Analysis on the Influence of Brand Equity Towards Consumer Purchase Intention Case Study of Nokia/ Microsoft Lumia in President University

Analysis on the Influence of Brand Equity towards Consumer Purchase Intention Case Study of Nokia/ Microsoft Lumia in President University

By Stephen ID No. 014201100104

A Skripsi presented to the Faculty of Business President University In partial fulfillment of the requirements for Bachelor Degree in Economy Major in Management

March 2015

PANEL OF EXAMINERS APPROVAL SHEET

The Panel of Examiners declare that the Skripsi entitled “Analysis on the Influence of Brand Equity towards Consumer Purchase Intention - Case Study of Nokia/ Microsoft Lumia in President University” that was submitted by Stephen majoring in Management from the Faculty of Business was assessed and approved to have passed the Oral Examinations on 17th of March, 2015

Purwanto, S.T., M.M. ______Chair-Panel of Examiners

Viscensius Jajat Kristanto S.E., M.M., MBA ______Examiner I

Liswandi, S. Pd., M.M. ______Examiner II

SKRIPSI ADVISER RECOMMENDATION LETTER

This Skripsi entitled “Analysis on the Influence of Brand Equity towards Consumer Purchase Intention - Case Study of Nokia/ Microsoft Lumia in President University” prepared and submitted by Stephen in partial fulfillment of the requirements for the degree of Bachelor in the Faculty of Business has been reviewed and found to have satisfied the requirements for a Skripsi fit to be examined. I therefore recommend this Skripsi for Oral Defense.

Cikarang, Indonesia, 13th of March 2015

Acknowledged by, Recommended by,

Vinsensius Jajat Kristanto S.E., M.M., MBA Liswandi, S. Pd., M.M. Head of Management Study Program Adviser

DECLARATION OF ORIGINALITY

I declared that this thesis, entitled “Analysis on the Influence of Brand Equity towards Consumer Purchase Intention - Case Study of Nokia/ Microsoft Lumia in President University” is, to the best of my knowledge and belief, an original piece of work that has not been submitted, either in whole or in part, to another university to obtain a degree.

Cikarang, Indonesia, 13th of March 2015

Stephen

iii

ACKNOWLEDGEMENT

The researcher would like to express his gratitude to his family and colleagues in motivating and assistance until the researcher could finish this study “ANALYSIS ON THE INFLUENCE OF BRAND EQUITY TOWARDS CONSUMER PURCHASE INTENTION - CASE STUDY OF NOKIA/ MICROSOFT LUMIA IN PRESIDENT UNIVERSITY”.

Gratitude would also be expressed to Ms. Grace Amin and Mr. Liswandi as the thesis advisors who assist the researcher in order to be able to deliver this study, to Ms. Geraldine and Mr. Orlando as lecturers who assisted me in the time of needs during the preparation of this thesis.

This study consists of dimensions of brand equity which may affect consumer purchase intention. The detail of the findings will be tested, described and analyzed by the researcher and may be used to give several recommendations to the related companies and related companies in the same industry.

All inputs are welcome and would be put into consideration to improve this study since the study itself will not be perfect but still could be improved. May this study be useful for readers and future researcher.

Cikarang, Indonesia, 13th of March 2015

Stephen

TABLE OF CONTENTS

CHAPTER I INTRODUCTION 1.1 Background of the Study ...... 1 1.2 Problem Identification ...... 6 1.3 Statement of Problem ...... 6 1.4 Research Objective...... 7 1.5 Definition of Terms ...... 7 1.6 Scope and Limitation ...... 8 1.7 Research Benefits ...... 9 CHAPTER II LITERATURE REVIEW 2.1 Theoretical Review ...... 10 2.1.1 Brand ...... 10 2.1.2 Brand Equity ...... 12 2.1.3 Brand Awareness ...... 15 2.1.4 Perceived Quality ...... 17 2.1.5 Brand Associations ...... 19 2.1.6 Brand Loyalty...... 22 2.1.7 Consumer Purchase Intention ...... 24 2.1.8 Relationship between Concepts ...... 25 2.2 Previous Research ...... 26 2.3 Theoretical Framework ...... 28 2.4 Operational Variables...... 29 2.5 Hypothesis ...... 31 CHAPTER III METHODOLOGY 3.1 Research Design ...... 33 3.2 Sampling Design ...... 34 3.2.1 Population ...... 34 3.2.2 Sample ...... 34 3.3 Research Framework ...... 36 3.4 Research Instrument ...... 37 3.4.1 Primary Data ...... 37

3.4.2 Secondary Data ...... 41 3.5 Validity and Reliability ...... 41 3.5.1 Validity ...... 36 3.5.2 Reliability ...... 38 3.6 Descriptive Statistic ...... 45 3.7 Classical Assumption Test ...... 46 3.8 Multiple Linear Regression ...... 48 3.9 Data Collection Procedure ...... 49 3.10 Hypothesis Testing ...... 49 3.10.1 T-Test ...... 49 3.10.2 F-Test ...... 50 3.10.3 R2 Test ...... 51 CHAPTER IV ANALYSIS AND INTERPRETATION 4.1 Company Profile ...... 52 4.2 Data Analysis ...... 53 4.2.1 Validity Test ...... 54 4.2.2 Reliability Test ...... 55 4.2.3 Descriptive Statistic ...... 55 4.2.3.1 Descriptive Statistic of Variable “Brand Awareness” ...... 56 4.2.3.2 Descriptive Statistic of Variable “Perceived Quality” ...... 57 4.2.3.3 Descriptive Statistic of Variable “Brand Association” ...... 58 4.2.3.4 Descriptive Statistic of Variable “Brand Loyalty”...... 59 4.2.3.5 Descriptive Statistic of Variable “Consumer Purchase Intention” ...... 60 4.2.4 Classical Assumption Test ...... 61 4.2.4.1 Normality Test ...... 61 4.2.4.2 Multicollinearity Test ...... 62 4.2.4.3 Heteroscedasticity Test ...... 64 4.2.4.4 Autocorrelation Test...... 65 4.2.5 Multiple Linear Regression ...... 65 4.2.6 Hypothesis Testing ...... 66 4.2.6.1 T-Test ...... 66 4.2.6.2 F-Test ...... 68

4.2.6.3 R2 Test ...... 69 4.3 Interpretation of Result ...... 70 4.3.1 The Influence of Brand Awareness on Consumer Purchase Intention ...... 70 4.3.2 The Influence of Perceived Quality on Consumer Purchase Intention ...... 71 4.3.3 The Influence of Brand Association on Consumer Purchase Intention ...... 72 4.3.4 The Influence of Brand Loyalty on Consumer Purchase Intention ...... 73 4.3.5 The Influence of Brand Awareness, Perceived Quality, Brand Association, and Brand Loyalty Simultaneously on Consumer Purchase Intention ...... 74 CHAPTER V CONCLUSION AND RECOMMENDATION 5.1 Conclusion ...... 75 5.2 Recommendation...... 76 REFERENCES ...... 77 APPENDICES ...... 83

vii

LIST OF FIGURE

Figure 1.1 Global Smartphone Users (2013 – 2018) ...... 2 Figure 1.2 Global Smartphone Sales in Millions of Unit (2014) ...... 3 Figure 1.3 Smartphone Penetration in Indonesia (2013) ...... 3 Figure 1.4 Smartphone Popular Brand Index in Indonesia (2014) ...... 4 Figure 1.5 Brand of Current Smartphone ...... 5 Figure 2.1 Brand Equity Model ...... 14 Figure 2.2 Measurement of Brand Awareness ...... 16 Figure 2.3 Theoretical Framework – 4 Dimensions of Brand Equity ...... 28 Figure 3.1 Research Framework ...... 36 Figure 4.1 Normality Test: Histogram ...... 60 Figure 4.2 Normality Test: P-P Plot ...... 61 Figure 4.3 Heteroscedasticity Test: Scatterplot ...... 63

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LIST OF TABLES

Table 3.1 Likert-Scale Interpretation ...... 37 Table 3.2 Measurement Items in the Questionnare ...... 37 Table 3.3 Cronbach’s Alpha Interpretation Table...... 43 Table 4.1 Pearson Product-Moment Correlation Test Result ...... 53 Table 4.2 Cronbach’s Alpha Test Result ...... 54 Table 4.3 Descriptive Statistic of Variable “Brand Awareness” Result ...... 55 Table 4.4 Descriptive Statistic of Variable “Perceived Quality” Result ...... 56 Table 4.5 Descriptive Statistic of Variable “Brand Association” Result ...... 57 Table 4.6 Descriptive Statistic of Variable “Brand Loyalty” Result ...... 58 Table 4.7 Descriptive Statistic of Variable “Consumer Purchase Intention” Result ...... 59 Table 4.8 Multicollinearity Test ...... 62 Table 4.9 Durbin-Watson Test ...... 65 Table 4.10 Regression Coefficient Table ...... 65 Table 4.11 F-Test (ANOVA) Result ...... 68 Table 4.12 R2 Test Result ...... 70

ABSTRACT

This study is conducted to analyze the significant influence of brand equity towards consumer purchase intention. There are four dimension of brand equity that is employed in this study and treated as the independent variable, which are: (1) Brand Awareness, (2) Perceived Quality, (3) Brand Association, and (4) Brand Loyalty. Consumer purchase intention is treated as the dependent variable in this study. Slovin’s formula is employed for the sampling size calculation with 90% confidence level. This study implements quantitative research method. The questionnaire is used as a tool in this study to be able to measure the significance influence of each dimensions (variables). The population in this study is President University students of batch 2011, because at this age they are assumed to be independent individuals who make their own purchase decision. This study use multiple-linear regression as its statistical treatment of data with descriptive as complement; pearson product-moment correlation test for validity, cronbach’s alpha for reliability, and classical assumption test as the pre-requisite for multiple- linear regression analysis. All items used in this study are valid and normally distributed with no multicollinearity and heteroscedasticity detected. T-test and F- test are employed for the hypothesis testing. This study find that brand awareness, perceived quality, and brand association has no significant influence towards consumer purchase intention whereas a whole, they have simultaneous significant influence towards consumer purchase intention.

Keywords: Brand Equity, Brand Awareness, Perceived Quality, Brand Association, Brand Loyalty Consumer Purchase Intention, Multiple-Linear Regression Analysis

CHAPTER I

INTRODUCTION

1.1 Background of the Study

Brand equity is a set of brand assets and liabilities linked to a brand, its name and symbol that add to or subtract from the value provided by a product or service to a firm and/or to that firm’s customer. There are five dimensions in consumer-based brand equity, namely brand awareness, brand associations, perceived quality, brand loyalty, and other proprietary brand assets such as patents, trademarks and channel relationships. The former four dimensions of brand equity represent consumer perceptions and reactions to the brand, while proprietary brand assets are not pertinent to consumer based brand equity. (Aaker, 1991 as cited by Christodoulides & Chernatony, 2009)

Brand equity provides value for both the customer and the firm. Brand equity creates value to customers by enhancing efficient information processing and shopping, building confidence in decision making, reinforcing buying, and contributing to self-esteem. Brand equity creates value to firms by increasing - margins, gaining leverage over retailers, and achieving distinctiveness over the competition

This study is going to use the four dimensions proposed by Aaker (1991) as cited by Christodoulides & Chernatony (2009) to find the relation between consumer- based brand equity with consumer purchase intention in smartphone market. Consumer purchase intention is related to the intent of the consumer to make a purchase decision, the point in which the consumer will make the purchase or not is consumer purchase behavior.

President University is the study site where college students with age span of 18 to 21 years old are going to be the main respondents.

GLOBAL SMARTPHONE USERS 2013 - 2018 2.56 100.0% 2.50 2.40 90.0% 2.16 80.0% 2.00 1.91 70.0% 1.64 60.0% 49.5% 51.7% 1.50 1.31 46.4% 42.9% 50.0% 38.4% 40.0% 1.00 32.4% 30.0% 34.3% 20.0% 0.50 25.0% 16.8% 10.0% 12.6% 10.4% 0.00 7.6% 0.0% 2013 2014 2015 2016 2017 2018

Smartphone Users (in Billions) % of Mobile Phone Users % Change

Figure 1.1 Global Smartphone Users (2013 – 2018) Source : eMarketer, 2014

The number of smartphone users in the world are increasing annually (figure 1.1) and that is one of the simple phenomenon where smartphone has become an integral part in daily life. The decreasing number of percentage of change means that the absorption rate of smartphone is decreasing because the number of users are already dominating the majority.

University students usually own a smartphone in present setting where smartphone are going to be used for communication between colleagues and even to manage assignments and exams conducted in the university.

GLOBAL SMARTPHONE SALES PER UNIT (IN MILLIONS OF UNITS) - 2014

Apple Samsung Lenovo + Motorola Huawei LG TCL - Alcatel Others 316.6

192.6

94.1 75.3 61.2 59.6 37

Figure 1.2 Global Smartphone Sales in Millions of Unit (2014) Source : Counterpoint Technology Market Research (2014)

The shipments number of smartphone in the global market is dominated by Samsung and Apple with other brand following it. This prove that Nokia is not even in the top 5 among other manufacturers in terms of units sold.

