ZMPI < - ZMST 439 058 7.505 *** ZMPI < - ZMVB 186 060 3.106 002 ZMPI < - ZMIL 109 058 1.871 061 ZMPI < - ZMSTxZIL 115 057 2.007 045

Standardized Regression Weights: (Group number - Default model)

Estimate ZMPI < - ZMGR 419 ZMPI < - ZMST 449 ZMPI < - ZMVB 191 ZMPI < - ZMIL 112 ZMPI < - ZMSTxZIL 120

Covariances: (Group number - Default model)

Correlations: (Group number - Default model)

Estimate ZMGR < > ZMIL 014 ZMIL < > ZMGRxZIL -.124 ZMGR < > ZMGRxZIL -.042 ZMIL < > ZMSTxZIL -.047 ZMST < > ZMSTxZIL 038 ZMIL < > ZMVBxZIL 130 ZMVB < > ZMIL -.057 ZMVB < > ZMVBxZIL -.015 ZMGR < > ZMVB 216

Variances: (Group number - Default model)

Estimate S.E C.R P Label ZMGR 994 116 8.545 *** ZMST 993 116 8.544 *** ZMVB 994 116 8.544 *** ZMIL 1.003 117 8.547 *** ZMSTxZIL 1.033 121 8.544 *** ZMGRxZIL 1.124 132 8.544 *** ZMVBxZIL 887 104 8.544 *** e1 495 058 8.544 ***

Squared Multiple Correlations: (Group number - Default model)