SMARTPHONE PENETRATION IN INDONESIA 2013

30.0% 27.4%

25.0%

20.0% 16.8% 14.0% 15.0% 12.4%

10.0%

5.0% 3.6%

0.0% 18 - 24 25 - 34 35 - 44 45 - 54 All

Figure 1.3 Smartphone Penetration in Indonesia (2013) Source : Our Mobile Planet (2013) – Joint Research by Google, Ipsos, Mobile Marketing Association, and the Interactive Advertising Bureau (iab.). 3

Smartphone penetration in Indonesia in 2013 is comprised of 14% of the total population, with the highest number of smartphone users are in age group of 18 – 24 years old (young adults) and then the penetration rate is decreasing along with the increase of age group (figure 1.2).

President University students are comprised of people of age 18 – 21 years old (which is considered as young adults) aligned with the data presented above. Along with this association, there is an assumption where most if not all students in President University own a smartphone.

Although still a young University, President University (PresUniv) is growing at a tremendous rate every year. There are now around 3,500 students attending President University from Indonesia as well as many other countries. President University has expanded its course offerings and now offers 32 different majors in subjects ranging from Industrial Engineering to Public Relations.

President University students own a wide variety of smartphones available and young generation is usually aware of technology development which is why they are chosen as the sample of this study. The study will specifically aimed to President University students of batch 2011 which is 988 in number.

POPULAR BRAND INDEX IN INDONESIA

2014

51.6

8.7

6.6

6.1

3.6

3.3

2.4

1.9

1.8

1.7

0.9 0.9 0.5

Figure 1.4 Smartphone Popular Brand Index in Indonesia (2014) Source : W&S Market Research (2014) 4

Samsung is the most popular brand in Indonesia with around half of Indonesian population prefers Samsung among other smartphone brands (figure 1.3). This is also become an assumption where most students in President University are using smartphone with Samsung brand.

CURRENTLY USED SMARTPHONE BRAND IN INDONESIA (2014)

Samsung BlackBerry Smartfren Nokia Advan Sony Evercoss Lenovo Oppo Mito

Apple LG Hisense Huawei Himax

49.7%

16.2%

14.4%

9.2%

8.5%

8.0%

7.9%

7.1%

6.4%

3.9%

3.0%

2.7%

1.9%

1.0% 0.7%

CURRENT SMARTPHONE BRAND

Figure 1.5 Brand of the Current Smartphone Source : Baidu Indonesia, 2014

From the depiction in figure 1.3 and figure 1.4 it is safe to assume that Samsung has the highest brand equity and it is leading to consumers’ decision to purchase a smartphone with Samsung brand. Nokia is placed third in popular brand index in Indonesia and is placed fourth in the currently used smartphone brand in Indonesia.

Figure 1.3 and figure 1.4 also depict that popularity is not necessarily the same with consumer purchase behavior (except for Samsung brand where they have the strongest brand equity along with the highest number of users in Indonesia).

1.2 Problem Identification

Nokia/ Microsoft Lumia has a strong brand presence in Indonesia (figure 1.3) but in reality it is not aligned with the data in figure 1.4 regarding the number of user that use Nokia/ Microsoft Lumia smartphone.

In the global market, Nokia has an insignificant number of sales compared to other manufacturers which pose a problem to Nokia as a global manufacturer.

Brand equity is one of the underlying factor that affect consumer purchase intention (instead of focusing on consumer purchasing behavior or purchase decision, the most fundamental aspect is the intention to make a purchase. In such a competitive environment, brand equity is playing a bigger role in affecting consumer purchase intention.

There are four dimension in brand equity which were proposed by Aaker (1991): 1) Brand Awareness, 2) Perceived Quality, 3) Brand Association, and 4) Brand Loyalty.

President University is chosen as the case study because of two reasons. First, the age range suit the highest penetration rate of smartphone in Indonesia. Second, the researcher is a student of President University which make the data collection more convenient than searching for data in other universities. Third, most if not all President University students own at least a smartphone.

1.3 Statement of Problem

1. Is there any significant influence of brand awareness towards consumer purchase intention of President University students batch 2011?

2. Is there any significant influence of perceived quality towards consumer purchase intention of President University students batch 2011?

3. Is there any significant influence of brand association towards consumer purchase intention of President University students batch 2011?

4. Is there any significant influence of brand loyalty towards consumer purchase intention of President University students batch 2011?

5. Is there any simultaneous significant influence of brand awareness, perceived quality, brand association, and brand loyalty towards consumer purchase intention of President University students batch 2011? 6

1.4 Research Objective

1. To analyze significant influence of brand awareness towards consumer purchase intention of President University students batch 2011 2. To analyze significant influence of perceived quality towards consumer purchase intention of President University students batch 2011 3. To analyze significant influence of brand association towards consumer purchase intention of President University students batch 2011 4. To analyze significant influence of brand loyalty towards consumer purchase intention of President University students batch 2011 5. To analyze simultaneous significant influence of brand awareness, perceived quality, brand association, and brand loyalty towards consumer purchase intention of President University students batch 2011

1.5 Definition of Terms

1. Brand: a name, term, sign, symbol, or design, or a combination of them intended to identify the goods or services of one seller from among a group of sellers and to differentiate them from those of the competitors. 2. Brand Equity: a set of brand assets and liabilities linked to a brand, its name and symbol that add to or subtract from the value provide by a product or service to a firm and/or to that firm’s customer. 3. Consumer: a person or organization that uses economic services or commodities. 4. Purchase Intention: a plan to purchase a particular good or service in the future. 5. Smartphone: a mobile phone with an advanced operating system; typically include the features of a phone with those of other popular mobile devices, such as personal digital assistant, media player and GPS navigation unit.

1.6 Scope and Limitation

This study will have President University students because almost all President University students own a smartphone, however, not all students will be used as samples but only students in batch 2011 will be used as the sample. The sample size will be determined with Slovin’s formula since the behavior of university students are heterogeneous.

The primary data will be obtained via survey and secondary data will be obtained from related journal articles, periodical article, books, and web sources with previous studies as further references in this study.

The independent variables used in the study were based from the four dimensions brand equity proposed by Aaker (1991) cited by Christodoulides & Chernatony (2009) and consumer purchase intention as the dependent variable.

Since this study is conducted under a limited amount of time and resources, the result may not be perfect but the researcher believe that the data and information obtained are valid, reliable, and accountable in all records. The dependent variable used was limited to consumer purchase intention because of the time constraint to collect the information after the purchase was done. The research could also be used as a reference for future studies.

1.7 Research Benefits

Nokia/ Microsoft Inc.

To be able to know the brand equity power of their brand in Indonesia, primarily in President University which may be used for further research references in college setting.

Nokia/ Microsoft could also take a look at what component of brand equity that they lack of which they could improve: brand awareness, perceived quality, brand association, brand loyalty; those dimensions could be improved along with their market segmentation to be able to compete better in the smartphone industry.

The Researcher

This study acts as one of the requirement to get a bachelor degree in President University, therefore this study is conducted by the researcher. Other than that, the researcher would also know whether brand equity actually affect consumer purchase intention among college students. The brand in question is Nokia/ Microsoft Lumia where they have difficulty competing with other brand.

Readers and Future Researcher

This study could be used for future research and may the variables used could be improved and developed; similar to what was done by Yoo and Donthu (2001) where they combine both brand awareness with brand association since they found that they are similar and they add another variable, Overall Brand Equity.

For readers, this study may give a better insight on how a study about brand equity could be done with Aaker basic framework and how brand equity power could affect consumer purchase intention related to Nokia/ Microsoft Lumia brand.

CHAPTER II

LITERATURE REVIEW

2.1 Theoretical Review

2.1.1 Brand

Brand names act as signals to consumers. A brand signal becomes the sum of that brand’s past and present marketing activities. Imperfect and asymmetrical market information produces uncertainty in consumers’ minds. (Erdem, et al., 2006 as cited by Christodoulides & Chernatony, 2009). Brand is a distinguishing name and symbol (such as logo, trademark, or packaged design) intended to identify the goods or services of either one seller or a group of sellers, and to differentiate those goods or services from those of competitors (Aaker, 1991 as cited by Jung & Sung, 2008).

A brand conveys a specific set of features, benefits and services to buyers. It is a mark, a tangible emblem, which says something about the product. The best brands, for example, often convey a warranty of quality. A brand can deliver up to four levels of meaning (Kotler & Armstrong, 2010 as cited by Rinto, 2013):

1. Attributes

A brand first brings certain product attributes, e.g.: Mercedes suggests such attributes as ‘well engineered’, ‘well built’, ‘durable’, ‘high prestige’, ‘fast’, ‘expensive’ and ‘high resale value’.

The company may use one or more of these attributes in its advertising for the car. For years, Mercedes advertised ‘Engineered like no other car in the world’. This provided a positioning platform for other attributes of the car.

2. Benefits

Customers do not buy attributes, they buy benefits. Therefore, attributes must be translated into functional and emotional benefits. For example, the attribute ‘durable’ could translate into the functional benefit, “I won’t have to buy a new car every few years’. The attribute ‘expensive’ might translate into the emotional benefit, ‘The car makes me feel important and admired’. The attribute ‘well built’ might translate into the functional and emotional benefit, that I am safe in the event of an accident’.

3. Values

A brand also says something about the buyers’ value. Thus Mercedes buyers value high performance, safety and prestige. A brand marketer must identify the specific groups of car buyers whose values coincide with the delivered benefit package.

4. Personality

A brand also projects a personality. Motivation researchers sometimes ask, ‘If this brand were a person, what kind of person would it be?’ Consumers might visualize a Mercedes automobile as being a wealthy, middle-aged business executive. The brand will attract people whose actual or desired self-images match the brand’s image. A brand must deliver a promise, brand marketing is said to be a promise, which actually exists in the minds of consumers. Brand as one of the decisions of individual products that provide benefits to consumers and producers (sellers) due to the brand can help consumers to identify certain products which in turn will help the buying process. The stronger the difference and distinction of a brand is the stronger brand preference of consumers.

2.1.2 Brand Equity

The term brand equity refers to the incremental value added by a brand name to a product (Farquhar, 1989 as cited by Spry, et al., 2011). Aaker (1991) as cited by Spry, et al., (2011) and Keller (1993) as cited by Spry, et al. (2011) provide two of the most widely accepted conceptualizations of brand equity based on the consumer perspective. Aaker (1991) as cited by Spry, et al. (2011) operationalized brand equity as a set of assets (or liabilities) consisting of brand awareness, brand associations, perceived quality, brand loyalty and other proprietary assets.

On the other hand, Keller (1993) referred to brand equity as customer-based brand equity and deﬁned it as “the differential effect of brand knowledge on consumer response to the marketing of the brand”. Though both Aaker (1991) and Keller (1993) adopted a consumer perspective and focused largely on memory-based brand associations there are minor differences in their conceptualization of brand equity. While Aaker (1991) had elevated perceived quality (quality-related brand associations) as a separate dimension, Keller (1993) considered all types of brand associations (including those that are quality-related) as brand image. (Spry et al., 2011)

The concept of brand equity cover a wide range, because consumers’ experiences, feelings and what they learn about the brand in long term, is relevant with concept of brand equity. This term is the word that we know about consumer based brand equity and that is the added value that connects to the product in consumers mind, words and actions. (Leone et al, 2006 as cited by Moradi and Zarei, 2011)

Brand equity in the consumer-based approach concentrate on the knowledge of consumers about the brand. Cobb-Walgren, et al (1995) confirms that brand equity influence brand preference and purchase intentions directly and ultimately influence consumers’ brand choice. Also other researchers (e.g., Myers, 2003; Prasad and Dav, 2000; de Chernaony, 2004) pointed out that high equity leads to high brand preference and loyalty. As brand equity is reflected in brand preference, it could be inferred that brand preference would be reflected in purchase or usage intention. (Moradi and Zarei, 2011)

Companies that managed to create a good brand equity will gain a competitive advantage. According to Kotler (2007) as cited by Tobing (2014), the competitive advantage of high brand equity are:

1. The company will has less marketing costs due to customer awareness and brand loyalty is high.

2. The company will has a stronger position in negotiations with distributors and retailers because customers expecting them to sell the brand.

3. Companies can charge a higher price than its competitors because the brand is believed to have high quality.

4. Company will be easy to launch a brand extension because the brand has high credibility.

5. Brands that protects the company from price competition.

Aaker’s brand equity model lists three ways of how brand assets create value for the customer. Firstly, brand equity can help a customer interpret, process, store, and retrieve a huge quantity of information about products and brands. Secondly, it can affect the customer’s confidence in the purchase decision; a customer will usually be more comfortable with the brand that was last used, is considered to have high quality, or is familiar. Finally, perceived quality and brand associations provide value to the customer by enhancing the customer’s satisfaction. (Moisescu, 2005)

There are five dimensions in consumer-based brand equity, namely brand awareness, brand associations, perceived quality, brand loyalty, and other proprietary brand assets such as patents, trademarks and channel relationships. The former four dimensions of brand equity represent consumer perceptions and reactions to the brand, while proprietary brand assets are not pertinent to consumer based brand equity. (Aaker, 1991 as cited by Christodoulides and Chernatony, 2009)

Figure 2.1 Brand Equity Model Source : Aaker, 2006 as cited by Rinto, 2013

Brand equity can be grouped into five categories (Aaker, 2006 as cited by Rinto, 2013), which are:

1. Brand Awareness Indicates the ability of a potential buyer to recognize or recall that a brand is part of a particular product category.

2. Brand Associations Reflects the image of a brand of a particular impression in relation to the habits, lifestyles, benefit, product attributes, geographic, price, competitors, celebrities and others.

3. Perceived Quality Reflecting the overall customer perception of quality/ excellence of a product or service with respect to the expected mean.

4. Brand Loyalty Reflects the level of consumer engagement with a brand product.

5. Other proprietary brand assets.

2.1.3 Brand Awareness

Brand awareness refers to whether consumers can recall or recognize a brand, or simply whether or not consumers know about a brand (Keller, 2008 as cited by Huang & Sarigöllü, 2012). Consumers may link the related brand knowledge to the brand name, which finally constitutes brand equity (Aaker, 1991; Keller, 1993 as cited by Huang & Sarigöllü, 2012). Hence, brand awareness provides a kind of learning advantage for the brand (Keller, 2008 as cited by Huang & Sarigöllü, 2012). Brand awareness affects consumer decision-making, especially for low- involvement packaged goods. Brands that consumers know are more likely to be included in the consumers’ consideration set. Consumers may use brand awareness as a purchase decision heuristic. Therefore brand awareness increases brand market performance. (Huang & Sarigöllü, 2012)

Measurement of brand awareness is based on notions of brand awareness that includes the brand level, which is top of mind, an increase in re-brand (brand recall), and the introduction of brand (brand recognition), and not aware of the brand (unaware of brand). (Aaker, 2006 as cited by Rinto, 2013)

Top of Mind

Brand Recall

Brand Recognition

Unaware of Brand

Figure 2.2 Measurement of Brand Awareness Source : Aaker, 2006 as cited by Rinto, 2013

The measurement could be interpreted as follows:

1. Top of Mind Brand which first appeared in the minds of consumers when consumers were asked about a product category that can be recalled spontaneously, without any assistance. In other words, the brand is the major brands of different brands that exist in the minds of consumers.

2. Brand Recall Recall of the brand without the help of hint or information (unaided recall).

3. Brand Recognition Minimal level of brand awareness, where the introduction of an emerging brand again after the recall through the aid of hint or information (aided recall).

4. Unaware of Brand This level is the lowest level in the pyramid of brand awareness, where consumers are not aware of the brand. 16

Brand awareness plays an important role on purchase intention because consumers tend to buy familiar and well known product. Brand awareness can help consumers to recognize a brand from a product category and make purchase decision. Brand awareness has a great influence on selections and can be a prior consideration base in a product category. Brand awareness also acts as a critical factor in consumer purchase intention, and certain brands will accumulate in consumers’ mind to influence consumer purchase decision. A product with a high level of brand awareness will receive higher consumer preferences because it has higher market share and quality evaluation. (Chi, et al., 2009)

2.1.4 Perceived Quality

Perceived quality is another important dimensions of brand equity (Aaker, 1991 as cited by Jalilvand, et al., 2011). Perceived quality is not the actual quality of the product but the consumer’s subjective evaluation of the product (Zeithaml, 1988 as cited by Jalilvand, et al., 2011). It is a competitive necessity and many companies today have turned customer-driven quality into a potent strategic weapon. They create customer satisfaction and value by consistently and profitability meeting customer’s needs and preferences for quality. (Jalilvand, et al., 2011)

The reason why perceived quality is different to real quality is because (a) a previous bad image of a product will influence consumers’ judgment on product quality in the future. Moreover, even the product quality has been changed, consumers will not trust that product because of their unpleasant experience in, (b) manufacturers and consumers have different views on the judgment of the quality dimensions, (c) consumers seldom hold enough information to evaluate a product objectively. Though consumers have enough information, they may be insufficient in time and motivation to do a further judgment, and in the end they can only select little important information and make an evaluation on quality. Perceived quality will be affected by factors such as previous experience, education level, and perceived risk and situational variables such as purchase purpose, purchase situation, time pressure, and social background from consumers. In sum, perceived quality is a consumer subjective judgment on product quality, and he or she will

evaluate product quality from their previous experiences and feelings. (Chi, et al., 2009)

A brand will have associated with it a perception of overall quality not necessarily based on a knowledge of detailed specifications. The quality associated with a brand can also be a strong factor of differentiation and positioning. Building a strong durable brand implies nevertheless an above average quality positioning or at least a minimum perceived quality when considering brands positioned as low market competitors. Perceived quality can also attract channel member interest, allow extensions and support a higher price that provides resources to reinvest in the brand. (Moisescu, 2005)

A perception of a quality will have a direct impact to the potential buyer, as for example if a customer form a perception that hand phones from brand A are good, it would form a higher possibilities for a customer to bought a product from a certain brand. In general, the impression of quality may produce the following values (Aaker, 2006 as cited by Rinto, 2013):

1. Reasons to Buy Limitations of information, money, and time to make one’s purchase decision is influenced by the quality of a brand impression in the minds of consumers, so often the reason a buying decision based only on the impression of quality (perceived quality) of the brand to be bought.

2. Differentiation/ Positioning One of the most important characteristics of the product brand is the position in the dimensions of the impression of quality.

3. Price Premium One of the advantage of giving the impression of quality is the choice of space in determining premium rates. Price premium can increase the profit that can directly improve profitability.

4. Interest in Distribution Channels Retailers and distributors will be motivated to deliver high-quality brands. This provides an advantage for the expansion of the distribution of these brands can also enhance the image of a channel distributor of brands that have a high quality impression.

5. Expansions of Trademark Impression of the brand with high quality can be used to introduce new product categories and have a greater chance of success than the weak brand. In this case the impression of quality is significant guarantee for brand extensions.

2.1.5 Brand Associations

A brand association is “anything linked in memory to a brand”. Aaker (1991) as cited by Jalilvand, et al. (2011) argued that a brand association has a level of strength, and that the link to a brand (from the association) will be stronger when it is based on many experiences or exposures to communications, and when a network of other links supports it. Brand associations may reflect characteristics of the product. Product associations and organizational associations are taken as the two mostly referred categories according to Chen’s (2001) brand association typology. Further, Aaker (1991) suggested that brand associations could provide value to the consumer by providing a reason for consumers to buy the brand, and by creating positive attitudes/ feelings among consumers. Rio, et al. (2001) proposes that brand associations are a key element in brand equity formation and management. In this respect, high brand equity implies that consumers have strong positive associations with respect to the other brand. (Jalilvand, et al., 2011)

Two identical products may create a different effect in using only because their brand’s association differ. Associations can be critical factors in differentiating and positioning, creating a reason to buy to those potential customers who are looking for specific associated physical or emotional features. If a brand is well positioned upon a key product attribute the attempt of a frontal assault by claiming superiority via that dimension will be a credibility failure, thus an association being a barrier

to competitors. A strong associations may be also the basis of a brand extension providing significant competitive advantage in the targeted area. (Moisescu, 2005)

According to Keller (2003) as cited by Rinto (2013), brand association consist of various types which could categorize in three, which are:

1. Attribute Attribute describes about a characteristic of a product or service in the time of purchasing or consumption. Attributes can be distinguished according to how directly they relate to product or service performance. Along these lines, attributes can be classified into product related and non-product related attributes.

a. Product Related Product related attributes are defined as the ingredients necessary for performing the primary product or service function sought by consumers. Hence, they relate to a product’s physical composition or a service’s requirements.

Product related attributes determine the nature and level of product performance. Product related attributes can be further distinguished according to essential ingredients and optional features, either necessary for a product to work or allowing for customization and more versatile personalized usage.

b. Non-Product Related Non-product related attributes are defined as external aspects of the product or service that relate to its purchase or consumption. Non- product related attributes may affect the purchase or consumption processes but do not directly affect the product performance. The examples of non-product related attributes are price information, packaging or product appearance information, country of origin, and user imagery.

2. Benefits Benefits are the personal value and meaning that consumers attach to the product or service.

a. Functional Benefit Functional benefits is the benefit which may obtain directly with product performance.

b. Experiential Benefit Experiential benefit relate to what is felt when the product or service is used and usually also correspond to both product-related as well as non- product related attributes such as usage imagery.

c. Symbolic Benefit They usually correspond to non-product related attributes and relate to underlying needs for social approval or personal expression. Symbolic benefits are especially relevant for socially visible products.

3. Attitude Brand attitudes are defined in terms of consumers’ overall evaluations of a brand. Brand attitudes are important because they often form the basis for actions and behavior that consumers take with the brand. Consumers’ brand attitudes generally depend on the specific considerations concerning the attributes and benefits of the brand. It is important to note that brand attitudes can be formed on the basis of benefits about product-related attributes and functional benefits and/ or beliefs about non-product related attributes, symbolic and experiential benefits.

Furthermore, brand association and image will be associated with the brand positioning. According to Aaker (2006 as cited by Rinto, 2013) brand association can be a plus:

a. Help the process or return information, the association may be a summary of the facts and specifications for the consumer in mind.

b. Differentiation, the association became an important basis for differentiation by forming a unique and distinctive associations which can be a key of competitive advantage.

c. Reason to buy, the association or the product attributes is often become the reason for consumers to buy the product. Creating positive attitudes or feelings, some associations to be a very desirable and stimulate a positive attitude which is then transferred to the brand. The basis for the extension, the association could also be the basis to extend or add a new product, which is by creating a sense of fit and comfort of a brand name with the new product.

2.1.6 Brand Loyalty

Brand loyalty is a situation which reflects how likely a customer will be to switch to another brand, especially when that brand makes a change, either in price or in product features (Aaker, 1991 as citeed by Jalilvand et al., 2011). There are another perspective regarding brand loyalty which are: behavioral, attitudinal, and choice perspective (Javalgi & Moberg, 1997 as cited by Jalilvand et al., 2011).

Behavioral perspective is based on the amount of purchases for a particular brand, attitudinal perspective incorporates consumer preferences and dispositions towards brands. Definitions regarding the choice perspective focus on the reasons for purchases or the factors that may influence choices.

These brand loyalty definitions were empirically researcher under three major categories: multi domain approach, behavioral approach, and attitudinal approach (Rundle-Thiele and Bennett, 2001 as cited by Jalilvand et al., 2011). Oliver (1997) defines brand loyalty as a deeply held commitment to rebuy or re-patronize a preferred product or service consistently in the future, despite situational influences and marketing efforts having potential to cause switching behavior. Oliver’s definition emphasizes the behavioral dimension of brand loyalty, whereas Rossiter and Percy (1987) argued that brand loyalty is often characterized by a favorable attitude towards a brand and repeated purchases of the same brand over time. Brand

loyalty is also conceptualized based on an attitudinal perspective. (Jalilvand et al, 2011)

From an attitudinal perspective, brand loyalty was defines as “the tendency to be loyal to a focal brand, which is demonstrated by the intention to buy the brand as a primary choice” (Yoo & Donthu, 2001 as cited by Jalilvand et al., 2011).

Brand loyalty can be separated from short term loyalty and long term loyalty. Short term loyalty is not a real brand loyalty because a long term customer will not buy other brands even if there is a better choice. Brand loyalty can be measured in two dimensions: affective loyalty and action loyalty. Affective loyalty is a specific brand preference from accumulative satisfaction to previous using experiences. However, affective loyalty just represents that of a repurchase intention. It does not mean that consumers will take purchase action. (Chi, et al., 2009)

Brand loyalty generates value by reducing marketing costs and leveraging trade. Loyal customers expect the brand to be always available and entice others advising them to use it. Retaining existing customers is much less costly than attracting new ones and even if there are low switching costs there is a significant inertia among customers. It is also difficult for competitors to communicate to satisfied brand users because they have little motivation to learn about alternatives. Therefore competitors may be discouraged from spending resources to attract satisfied and loyal customers and even if they do so, there is plenty of time to respond accordingly to that action. (Moisescu, 2005)

There are five levels of brand loyalty (Durianto, 2001 as cited by Tobing, 2014) which:

1. Price Buyer Customers who are on this level of loyalty can be said as the customer that located at the most basic level. The higher the customer frequency that move the purchases from one brand to another brand indicates the buyers are not loyal or not interested in the brand.

2. Habitual Buyer Purchases that are within the level of loyalty can be categorized as the satisfied buyer for the brand product consumed. At this level, basically here are no enough reason to create a desire for purchasing product of other brand or brand switching, especially if the transition requires effort, cost, and sacrifices.

3. Satisfied Buyer Buyers of brand are in satisfied category, when consumers consume brands, though buyers can move the intention to buy other brands with associated costs for the transition of time, money, or performance risk attached with the switching brand action.

4. Like the Brand Buyers who fall into this category are buyers who really like the brand. At this level, there is emotional feelings towards the brand. Sense of likeness of a buyer could be constituted by the association related to the symbol, a series of prior experience in using the products (personal experience) or caused by the perception of high quality of the product.

5. Committed Buyer Buyers have a pride as a user of the brand, even the brand becomes very important to express who they are.

2.1.7 Consumer Purchase Intention

Purchase intention is a kind of decision in which studied why a customer purchases a brand in particular. Constructs like considering something purchasing a brand and anticipating to purchase a brand aids to scope the intentions of purchasing (Porter, 1974 as cited by Shah, et al., 2012). Porter (1974) as cited by Shah, et al. (2012) also elaborated consumers’ intention to purchase a focused brand is not merely by his same brand attitude, but also by attitudes leading to other brands in choice of set considered. (Shah, et al., 2012)

Consumer response to the marketing mix of a brand can be translated at various stages of the purchase decision making sequence such as preference, choice intentions and actual choice (Christodoulides and Chernatony, 2009). Purchase intention can measure the possibility of a consumer to buy a product, and the higher the purchase intention is, the higher a consumer’s willingness is to buy a product (Dodds, et al., 1991; Schiffman & Kanuk, 2000). Purchase intention indicates that consumers will follow their experience, preference and external environment to collect information, evaluate alternatives, and make purchase decision (Dodds, et al., 1991; Schiffman & Kanuk, 2000; Yang, 2009; Chi, et al., 2011).

There are two factors that can influence the purchase intention, the first factor is the attitude of the other, the extent to which the attitudes of others toward one’s preferred alternative. The second factor is the situation is not expected. Consumers may form the intention to buy based on factors such as expected price. However, unexpected events can change the intention to buy. Consumers make choices and purchase intentions do not always result into actual choice. (Kotler, 2007)

Consumers’ brand attitude and purchase intention will be higher when a product has high preference image and familiarity (Kamins and Marks, 1991; Laroche, et al. 1996). Monroe and Krishnan (1985) submitted that perceived value and perceived quality will influence purchase intention, and the more perceived value and perceived quality, the higher the purchase intention is. (Chi, et al., 2011)

2.1.8 Relationship between Concepts

Brand awareness plays an important role in consumers purchase intention as it can increase the familiarity of consumer towards the brand, which can then lead to possible purchase. Brand associations affect the basis for purchase intention towards a brand as it helps consumer to obtain information, distinguish the brand, generates reason to purchase, and creates positive values/attitudes to the firms and their consumers. Similarly, perceived quality also provides values to the consumers as it can be the point of differentiation and reason to purchase. Finally, brand loyalty will make consumers do not only repeat buying but also make them invulnerable to

any brand changes such as change of price or product features. (Santoso & Cahyadi, 2014)

2.2 Previous Research

1. Mohammad Reza Jalilvad, Neda Samiei, and Seyed Hessamaldin Mahdavinia (2011) with research titled “The Effect of Brand Equity Components on Purchase Intention: An Applications of Aaker’s Model in the Automobile Industry” the study adopt Aaker’s brand equity model and in that research, they find that each dimensions of brand equity (brand awareness, perceived quality, brand association, and brand loyalty) possess simultaneous significant influence towards consumer purchase intention.

2. Cynthia Ratna Santoso and Tabita Ella Cahyadi (2014) with research entitled “Analying the Impact of Brand Equity towards Purchase Intention in Automotive Industry: A Case Study of ABC in Surabaya” this study use Aaker’s brand equity theory, covering brand awareness, brand association, perceived quality, and brand loyalty. Multiple Linear Regression Analysis and comparative analysis is employed in this study. They find that despite the result where brand equity simultaneously has a significant influence towards purchase intention, only brand association and brand loyalty that possess significant influence towards purchase intention partially.

3. Kristedy Rinto (2013) with research entitled “The Influence of Brand Awareness, Perceived Quality, and Brand Association on Customer Purchase Decision (A Case Study at President University Extension Program Batch 2012)” use brand awareness, perceived quality, and brand association (adopted from Aaker, 2006) excluding brand loyalty from the original model as the independent variable with customer purchase decision as the dependent variable. The result of this study is that the independent variables used (brand awareness, perceived quality, and brand association) influence customer purchase decision. Although the dependent variable is different than the researcher’s (which is consumer purchase intention), purchase intention occurs a step before purchase decision is make, therefore 26

in this study, those three dimensions used are also affecting consumer purchase intention (in this case, to even consider to study in President University).

4. Hardian Ruben Hasiholan Lumban Tobing (2014) with research entitled “Impact of Brand Equity on Customer Purchase Intention (Nike Shoes as a Study Case)” this study adapt the four dimensions of brand equity which are: brand awareness, perceived quality, brand association, and brand loyalty and analyzing it impact towards customer purchase intention. The result of this study shows that only perceived quality has an impact towards customer purchase intention.

5. Suriansen Wirianto (2013) with research entitled “The Analysis of Influences of Brand Equity and Advertising toward Pocari Sweat’s Purchasing Intention (Case Study in Citywalk, Jababeka)” shows that all dimensions of brand equity have an influence towards consumer purchase intention. In specific, brand awareness and brand association have negative influence while brand loyalty and perceived quality have positive influence towards consumer purchase intention.

6. Muhammad Iqbal Hardiansyah (2013) with research entitled “The Effect of Brand Equity towards Students Decision in Choosing President University as Their College (Study Case President University Students Batch 2012)” find that brand awareness, perceived quality, brand association and brand loyalty has an influence towards consumer purchasing decision (in this case is about choosing a university), with brand loyalty has the highest influence.

7. Nabila Hilmy Zhafira (2014) with research entitled “The Analysis of Marketing Mix and Brand Equity on Customers’ Buying Decision A Survey of Tea bag Consumers in Jakarta” Brand equity dimensions used in this study are: brand awareness, perceived quality, and brand loyalty. This study find that there is a simultaneous influence of brand equity towards consumer purchase decision. There are also partial influence from each dimensions of brand equity used in this study.

2.3 Theoretical Framework

Figure 2.3 Theoretical Framework – 4 Dimensions of Brand Equity Source : Aaker (1991) as cited by Jalilvand, et al. (2011)

The four dimensions presented in the theoretical framework were proposed by Aaker (1991) as cited by Spry, et al. (2011) and used in many studies following his framework. The consumer purchase intention is the phase where there is an intention to make a purchase from the side of the consumer, whether the actual purchase transaction is conducted or not is treated as consumer purchasing or buying behavior. This framework will only cover the consumer purchase intention.

Four dimensions of brand equity: brand awareness, perceived quality, brand association, and brand loyalty, are independent variables with consumer purchase intention as the dependent variable. In this framework, each dimension will be analyzed its relationship with consumer purchase intention, whether there is a relationship or not. After each dimension are analyzed individually, all the four

dimensions will be then analyzed as a whole (brand equity) to analyze the strength of the relationship in the overall (if available).

2.4 Operational Variables

Variable Definition Measurement Type

Brand I can recognize Nokia/ awareness refers Microsoft Lumia to whether smartphones among other competing brands consumers can I am aware of Nokia/ recall or Microsoft Lumia recognize a smartphones Brand brand, or simply I am familiar with Likert-Scale Awareness whether or not Nokia/ Microsoft consumers Lumia when I see it know about a from its logo brand (Keller, Nokia/ Microsoft Lumia brand is famous 2008 as cited by Huang & Nokia/Microsoft Sarigöllü, 2012) Lumia brand is well known Perceived Nokia/ Microsoft quality is not the Lumia smartphones have good quality actual quality of Nokia/ Microsoft the product but Lumia smartphones the consumer’s have consistent quality Perceived subjective Nokia/ Microsoft Likert-Scale Quality evaluation of the Lumia smartphones are product very durable (Zeithaml, 1988 Nokia/ Microsoft Lumia smartphones are as cited by very reliable Jalilvand, et al., Nokia/ Microsoft 2011) Lumia smartphones have excellent features A brand Some characteristics of association is Nokia/ Microsoft Brand Lumia come to my “anything linked Likert-Scale mind quickly Associations in memory to a I can always trust on brand. (Aaker, Nokia/ Microsoft 1991 as cited by Lumia smartphone

Jalilvand, et al., brand if I want a 2011) product of high quality Nokia/ Microsoft Lumia smartphones provide good value for the money Nokia/ Microsoft Lumia is the brand made by an organization I would trust Nokia/Microsoft Lumia is different from competing brands I consider myself to be loyal to Nokia/ Brand loyalty is Microsoft Lumia brand a situation Nokia/ Microsoft which reflects Lumia would be my how likely a first choice customer will be I will not buy other to switch to brands if Nokia/ another brand, Microsoft Lumia is

especially when available at the store Brand Loyalty Even if another brand Likert-Scale that brand has the same features makes a change, as Nokia/ Microsoft either in price or Lumia, I would prefer in product to buy Nokia/ features (Aaker, Microsoft Lumia 1991 as citeed If there is another by Jalilvand et brand as good as Nokia/ Microsoft al., 2011). Lumia, I prefer to buy Nokia/ Microsoft Lumia Purchase I would likely buy a Nokia/ Microsoft intention is a Lumia brand for my kind of decision next purchase of in which studied smartphone Purchase why a customer I would probably buy a Likert-Scale Intention purchases a Nokia/ Microsoft brand in Lumia brand for my particular. next purchase of smartphone Constructs like I would certainly buy a considering Nokia/ Microsoft 30

something Lumia brand for my purchasing a next purchase of brand and smartphone anticipating to I would definitely buy a Nokia/ Microsoft purchase a Lumia brand for my brand aids to next purchase of scope the smartphone intentions of purchasing My willingness to buy Nokia/ Microsoft (Porter, 1974 as Lumia smartphone for cited by Shah, et my next purchase is al., 2012). high

2.5 Hypothesis

1) Partial significance influence of brand awareness to consumer purchase intention Ho1: There is no significance influence of brand awareness towards consumer purchase intention Ha1: There is a significance influence of brand awareness towards consumer purchase intention 2) Partial significance influence of perceived quality to consumer purchase intention Ho2: There is no significance influence of perceived quality towards consumer purchase intention Ha2: There is a significance influence of perceived quality towards consumer purchase intention 3) Partial significance influence of brand association to consumer purchase intention Ho3: There is no significance influence of brand association towards consumer purchase intention Ha3: There is a significance influence of brand association towards consumer purchase intention

4) Partial significance influence of brand loyalty to consumer purchase intention Ho4: There is no significance of brand loyalty towards consumer purchase intention Ha4: There is a partial significance of brand loyalty towards consumer purchase intention 5) Simultaneous significance influence of brand awareness, perceived quality, brand association, brand loyalty, and consumer purchase intention Ho5: There is no simultaneous influence of brand awareness, perceived quality, brand association, and brand loyalty towards consumer purchase intention Ha5: There is a simultaneous influence of brand awareness, perceived quality, brand association, and brand loyalty towards consumer purchase intention

CHAPTER III

METHODOLOGY

3.1 Research Design

This study will implement quantitative research method; quantitative research is about asking people for their opinion in a structured way so that hard facts and statistics could guide the overall research process. Simple random sampling design with Slovin’s formula to calculate the sample size will be implemented. The study is focused at President University students of batch 2011 as the targeted respondents. According to figure 1.2, smartphone users are concentrated at young- adult groups which is suitable for college setting and with the assumption that students of batch 2011 which is in the median of that age group already shows the individuality in purchase decision in their daily life.

The primary data will be obtained from questionnaire with likert-scale type questions which were adapted from Pappu, et al. (2005) and also Yoo and Donthu (2001).

There are five variables which are measured in this study. Four variables are independent variables as the indicators are Brand Awareness (AW), Perceived Quality (PQ), Brand Association (AS), Brand Loyalty (LO). Another variables which is dependent variable as the indicator is Consumer Purchase Intention (PI).

Secondary data will be obtained from related journal articles, periodical article, books, and web sources with previous studies as further references in this study.

3.2 Sampling Design

3.2.1 Population

Population is the complete collection to be studied (Kowalczyk). A population is any complete group with at least one characteristics in common. Populations are not just people. Populations may consist of, but are not limited to, people, animals, business, buildings, motor vehicles, farms, objects or events (Australian Bureau of Statistics, 2013). This study is aimed to analyze the significance of brand equity towards consumer purchase intention among President University students, specifically students of batch 2011. The population of this study is 988.

3.2.2 Sample

A section or part of the population (Kowalczyk). Which is a section or a part of the population of this study which is 988. Samples are taken because it is next to impossible to be able to obtain responses from all member of the population.

To calculate the sample size of this study, Slovin’s formula will be implemented in this study. Slovin’s formula is used when the population’s behavior is not known or understood.

n = N / (1 + Ne^2) n = Number of Samples N = Total population e = Error tolerance

Error tolerance that will be used in this study is 10% or 0.1 which will provide 90% confidence level. The total population would be President University students of batch 2011 with total of 988 students. The number of samples will be used for this study using Slovin’s formula would be: n = 988 / (1 + 988 (0.1)^2) = 988 / (1 + 988 (0.001) = 988 / 10.88 = 91 samples 34

If in the pilot testing, all items measured are valid, 30 samples that were used in the pilot testing will be included in the total samples that are going to be analyzed and interpreted in this study. If there is an invalid item, the samples used in the pilot testing will be dropped. The number of sample is in accordance with a ratio of minimun ten respondents per parameter was considered most appropriate for structural equation modelling (Hair, Anderson, Tatham, & Black, 1998).

3.3 Research Framework

Figure 3.1 Research Framework Source : Self-Construct by the researcher 36

3.4 Research Instrument

Research instrument is the tool that used to answer the research questions that stated in the previous chapter, which also used to gather, examine, investigate an issue or collecting, process, analyze and present the data in a systematic and objective in order to solve the problem or to test a hypothesis. The researcher intention is to gather the information from the sources.

3.4.1 Primary Data

Questionnaire is implemented as the primary source of data in this study. The questionnaire is adapted from various sources which are: Pappu, et al. (2005) and also Yoo and Donthu (2001).

The questionnaire will use five points likert-scale type questions. The likert-scale’s invention is attributed to Rensis Likert in 1931, who described this technique for assessment of attitudes. Likert-scale is a set of items, composed of approximately an equal number of favorable and unfavorable statements concerning the attitude object, is given to a group of subjects. They are asked to respond to each statement in terms of their own degree of agreement or disagreement. Typically, they are instructed to select one of five responses: strongly agree, agree, undecided, disagree, or strongly agree. The specific responses to the items are combined so that individuals with the most favorable attitudes will have the highest scores while individuals with the least favorable (or unfavorable) attitudes will have the lowest scores. While not all summated scales are created according to Likert’s specific procedures, all such scales share the basic logic associated with Likert scaling.

Here is the interpretation of likert-scale type questions used in this study:

Table 3.1 Likert-Scale Interpretation Scale Response 1 Strongly Disagree 2 Disagree 3 Neutral 4 Agree 5 Strongly Agree Source : Yoo & Donthu (2001)

The questionnaire are separated into 3 (three) parts which consisted of:

1. Demographic information 2. Four dimensions of brand equity (brand awareness, perceived quality, brand association, brand loyalty) 3. Consumer purchase intention

There are 5 items in point number 2 (four dimensions of brand equity) and point number 3 (consumer purchase intention). For simplifying purpose, the items will be in code in accordance with their respective name: AW (Brand Awareness); PQ (Perceived Quality); AS (Brand Association); LO (Brand Loyalty); PI (Consumer Purchase Intention).

The factors are measured by the items illustrated below:

Table 3.2 Measurement Items in the Questionnaire Code Measurement Item References I Brand Awareness AW1 I can recognize Nokia/ Microsoft Lumia smartphones among other competing brands Pappu, et al. (2005); AW2 I am aware of Nokia/ Microsoft Lumia Yoo and Donthu (2001); smartphones Oh (2000) AW3 I am familiar with Nokia/ Microsoft Lumia when I see it from its logo

AW4 Nokia/ Microsoft Lumia brand is famous AW5 Nokia/Microsoft Lumia brand is well known II Perceived Quality PQ1 Nokia/ Microsoft Lumia smartphones have good quality PQ2 Nokia/ Microsoft Lumia smartphones have consistent quality PQ3 Nokia/ Microsoft Lumia smartphones are Pappu, et al. (2005) very durable PQ4 Nokia/ Microsoft Lumia smartphones are very reliable PQ5 Nokia/ Microsoft Lumia smartphones have excellent features III Brand Association AS1 Some characteristics of Nokia/ Microsoft Lumia come to my mind quickly AS2 I can always trust on Nokia/ Microsoft Lumia smartphone brand if I want a product of high quality AS3 Nokia/ Microsoft Lumia smartphones Yoo and Donthu (2001) provide good value for the money AS4 Nokia/ Microsoft Lumia is the brand made by an organization I would trust AS5 Nokia/Microsoft Lumia is different from competing brands IV Brand Loyalty LO1 I consider myself to be loyal to Nokia/ Microsoft Lumia brand Pappu, et al. (2005); LO2 Nokia/ Microsoft Lumia would be my first Yoo and Donthu (2001) choice

LO3 I will not buy other brands if Nokia/ Microsoft Lumia is available at the store LO4 Even if another brand has the same features as Nokia/ Microsoft Lumia, I would prefer to buy Nokia/ Microsoft Lumia LO5 If there is another brand as good as Nokia/ Microsoft Lumia, I prefer to buy Nokia/ Microsoft Lumia V Consumer Purchase Intention PI1 I would likely buy a Nokia/ Microsoft Lumia brand for my next purchase of smartphone PI2 I would probably buy a Nokia/ Microsoft Lumia brand for my next purchase of smartphone PI3 I would certainly buy a Nokia/ Microsoft Lumia brand for my next purchase of Jiang, et al. (2010) smartphone PI4 I would definitely buy a Nokia/ Microsoft Lumia brand for my next purchase of smartphone PI5 My willingness to buy Nokia/ Microsoft Lumia smartphone for my next purchase is high

Source : Self-Construct by the Researcher

3.4.2 Secondary Data

Secondary data is information that has been collected for a purpose other than your current research project but has some relevance and utility for your research. (Grimsley, n.d.). Secondary data could be divided into two based on the sources:

1. Internal Sources = data that exist and is stored inside your organization 2. External Sources = information collected and stored by some person or organization outside of your organization

This study will mainly use external sources from other studies conducted in similar manner by other researcher. External sources includes: journal articles, web page, and books. It is not limited, however, only to external sources since internal sources is also utilized which includes: research conducted by seniors in President University and book provided in the Adam Kurniawan Library in President University.

3.5 Validity and Reliability

3.5.1 Validity

Validity encompasses the entire experimental concepts and establishes whether the results obtained meet all of the requirements of the scientific research method. Internal validity dictates how an experimental design is structured and encompasses all of the steps of the scientific research method. External validity is the process of examining the results and questioning whether there are any other possible causal relationships. Internal validity and reliability are at the core of any experimental design. (Shuttleworth, 2013)

This study will employ Pearson Product-Moment Correlation Coefficient for its validity testing instrument.

The Pearson product-moment correlation coefficient (or Pearson correlation coefficient, for short) is a measure of the strength of a linear association between two variables and is denoted by r. Basically, a Pearson product-moment correlation

attempts to draw a line of best fit through the data of two variables, and the Pearson correlation coefficient, r, indicates how far away all these data points are to this line of best fit (how well the data points fit this new model/line of best fit).

The Pearson correlation coefficient, r, can take a range of values from +1 to -1. A value of 0 indicates that there is no association between the two variables. A value greater than 0 indicates a positive association; that is, as the value of one variable increases, so does the value of the other variable. A value less than 0 indicates a negative association; that is, as the value of one variable increases, the value of the other variable decreases.

The stronger the association of the two variables, the closer the Pearson correlation coefficient, r, will be to either +1 or -1 depending on whether the relationship is positive or negative, respectively. Achieving a value of +1 or -1 means that all your data points are included on the line of best fit - there are no data points that show any variation away from this line. Values for r between +1 and -1 (for example, r = 0.8 or -0.4) indicate that there is variation around the line of best fit. The closer the value of r to 0 the greater the variation around the line of best fit. (Laerd Statistics, 2015)

Correlation r formula:

For any two variables, X and Y, the correlation coefficient between them is given by the formula:

푛(Σ푥푦) − (Σ푥)(Σ푦) 푟 = √[푛Σ푥2 − (Σ푥)2][푛Σ푦2 − (Σ푦)2]

Where: r = correlation between two variables n = number pair of scores

Σ xy = sum of the products of pair scores

Σ x = sum of x scores

Σ y = sum of y scores

Σ x2 = sum of squared x scores

Σ y2 = sum of squared y scores

SPSS 21.0 will be employed to run Pearson Product-Moment Correlation (Pearson Correlation, in short). Since the pilot testing will be using 30 samples, based on the r-table related to the Pearson Product-Moment Correlation test is .361 (see APPENDICES). This result comes from the calculation of “df (N – 2)” and the df is 30 which means the data chosen will be from 28 with α = .5.

3.5.2 Reliability

The idea behind reliability is that any significant results must be more than a one- off finding and be inherently repeatable. Other researchers must be able to perform exactly the same experiment, under the same conditions and generate the same results. This will reinforce the findings and ensure that the wider scientific community will accept the hypothesis.

Without this replication of statistically significant results, the experiment and research have not fulfilled all of the requirements of testability.

This prerequisite is essential to a hypothesis establishing itself as an accepted scientific truth. (Shuttleworth, 2013)

This study will employ Cronbach’s Alpha as its reliability testing instrument. The formula of Cronbach’s Alpha is as follows:

푛 푥r̅ 훼 = 1 + (푛 − 1)푟̅

Where:

α = reliability of the average or sum n = number of items r̅ = average correlation between any two items

Cronbach’s alpha is a test reliability technique that requires only a single test administration to provide a unique estimate of the reliability for a given test. Cronbach’s alpha is the average value of the reliability coefficients one would obtained for all possible combinations of items when split into two half-tests.

While most individuals utilizing Likert-type scales will report overall scale and subscale internal consistency reliability estimates in the analysis of the data, many will analyze individual scale items. The majority of individuals correctly reported Cronbach’s alpha as the measure of internal consistency reliability, but then opted to conduct data analysis using individual items. This is particularly troubling because single item reliabilities are generally very low, and without reliable items the validity of the item is poor at best and at works unknown. (Gliem & Gliem, 2003).

Table 3.3 Cronbach’s Alpha Interpretation Table Cronbach’s Alpha Internal Consistency α ≥ 0.9 Excellent 0.8 ≤ α < 0.9 Good 0.7 ≤ α < 0.8 Acceptable 0.6 ≤ α < 0.7 Questionable 0.5 ≤ α < 0.6 Poor α < 0.5 Unacceptable

Source : George and Mallery (2003)

3.6 Descriptive Statistic

Descriptive statistics is the term given to the analysis of data that helps describe, show or summarize data in a meaningful way such that, for example, patterns might emerge from the data. Descriptive statistics do not, however, allow us to make conclusions beyond the data we have analysed or reach conclusions regarding any hypotheses we might have made. They are simply a way to describe our data.

Descriptive statistics are very important because if we simply presented our raw data it would be hard to visulize what the data was showing, especially if there was a lot of it. Descriptive statistics therefore enables us to present the data in a more meaningful way, which allows simpler interpretation of the data. (Laerd Statistics, 2015)

There are four main descriptive statistics items that are employed in this study which are:

a. Mean: the sum of the observations divided by the total number of observations. It is the most common indicator of central tendency of a variable. (Torres-Reyna, 2008)

With the formula as follows:

X̅ = Σ x / n

Where:

Σ x = the sum of all sample observations

n = the number of sample observations

b. Standard deviation: the squared root of the variance. Indicates how close the data is to the mean. Assuming a normal distribution. (Torres-Reyna, 2008)

With the formula as follows:

2 s = Σ (xi – x̅ ) / (n – 1)

Where:

xi = the ith element from the sample

x̅ = the sample mean

n = the number of elements in the sample

c. Min: the lowest value in an array of values. (Torres-Reyna, 2008) d. Max: the highest value in an array of values. (Torres-Reyna, 2008)

3.7 Classical Assumption Test

Classical assumptions test are requirement tests for multiple linear regression that use Ordinary Least Square (OLS) techniques. Classical assumptions test isn’t needed in linear regression that use to count a value in a variable. There are five types of test in classical assumptions but in this study only four types of test will be used which are:

1. Normality test: to know sample data distribution comes from population that is normally distributed or not. Central limit theorem said that big sample size (>25) tend to be normal distributed. The methods used to detect normality in this study are: Graph techniques (Histogram, Q-Q Plot) and, Skewness and Kurtosis analysis with the calculation of z-value.

2. Multicolinearity test: to know high correlation between independent variable in multiple linear regression test. High collinearity between independent variables will disturb relationship between independent and dependent variable. There are sev0eral methods to detect multicollinearity 46

which are: Variance Inflation Factor (VIF) > 10, Eigen Values and Condition Indexes (CI).

3. Heteroscedasticity test: to identify variance differences from residual in an observation with other observations. Regression model should have a residual variance similarity between observations with other observation (homoscedastic). There are several methods to detect heteroscedasticity which are: Graph Technique (Scatter Plot).

4. Autocorrelation test: the aim of this test is to examine if there is a correlation in linear regression model in the t-period with the previous period (t-1 period) and if there is a correlation in the test means there is an autocorrelation problem. In this research, researcher employ Durbin- Watson (DW) test in order to know the presence of autocorrelation. The value of Durbin-Watson lies between 0 and 4. The interpretations of Durbin – Watson (DW) test are:

a. If d = 2, there is no autocorrelation

b. If d < dL, there is statistical evidence that the error terms are positively autocorrelated

c. If d > dU, there is no statistical evidence that the error terms are positively autocorrelated.

d. If (4-d) < dL, there is statistical evidence that the error terms are negatively autocorrelated

e. If (4-d) > dU, there is no statistical evidence that the error terms are negatively autocorrelated

Negative serial correlation implies that a positive error for one observation increases the chance of a negative error for another observation and a negative error for one observation increases the chances of a positive error for another.

3.8 Multiple Linear Regression

Multiple linear regression attempts to model the relationship between two or more explanatory variables and a response variable by fitting a linear equation to observed data. Every value of the independent variable x is associated with a value of the dependent variable y. The population regression line for p explanatory variables x1, x2, ... , xp is defined to be y = 0 + 1x1 + 2x2 + ... + pxp. This line describes how the mean response y changes with the explanatory variables.

The observed values for y vary about their means y and are assumed to have the same standard deviation . The fitted values b0, b1, ..., bp estimate the parameters

0, 1, ..., p of the population regression line.

Since the observed values for y vary about their means y, the multiple regression model includes a term for this variation. In words, the model is expressed as DATA

= FIT + RESIDUAL, where the "FIT" term represents the expression 0 + 1x1 +

2x2 + ... pxp. The "RESIDUAL" term represents the deviations of the observed values y from their means y, which are normally distributed with mean 0 and variance . The notation for the model deviations is .

Formula for multiple linear regression is

yi = 0 + 1xi1 + 2xi2 + ... pxip i for i = 1,2, ... n.

When adjusted to this study, the equation is constructed of:

Y = Consumer Purchase Intention

β0 = Constanta

푥1 = Brand Awareness

푥2 = Perceived Quality

푥3 = Brand Association

푥4 = Brand Loyalty 3.9 Data Collection Procedure

Before the main survey is going to be executed, there would be a pilot testing to ensure the questions validity and reliability and the number of samples intended for the pilot testing would be 30 samples. The samples in the pilot testing will be then included in the total of 91 samples required for this study. The survey is conducted online by distributing the link to the online form, the responses will be then stored in the online storage for further purpose (this procedure is for both pilot testing and main process).

3.10 Hypothesis Testing

3.10.1 T-Test

T-test are used to compare two means to assess whether they are from the same population. T-test presume that both groups are normally distributed and have relatively equal variances. The t-statistic is distributed on a curve that is based on the number of degrees of freedom (df). There are three kinds of t-test: independent samples, paired-samples, and one-sample.

푏푗 − 훽푗 푡 = 푆푏푗

Where: t = statistic test for t-distribution bj = sample slope

βj = slope of the population

Sbj = standard error of the slope

3.10.2 F-Test

Hypothesis involving multiple regression coefficients require a different test statistic and a different null distribution. The test statistic used for that analysis is test statistics F0 and its null distribution F-distribution. F-test is used to evaluate hypothesis that involved multiple parameters.

(푆푆푅푟 − 푆푆푅푢푟)/푞 퐹0 = 푆푆푅푢푟/(푛 − (푘 + 1))

Where:

SSRr = sum of the squared residuals of the restricted model

SSRur = sum of the squared residuals of the unrestricted model n = number of observations k = number of independent variables in the unrestricted model q = number of restrictions (or the number of coefficients being jointly tested)

This terminology may seem a bit strange at first. We are “restricting” the general model by imposing supposing that the null is true and removing variables from the model. Thus, the difference (SSRr−SSRur) is telling us how much bigger the residuals are in the model where the null hypothesis is true. If the residuals are a lot bigger in the restricted model, then F0 will also be big. When the residuals are bigger, we know that this means the fit of the regression is worse. Thus, F0 is big when the restriction makes the fit of the regression a lot worse which is exactly when we would question the null hypothesis. If these variables really had no predictive power, then removing them should not affect the residuals. We will discuss how big F0 needs to be to reject the null hypothesis a bit later. (Blackwell, 2008).

3.10.3 R2 Test

Coefficient of determination or R-square is the number that shows the amount of variance in the dependent variable that could be explained by the independent variables. Normally, the coefficient of determination value is between 0 and 1.

The bigger the coefficient of determination, the superior the capability of independent variables to clarify the variance of dependent variable, thus the closer the coefficient of determination value to 1, the independent variables will provide more complete information to predict the variance of dependent variable (Ghozali, 2011).

CHAPTER IV

ANALYSIS AND INTERPRETATION

4.1 Company Profile

Microsoft Corporation is an American multinational corporation headquartered in Redmond, Washington, that develops, manufactures, licenses, supports and sells computer software, consumer electronics and personal computers and services. Its best known software products are the Microsoft Windows line of operating systems, Microsoft Office office suite, and Internet Explorer web browser. Its flagship hardware products are the Xbox game consoles and the Microsoft Surface tablet lineup. It is the world's largest software maker measured by revenues. It is also one of the world's most valuable companies.

Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975, to develop and sell BASIC interpreters for Altair 8800. It rose to dominate the personal computer operating system market with MS-DOS in the mid-1980s, followed by Microsoft Windows. The company's 1986 initial public offering, and subsequent rise in its share price, created three billionaires and an estimated 12,000 millionaires from Microsoft employees. Since the 1990s, it has increasingly diversified from the operating system market and has made a number of corporate acquisitions. In May 2011, Microsoft acquired Skype Technologies for $8.5 billion in its largest acquisition to date.

As of 2013, Microsoft is market dominant in both the IBM PC-compatible operating system and office software suite markets (the latter with Microsoft Office). The company also produces a wide range of other software for desktops and servers, and is active in areas including Internet search (with Bing), the video game industry (with the Xbox, Xbox 360 and Xbox One consoles), the digital services market (through MSN), and mobile phones (via the Windows Phone OS). In June 2012,

Microsoft entered the personal computer production market for the first time, with the launch of the Microsoft Surface, a line of tablet computers.

With the acquisition of Nokia's devices and services division to form Microsoft Mobile Oy, the company re-entered the smartphone hardware market, after its previous attempt, Microsoft Kin, which resulted from their acquisition of Danger Inc.

On February 4, 2014, Steve Ballmer stepped down as CEO of Microsoft and was succeeded by Satya Nadella, who previously led Microsoft's Cloud and Enterprise division. On the same day, John W. Thompson took on the role of chairman, with Bill Gates stepping down from the position to become more active within the company as Technology Advisor.

On April 25, 2014, Microsoft acquired Nokia Devices and Services and formed a new subsidiary, Microsoft Mobile Oy.

4.2 Data Analysis

This study will employ Multiple Linear Regression (MLR) analysis which is multivariate statistical technique for examining the linear correlations between two or more independent variables (IVs) and a single dependent variable (DV). MLR allows you to examine how multiple independent variables are related to a dependent variable. The independent variables in this study are: Brand Awareness, Perceived Quality, Brand Association, and Brand Loyalty. The dependent variable in this study is Consumer Purchase Intention. In processing the data, the researcher use two software which are SPSS 21.0 and Microsoft Office Excel 2013.

Prior to handling out questionnaires to the intended samples, the researcher conducted a pre-test in order to check the validity and reliability of the questionnaire.

4.2.1 Validity Test

30 samples is taken for the pre-test or pilot testing, the sample used in the pre-test or pilot testing will be included in the overall sample. The researcher use r-table to determine the validity of each statement in the questionnaire, in which each statement has to be at least 0.361 (see APPENDICES). Pearson Product-Moment Correlation Coefficient (or commonly stated as Pearson Correlation Coefficient) is used to test the validity and the result is shown on the table below:

Table 4.1 Pearson Product-Moment Correlation Test Result R-Computed R-Table Variable Item Status Value Value AW1 .811 .361 Valid AW2 .729 .361 Valid Brand AW3 .881 .361 Valid Awareness AW4 .709 .361 Valid AW5 .886 .361 Valid PQ1 .857 .361 Valid PQ2 .806 .361 Valid Perceived PQ3 .845 .361 Valid Quality PQ4 .633 .361 Valid PQ5 .675 .361 Valid AS1 .672 .361 Valid AS2 .679 .361 Valid Brand AS3 .544 .361 Valid Association AS4 .748 .361 Valid AS5 .613 .361 Valid LO1 .816 .361 Valid LO2 .871 .361 Valid Brand LO3 .871 .361 Valid Loyalty LO4 .840 .361 Valid LO5 .882 .361 Valid PI1 .924 .361 Valid PI2 .862 .361 Valid Purchase PI3 .886 .361 Valid Intention PI4 .809 .361 Valid PI5 .891 .361 Valid Source : Processed in SPSS 21.0

The table 4.1 shows that all statement in the questionnaire (25 out of 25) are valid. Along with this result, all statement used during the pre-test or pilot testing will be used for further data gathering and that also means that 30 samples used in the pre- test or pilot testing are viable to be included in the overall sample.

4.2.2 Reliability Test

Reliability test is conducted after validity has been done. There are 25 valid statements which are part of 4 independent variables and 1 dependent variable. Reliability test is going to employ Cronbach’s Alpha as its reliability test.

Table 4.2 Cronbach’s Alpha Test Result Cronbach’s Alpha Cronbach’s Alpha Based on Standardized N of Items Items .837 .851 5 Source : Processed in SPSS 21.0

The value of Cronbach’s Alpha is .837 which is according to George and Mallery (2003) categorization as Good. The score of .837 means that the true score variance or internally consistent variable in a composite score by combining those 5 items is 83.7%. Therefore, the data reliability exceeded the minimum value thus it is reliable.

With the data being reliable means that this study is more than a one-off finding and could be repeatable by other researchers under the same conditions and still generating the same results.

4.2.3 Descriptive Statistic

Descriptive statistic will be employed to evaluate the brand equity per variable, means value and standard deviations are used. The higher the mean value, the higher the brand equity is in the respective variable and vice versa. The standard deviation shows the difference among responses, the higher the standard deviation is the more varied the responses are and vice versa.

When the mean value is > 3, it shows that the respective variable is strong while if the mean value is ≤ 3, it shows that the respective variable is not strong or significant for the respondents.

4.2.3.1 Descriptive Statistic of Variable “Brand Awareness”

To evaluate the brand equity strength in term of “Brand Awareness”, basic statistic measurement such as mean value and standard deviation are used. The item of “AW” comes from the sum of 5 items of Brand Awareness (AW1 + AW2 + … + AW5) / 5. The mean value is then going to be observed whether the value is > 3 or ≤ 3. The result is shown on the table below (table 4.3).

Table 4.3 Descriptive Statistic of Variable “Brand Awareness” Result Std. N Minimum Maximum Mean Deviation AW1 91 1.00 5.00 3.9780 .94255 AW2 91 2.00 5.00 3.7912 .91307 AW3 91 1.00 5.00 3.9231 1.00256 AW4 91 2.00 5.00 4.0110 .76731 AW5 91 2.00 5.00 4.0110 .82320 AW 91 2.20 5.00 3.9429 .73229 Source : Processed in SPSS 21.0

The mean value of “Brand Awareness” is 3.9429 which means that the strength of brand awareness of Nokia/ Microsoft Lumia among respondents is strong in average (> 3). The standard deviation is .73229 which shows that the responses are quite centralized. In other words, there is not much difference between responses of customers to factor “Brand Awareness” regarding Nokia/ Microsoft Lumia.

Among the items of “Brand Awareness”, consumers agree the most with AW4 (“Nokia/ Microsoft Lumia brand is famous”) and AW5 (“Nokia/Microsoft Lumia brand is well known”) with mean = 4.0110 and agree the least with AW2 (“I am aware of Nokia/ Microsoft Lumia smartphones”) with mean = 3.7912.

That reveals that the difference between evaluations are very small, most of the items are highly evaluated.

4.2.3.2 Descriptive Statistic of Variable “Perceived Quality”

To evaluate the brand equity strength in term of “Perceived Quality”, basic statistic measurement such as mean value and standard deviation are used. The item of “PQ” comes from the sum of 5 items of Perceived Quality (PQ1 + PQ2 + … + PQ5) / 5. The mean value is then going to be observed whether the value is > 3 or ≤ 3. The result is shown on the table below (table 4.4).

Table 4.4 Descriptive Statistic of Variable “Perceived Quality” Result Std. N Minimum Maximum Mean Deviation PQ1 91 2.00 5.00 3.9121 .69360 PQ2 91 2.00 5.00 3.8791 .78649 PQ3 91 2.00 5.00 3.8681 .81933 PQ4 91 2.00 5.00 3.8571 .75383 PQ5 91 1.00 5.00 3.6154 1.00851 PQ 91 2.00 5.00 3.8264 .67804 Source : Processed in SPSS 21.0

The mean value of “Perceived Quality” is 3.8264 which means that the strength of perceived quality of Nokia/ Microsoft Lumia among respondents is strong in average (> 3). The standard deviation is .67804 which shows that the responses are quite centralized.

Among the items of “Perceived Quality”, consumers agree the most with PQ1 (“Nokia/ Microsoft Lumia smartphones have good quality”) with mean = 3.9121 and agree the least with PQ5 (“Nokia/ Microsoft Lumia smartphones have excellent features”) with mean = 3.6154.

That reveals that the difference between evaluations are very small, most of the items are highly evaluated.

4.2.3.3 Descriptive Statistic of Variable “Brand Association”

To evaluate the brand equity strength in term of “Brand Association”, basic statistic measurement such as mean value and standard deviation are used. The item of “AS” comes from the sum of 5 items of Brand Association (AS1 + AS2 + … + AS5) / 5. The mean value is then going to be observed whether the value is > 3 or ≤ 3. The result is shown on the table below (table 4.5).

Table 4.5 Descriptive Statistic of Variable “Brand Association” Result Std. N Minimum Maximum Mean Deviation AS1 91 1.00 5.00 3.6813 1.05282 AS2 91 1.00 5.00 3.6923 1.00766 AS3 91 1.00 5.00 3.6813 .94138 AS4 91 2.00 5.00 3.8681 .83278 AS5 91 2.00 5.00 3.8462 .84226 AS 91 1.80 5.00 3.7538 .75723 Source : Processed in SPSS 21.0

The mean value of “Brand Association” is 3.7538 which means that the strength of brand association of Nokia/ Microsoft Lumia among respondents is strong in average (> 3). The standard deviation is .75723 which shows that the responses are quite centralized.

Among the items of “Brand Association”, consumers agree the most with AS4 (“Nokia/ Microsoft Lumia is the brand made by an organization I would trust”) with mean = 3. 8681 and agree the least with AS1 (“Some characteristics of Nokia/ Microsoft Lumia come to my mind quickly”) and AS3 (“Nokia/ Microsoft Lumia smartphones provide good value for the money “) with mean = 3.6813

That reveals that the difference between evaluations are very small, most of the items are highly evaluated.

4.2.3.4 Descriptive Statistic of Variable “Brand Loyalty”

To evaluate the brand equity strength in term of “Brand Loyalty”, basic statistic measurement such as mean value and standard deviation are used. The item of “AS” comes from the sum of 5 items of Brand Loyalty (LO1 + LO2 + … + LO5) / 5. The mean value is then going to be observed whether the value is > 3 or ≤ 3. The result is shown on the table below (table 4.6).

Table 4.6 Descriptive Statistic of Variable “Brand Loyalty” Result Std. N Minimum Maximum Mean Deviation LO1 91 1.00 5.00 3.2747 1.07565 LO2 91 1.00 5.00 2.9780 1.15449 LO3 91 1.00 5.00 2.8791 1.13368 LO4 91 1.00 5.00 3.1099 1.08986 LO5 91 1.00 5.00 3.1648 1.05687 LO 91 1.00 5.00 3.0813 1.00663 Source : Processed in SPSS 21.0

The mean value of “Brand Loyalty” is 3.0813 which means that the strength of brand loyalty of Nokia/ Microsoft Lumia among respondents is quite strong in average (> 3).

Among the items of “Brand Loyalty”, consumers agree the most with LO1 (“I consider myself to be loyal to Nokia/ Microsoft Lumia brand”) with mean = 3. 1648 and agree the least with LO2 (“Nokia/ Microsoft Lumia would be my first choice”) with mean = 2.8791

That reveals that the difference between evaluations are very small, most of the items are not highly evaluated.

4.2.3.5 Descriptive Statistic of Variable “Consumer Purchase Intention”

To evaluate the brand equity strength in term of “Consumer Purchase Intention”, basic statistic measurement such as mean value and standard deviation are used. The item of “PI” comes from the sum of 5 items of Consumer Purchase Intention (PI1 + PI2 + … + PI5) / 5. The mean value is then going to be observed whether the value is > 3 or ≤ 3. The result is shown on the table below (table 4.7).

Table 4.7 Descriptive Statistic of Variable “Consumer Purchase Intention” Result Std. N Minimum Maximum Mean Deviation PI1 91 1.00 5.00 3.1099 .99388 PI2 91 1.00 5.00 3.1319 .96849 PI3 91 1.00 5.00 2.9560 .95350 PI4 91 1.00 5.00 2.9121 .93866 PI5 91 1.00 5.00 2.8681 .88454 PI 91 1.00 4.60 2.9956 .85061 Source : Processed in SPSS 21.0

The mean value of “Purchase Intention” is 2.9956 which means that the consumer purchase intention regarding Nokia/ Microsoft Lumia brand is low (≤ 3) with standard deviation of .85061 which shows that the responses are highly diverse.

Among the items of “Purchase Intention”, consumers agree the most with PI2 (“I would probably buy a Nokia/ Microsoft Lumia brand for my next purchase of smartphone“) with mean = 3.1319 and agree the least PI5 (“My willingness to buy Nokia/ Microsoft Lumia smartphone for my next purchase is high”) with mean = 2.8681 That reveals that the difference between evaluations are very small, most of the items are not highly evaluated.

4.2.4 Classical Assumption

4.2.4.1 Normality Test

Figure 4.1 Normality Test: Histogram Source : Processed in SPSS 21

Based on histogram presented above (figure 4.1), the data distribution in this study shows that the histogram is bell-shaped which shows a normally distributed data.

Figure 4.2 Normality Test: P-P Plot Source : Processed in SPSS 21.0

Based on the normal probability plot presented above (figure 4.2), the data is closely aligned with the diagonal line and follow the direction of the diagonal line, then the regression model meet the assumption of normality.

To be able to proceed to multiple-linear regression analysis, the data needs to be normally distributed. Since the data is normally distributed, T-test and F-test (ANOVA) could be used for the hypothesis testing in this study.

4.2.4.2 Multicollinearity Test

Though “no precise definition of collinearity has been firmly established in the literature”, collinearity is generally agreed to be present if there is an approximate linear relationship (i.e., shared variance) among some of the predictor variables in the data. In theory, there are two extremes: perfect collinearity and no collinearity. In practice, data typically are somewhere between those extremes. Thus, 62

collinearity is a matter of degree. Though some collinearity is always present, the real issue is to determine the point at which the degree of collinearity becomes “harmful.” (Mason & William D. Perrault, 1991)

Multicollinearity test has purpose to test whether the regression model found a correlation between the independent variables. A good regression model should have no correlation between independent variables. Since multicollinearity increases the standard errors of the coefficients. Increased standard errors in turn means that coefficients for some independent variables may be found not to be significantly different from 0, whereas without multicollinearity and with lower standard errors, these same coefficients might have been found to be significant and the researcher may not have come to null findings in the first place. Multicollinearty is indicated for a particular variable if the tolerance value is 0.01 or less and if the VIF greater than 10 as indicative of muticollinearity (Meyers, 2006).

Table 4.8 Multicollinearity Test Collinearity Statistics Variables Result Tolerance VIF Brand Awareness .462 2.165 Non Multicollinearity (AWT) Perceived Quality (PQT) .453 2.206 Non Multicollinearity Brand Association .315 3.175 Non Multicollinearity (AST) Brand Loyalty (LOT) .387 2.581 Non Multicollinearity Source : Processed in SPSS 21.0

Based on the table above (table 4.8), there is no variable that have VIF greater than 10 and there is no variable with tolerance less than .01 which indicates that there is no multicollinearity in accordance to the standard provided by Meyers (2006). This would mean that the independent variable has no correlation between each other. This also one of the pre-requisite to proceed to multiple-linear regression analysis.

4.2.4.3 Heteroscedasticity Test

Figure 4.3 Heteroscedasticity Test: Scatterplot Source : Processed in SPSS 21.0

Indicates that the points are not making any particular pattern (there is no clear pattern) and the point spread above and below the number 0 on the Y axis. Based on the scatterplot provided (figure 4.3), we can conclude that there is no occurrence of heteroscedasticity, the data in this study is normal and cleared to be used for further research process.

4.2.4.4 Autocorrelation Test (Durbin-Watson)

Table 4.9 Durbin-Watson Test Durbin-Watson Positive Negative

d d-4 dL dU Autocorrelation Autocorrelation No Statistical No Statistical 1.817 2.183 1.429 1.611 Evidence Evidence

Source : Processed in SPSS 21.0

The value of dL and dU is taken from the Durbin-Watson statistic table where the regressors in this study is 4 and the sample is 91 with dL = 1.429 and dU = 1.611. If the value of d > dU, there is no statistical evidence that there is positive autocorrelation in the study, which is as shown in table 4.9. It is the same with the negative autocorrelation where if d-4 > dU, there is no statistical evidence that there is negative autocorrelation in the study, which is also sown in table 4.9. It can be concluded that this study has no tendency of the existence of autocorrelation.

4.2.5 Multiple Linear Regression

In this study, multiple linear regression analysis will be used to examine the relationship between several independent variables (which in this study are: Brand Awareness, Perceived Quality, Brand Association, Brand Loyalty) on the dependent variable (Consumer Purchase Intention). If the significance value is greater than .05, it means that the dependent variable being measured does not have significant influence towards the dependent variable. (Santoso, 2010)

The calculation process were done with SPSS 21.0. Summary of results of data processing by using the SPSS program was as follows:

Table 4.10 Regression Coefficient Table Unstandardized Standardized t Sig. Coefficients Coefficients Model Std. B Beta Error 1 (Constant) 1.705 1.245 1.369 .174

AWT .131 .122 .100 1.075 .285 PQT .010 .142 .007 .071 .944 AST .102 .156 .073 .651 .517 LOT .679 .097 .709 6.994 .000

Dependent Variable: PIT

Source : Processed in SPSS 21.0

Based on the result presented on the table above (table 4.9), if written in the unstandardized form of the equation, the regression is as follows:

Y = 1.705 + .679 LOT + ϵ

All regression coefficient are at positive value and have significant value which are greater than .05 as proposed by Santoso (2010). From the regression model above, it can be interpreted as follows: the regression coefficient of brand loyalty is .679, it means that if the value of brand loyalty is increased by 1 unit while the other variable is constant, then consumer purchase intention variable (Y) will be increased by .679 unit.

4.2.6 Hypothesis Testing

4.2.6.1 T-Test

T-test is the test performed to determine the effect of partially independent variable (X) on the dependent variable (Y), then the test is used to test whether brand awareness, perceived quality, brand association, and brand loyalty influence the consumer purchase decision. T-test results in this study can be seen in table above (table 4.9).

1. Brand Awareness to Consumer Purchase Intention

Ho1: There is no partial significance influence of brand awareness to consumer purchase intention

Ha1: There is a partial significance influence of brand awareness to consumer purchase intention

By using SPSS for the variable AW (Brand Awareness), the researcher got the t value = 1.075 with the significance level of .285. From the result, the significance value is higher than α = 5% (0.05), the value is lower than the value in the t-table (1.662). Which means that Ho is accepted and Ha is rejected. Thus the hypothesis is accepted that there is no partial significance of brand awareness towards consumer purchase intention.

2. Perceived Quality to Consumer Purchase Intention

Ho2: There is no partial significance influence of perceived quality to consumer purchase intention

Ha2: There is a partial significance influence of perceived quality to consumer purchase intention

By using SPSS for the variable PQ (Perceived Quality), the researcher got the t value = .071 with the significance level of .944. From the result, the significance value is higher than α = 5% (0.05), the value is lower than the value in the t-table (1.662). Which means that Ho is accepted and Ha is rejected. Thus the hypothesis is accepted that there is no partial significance of perceived quality towards consumer purchase intention.

3. Brand Association to Consumer Purchase Intention

Ho3: There is no partial significance influence of brand association to consumer purchase intention

Ha3: There is a partial significance influence of brand association to consumer purchase intention

By using SPSS for the variable AS (Brand Association), the researcher got the t value = .651 with the significance level of .517. From the result, the significance value is higher than α = 5% (0.05), the value is lower than the value in the t-table (1.662). Which means that Ho is accepted and Ha is rejected. Thus the hypothesis is accepted that there is no partial significance of brand association towards consumer purchase intention. 67

4. Brand Loyalty to Consumer Purchase Intention

Ho4: There is no partial significance influence of brand loyalty to consumer purchase intention

Ha4: There is a partial significance influence of brand loyalty to consumer purchase intention

By using SPSS for the variable LO (Brand Loyalty), the researcher got the t value = 6.994 with the significance level of .000. From the result, the significance value is lower than α = 5% (0.05), the value is higher than the value in the t-table (1.662). Which means that Ho is rejected and Ha is accepted. Thus the hypothesis is accepted that there is a partial significance of brand association towards consumer purchase intention.

4.2.6.2 F-Test

F-test is used to test whether all of the independent variables, which are brand awareness, perceived quality, brand association, and brand loyalty have simultaneous significant influences toward dependent variable (Consumer Purchase Intention) with α = 0.05. The acceptance or rejection of the hypothesis are described as when F value > F table, then the Ho rejected and Ha accepted. Oppositely, if F value< F table, then Ho accepted and Ha rejected. The result of F- test (ANOVA) is shown in the following table below.

Table 4.11 F-Test (ANOVA) Result Sum of Mean Model df F Sig. Squares Square 1 Regression 855.380 4 213.845 41.293 .000b Residual 445.367 86 5.179 Total 1300.747 90 a. Dependent Variable: PIT b. Predictors: (Constant), LOT, AWT, PQT, AST Source : Processed in SPSS 21.0

Hypothesis:

Ho5: There is no simultaneous influence of brand awareness, perceived quality, brand association and brand loyalty to consumer purchase intention

Ha5: There is a simultaneous influence of brand awareness, perceived quality, brand association, and brand loyalty to consumer purchase intention

From the table above, the testing of independent variables simultaneously on dependent variable is done by using the F-test, the result of this F-test shows the F value = 41.293 with significance level of .000 (which implies that there is at least one factor affecting the dependent variable; this is supported by the t-test where “Brand Loyalty” is affecting “Purchase Intention”).

The F table value is found on the F table with df1 = 4 and df2 = 86, thus the F table value is 2.15i.

From the result, it showed that F value is higher than the value of F table (41.293 > 2.15) and significance level is lower than .10 (.000). Which means that there is a simultaneous significance influence of brand awareness, perceived quality, brand association, and brand loyalty to consumer purchase intention

4.2.6.3 R2 Test

The coefficient of determination (R2) was essentially measure how far the ability of the model (inter firm powers, relationship, performance) to explain the variations dependent variable (supplier satisfaction). Value of coefficient determination between zero (0) and one (1). The coefficient of determination represented in Table below:

i The value is obtained from F-value calculator since the df2 = 86 cannot be found in conventional F-value table. Source: http://www.danielsoper.com/statcalc3/calc.aspx?id=4 69

Table 4.12 R2 Test Result Model R R Square Adjusted R Std. Error of Square the Estimate

1 .811a .658 .642 2.27567 a. Predictors: (Constant), LOT, AWT, PQT, AST b. Dependent Variable: PIT Source : Processed in SPSS 21.0

From the table above, the adjusted R-square = .658 which implies that the independent variables can explain 65.8% the variation of the dependent variable “Consumer Purchase Intention”. The rest (34.2%) of consumer purchase intention can be explained by other variables which are not examined in this research.

4.3 Interpretation of Results

4.3.1 The Influence of Brand Awareness on Consumer Purchase Intention

Hypothesis 1 testing results shows that brand awareness has no significant influence towards consumer purchase decision in regard of Nokia/ Microsoft Lumia smartphone brand. Based on table 4.9, this variable has the significance level of .285 which is higher than α = .5. The beta coefficient of brand awareness is 1.075 which is lower than the value in the t-table which is 1.662. This data implies that brand awareness does not have a significant factor towards consumer purchase intention.

According to Chi, et al (2009), brand awareness plays an important role on purchase intention because consumers tend to buy familiar and well known product and a product with high brand awareness will receive higher consumer preferences because it has higher market share and quality evaluation. And brands that consumers know are more likely to be included in the consumers’ consideration set. Consumers may use brand awareness as a purchase decision heuristic. Therefore brand awareness increases brand market performance. (Huang & Sarigöllü, 2012)

This study, however, find another possibility where high brand awareness level does not necessarily mean that the consumer purchase intention of that product is 70

favorable. Chi, et al (2009),Jalilvand, et al (2009) and Malik, et al (2013) find that brand awareness affect consumer purchase intention, which is not similar with the finding in this study.

There are studies that find brand awareness has an influence towards consumer purchase intention where the result of this study find the otherwise. Among those studies, there are findings where brand awareness event has a negative influence towards consumer purchase intention which means that the higher the value of brand awareness is, the lower the consumer purchase intention (Jalilvand, et al., 2011; Rinto, 2013; Wirianto, 2013; Hardiansyah, 2013; Zhafira, 2014). This study shows that brand awareness are not even has an influence towards consumer purchase intention, which is similar with the finding of Santoso and Cahyadi (2014) and Tobing (2014).

From the descriptive statistic, x̅ of brand awareness is 3.94 which if related to the brand awareness concept could be assumed that Nokia/ Microsoft Lumia is at Brand Recall stage where consumers could recall the brand without the help of hint or information (unaided recall), considering the questionnaire used in this study does not state any hints nor information regarding the brand in general.

4.3.2 The Influence of Perceived Quality on Consumer Purchase Intention

Hypothesis 2 testing result shows that perceived quality has no significant influence towards consumer purchase decision in regard of Nokia/ Microsoft Lumia smartphone brand. Based on table 4.9, this variable has the significance level of .944 which is higher than α = 0.5. The beta coefficient of perceived quality is .71 which is lower than the value in the t-table which is 1.662. This data implies that perceived quality does not have a significant influence towards consumer purchase intention.

Perceived quality is a consumer subjective judgment on product quality, and he or she will evaluate product quality from their previous experiences and feelings. (Chi, et al., 2009). A perception of a quality will have a direct impact to the potential buyer, as for example if a customer form a perception that hand phones from brand

A are good, it would form a higher possibilities for a customer to bought a product from a certain brand. (Aaker, 2006 as cited by Rinto, 2013)

There are several studies that find perceived quality to has impact towards consumer purchase intention (Jalilvand, et al., 2011; Rinto, 2013; Wirianto, 2013; Hardiansyah, 2013; Tobing, 2014; Zhafira, 2014), in all off those studies, perceived quality has positive relationship with consumer purchase intention. In this study, perceived quality does not have an influence towards consumer purchase intention, similar with the result of the study conducted by Santoso and Cahyadi (2014). This is not aligned with the common theory where perceived quality will have a direct impact to the potential buyer where no higher possibility for a customer to bought a product because of its good perceived quality.

4.3.3 The Influence of Brand Association on Consumer Purchase Intention

Hypothesis 3 testing result shows that brand association has no significant influence towards consumer purchase decision in regard of Nokia/ Microsoft Lumia smartphone brand. Based on table 4.9, this variable has the significance level of .517 which is higher than α = 0.5. The beta coefficient of perceived quality is .651 which is lower than the value in the t-table which is 1.662. This data implies that perceived quality does not have a significant influence towards consumer purchase intention.

Aaker (1991) as cited by Jalilvand, et al. (2011) suggested that brand associations could provide value to the consumer by providing a reason for consumers to buy the brand, and by creating positive attitudes/ feelings among consumers.

Reason to buy, the association or the product attributes is often become the reason for consumers to buy the product. Creating positive attitudes or feelings, some associations to be a very desirable and stimulate a positive attitude which is then transferred to the brand. The basis for the extension, the association could also be the basis to extend or add a new product, which is by creating a sense of fit and comfort of a brand name with the new product. (Aaker, 2006 as cited by Rinto, 2013)

There are several studies related to brand equity concept which shows that brand association has a significant influence towards consumer purchase intention (Jalilvand, et al., 2011; Rinto, 2013; Wirianto, 2013; Hardiansyah, 2013; Santoso & Cahyadi, 2014), the result from this study is different from what they had found, brand association does not has an influence towards consumer purchase intention.

4.3.4 The Influence of Brand Loyalty on Consumer Purchase Intention

Hypothesis 4 testing result shows that brand loyalty has significant influence towards consumer purchase decision in regard of Nokia/ Microsoft Lumia smartphone brand. Based on table 4.9, this variable has the significance level of .000 which is lower than α = 0.5. The beta coefficient of perceived quality is 6.994 which is higher than the value in the t-table which is 1.662. This data implies that perceived quality has a significant influence towards consumer purchase intention.

Rossiter and Percy (1987) as cited by Jalilvand, et al. (2011) argued that brand loyalty is often characterized by a favorable attitude towards a brand and repeated purchases of the same brand over time.

Those literature support the finding of this research where brand loyalty has a significant influence towards consumer purchase intention. There are several studies that shows similar result (Jalilvand, et al., 2011; Wirianto, 2013; Hardiansyah, 2013; Santoso and Cahyadi, 2014; Zhafira, 2014)

4.3.5 The Influence of Brand Awareness, Perceived Quality, Brand Association, and Brand Loyalty Simultaneously on Consumer Purchase Intention

Hypothesis 5 testing result shows that brand awareness, perceived quality, brand association, and brand loyalty has a simultaneous influence towards consumer purchase decision in regard of Nokia/ Microsoft Lumia smartphone brand. Based on table 4.10, these variables have the significance level of .000 which is lower than α = 0.5. The F-value is 41.293 which is higher than the F-table value = 2.15. The significance level of .000 also indicates that at least one of the independent variable affect the dependent variable (Consumer Purchase Intention).

As brand equity is reflected in brand preference, it could be inferred that brand preference would be reflected in purchase or usage intention. (Moradi & Zarei, 2011). Which is the hard to analyze within the result of this study where, even though there is a simultaneous influence of brand equity dimensions, only brand loyalty has the significant influence towards consumer purchase intention.

The result of this study is similar with the result of several studies where brand equity has simultaneous influence towards consumer purchase intention (Jalilvand, et al., 2011; Wirianto, 2013; Hardiansyah, 2013)

CHAPTER V

CONCLUSION AND RECOMMENDATION

5.1 Conclusion

The research results helped answer the four research questions: if there is a partial significance influence of brand awareness towards consumer purchase intention, if there is a partial significance influence of perceived quality towards consumer purchase intention, if there is a partial significance influence of brand association towards consumer purchase intention, if there is a partial significance influence of brand loyalty towards consumer purchase intention, and if there is a simultaneous significant influence of brand awareness, perceived quality, brand association, and brand loyalty towards consumer purchase intention.

1. Brand awareness does not possess significant influence towards consumer purchase intention. The increase or decrease in brand awareness strength will not affect consumer purchase intention towards Nokia/ Microsoft Lumia.

2. Perceived quality does not possess significant influence towards consumer purchase intention. The increase or decrease in perceived quality strength will not affect consumer purchase intention towards Nokia/ Microsoft Lumia.

3. Brand association does not possess significant influence towards consumer purchase intention. The increase or decrease in brand association strength will not affect consumer purchase intention towards Nokia/ Microsoft Lumia.

4. Brand loyalty has a positive significant influence towards consumer purchase intention. The increase or decrease in brand loyalty strength will affect consumer purchase intention towards Nokia/ Microsoft Lumia.

5. Brand awareness, perceived quality, brand association, and brand loyalty has simultaneous significant influence towards consumer purchase intention. The increase or decrease in brand equity dimensions simultaneously will affect consumer purchase intention towards Nokia/ Microsoft Lumia.

5.2 Recommendation

For Nokia/ Microsoft Inc.

1. Trying to target different market segment. This study find that young adults are not interested to make a purchase of Nokia/ Microsoft Lumia despite the good brand awareness, perceived quality and brand association. Targeting different market segment in different age group may end in different result. 2. Increase customer interactions. Brand loyalty has significant influence towards consumer purchase intention which may result in re-purchase intention. Retaining loyal customer is an important task, increasing customer interactions may increase the loyalty of the current users.

For Future Researcher

1. Improve the number of variables and items in the analysis. There are some variables that are not included in the factors influencing consumer purchase intention, there is a possibility that price, mobile operating system and other marketing aspects that could be included in the study. 2. Develop population used for the study to give broader analysis. The population could also be developed outside President University and could use the population of university students in Indonesia or maybe in a certain region to be able to analyze the influence of brand equity towards consumer purchase intention in other part of Indonesia.

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APPENDICES

GENDER

Male 32%

Female 68%

MAJOR

Accounting Banking & Finance Business Administration Hotel & Tourism Management Human Resource Industrial Engineering Information Technology International Business International Relations Public Relations

Visual Communication Design

5 5

2 1

MONTHLY EXPENDITURE

< IDR 500,000 IDR 500,001 - IDR 1,000,000 IDR 1,000,001 - IDR 1,500,000 IDR 1,500,001 - IDR 2,000,000

> IDR 2,000,001

19 3

CURRENTLY USED SMARTPHONE BRAND

Apple Blackberry Lenovo Nokia Oppo Samsung Sony Other

4 4

3 2

INTRODUCTION TO QUESTIONNAIRE

Dear President University students of batch 2011,

I would like to ask for your willingness in filling this questionnaire regarding brand equity of Nokia/Microsoft Lumia and its influence towards consumer purchase intention. I herewith explain the main purpose of these questionnaire along with general instruction filling. This questionnaire was developed in order to know whether brand equity affect purchase intention and which aspect of brand equity has the most influence.

This questionnaire consists of three sections:

Section 1 : General information of yourself

Section 2 : Instructions in filling out the questionnaire

Section 3 : Mobile Operating System and Smartphone Purchase Decision Questionnaire

The respondents were asked to fill in the questionnaire in objective manner. Your answers will be only used for academic purposes.

Thank you for your attention and cooperation.

Cikarang, 18th of October, 2014

Stephen

SECTION 1

What is your gender? □ Male □ Female

What is your major? □ International Business □ Hotel & Tourism □ Accounting Management □ Visual Communication □ International Relations Design □ Banking & Finance □ Marketing □ Industrial Engineering □ Information Technology □ Public Relations □ Business Administration □ Electrical Engineering

What is your monthly expenditure? □ < IDR 500,000 □ IDR 500,001 – IDR 1,000,000 □ IDR 1,000,001 – IDR 1,500,000 □ IDR 1,500,001 – IDR 2,000,000 □ > IDR 2,000,001

What is your current smartphone brand? □ Samsung □ Lenovo □ Sony □ Nokia □ Apple □ Oppo □ Blackberry □ Other

SECTION 2

In this section, you are asked to give an opinion on how much the following statements are in accordance with your satisfaction.

Instructions

Give a tick mark in number 1 if your response for the question given is “Strongly Disagree”.

Give a tick mark in number 2 if your response for the question given is “Disagree”.

Give a tick mark in number 3 if your response for the question given is “Neutral”.

Give a tick mark in number 4 if your response for the question given is “Agree”.

Give a tick mark in number 5 if your response for the question given is “Strongly Agree”.

SECTION 3

1 2 3 4 5 Questions Strongly Strongly Agree Agree Neutral Disagree Disagree BRAND AWARENESS I can recognize Nokia/ Microsoft Lumia smartphones among other competing brands I am aware of Nokia/ Microsoft Lumia smartphones I am familiar with Nokia/ Microsoft Lumia when I see it from its logo Nokia/ Microsoft Lumia brand is famous Nokia/Microsoft Lumia brand is well known PERCEIVED QUALITY Nokia/ Microsoft Lumia smartphones have good quality Nokia/ Microsoft Lumia smartphones have consistent quality Nokia/ Microsoft Lumia smartphones are very durable Nokia/ Microsoft Lumia smartphones are very reliable Nokia/ Microsoft Lumia smartphones have excellent features BRAND ASSOCIATION Some characteristics of Nokia/ Microsoft Lumia come to my mind quickly I can always trust on Nokia/ Microsoft Lumia smartphone brand if I want a product of high quality Nokia/ Microsoft Lumia smartphones provide good value for the money

Nokia/ Microsoft Lumia is the brand made by an organization I would trust Nokia/Microsoft Lumia is different from competing brands BRAND LOYALTY I consider myself to be loyal to Nokia/ Microsoft Lumia brand Nokia/ Microsoft Lumia would be my first choice I will not buy other brands if Nokia/ Microsoft Lumia is available at the store Even if another brand has the same features as Nokia/ Microsoft Lumia, I would prefer to buy Nokia/ Microsoft Lumia If there is another brand as good as Nokia/ Microsoft Lumia, I prefer to buy Nokia/ Microsoft Lumia PURCHASE INTENTION I would likely buy a Nokia/ Microsoft Lumia brand for my next purchase of smartphone

I would probably buy a Nokia/ Microsoft Lumia brand for my next purchase of smartphone

I would certainly buy a Nokia/ Microsoft Lumia brand for my next purchase of smartphone

I would definitely buy a Nokia/ Microsoft Lumia

brand for my next purchase of smartphone My willingness to buy Nokia/ Microsoft Lumia smartphone for my next purchase is high

Values of r for the .05 and .01 Levels of Significance df (N – 2) .05 .01 1 .997 1.000 2 .950 .990 3 .878 .959 4 .812 .917 5 .755 .875 6 .707 .834 7 .666 .798 8 .632 .765 9 .602 .735 10 .576 .708 11 .553 .684 12 .533 .661 13 .514 .641 14 .497 .623 15 .482 .606 16 .468 .590 17 .456 .575 18 .444 .562 19 .433 .549 20 .423 .537 21 .413 .526 22 .404 .515 23 .396 .505 24 .388 .496 25 .381 .487 26 .374 .479 27 .367 .471 28 .361 .463 29 .355 .456 30 .349 .449