RESEARCH METHOD
Type of Research
This study used a quantitative approach to gather source of information on luxury brand image, perceived price, and perceived product quality to consumer’s purchase intention. This is explanatory research aiming to determine the causal relationship between each of the X variables (luxury brand image, perceived price, and perceived product quality) and the Y variable (purchase intention). For testing objective theories above, this type of research will be examining the relationship among the variables.
Location, Population, and sample of the Research
According to Roscoe (1975) in (Sugiyono, 2019), 30-500 is the appropriate sample size.
Therefore, 150 samples were being used for this research. Samples were taken using non- probability method and purposive sampling technique with the following criteria: those who are 17-41 years old, are not iPhone users, and have sufficient knowledge on iPhone quality, iPhone price, as well as other smartphones’ price ranges.
Sampling s the step to decide how many samples should be collected in this study. The sampling technique in this study is non-probability sampling with a purposive sampling approach defined by (Ferdinand, 2014). It was one of the most effective techniques to gather a specific amount of data. The population of this research was generations Y and Z in Indonesia who have not purchased an iPhone. Generation Y was born between 1981-1996 (aging 26-41 years old in 2022), while generation Z was born between 1997-2012 (aging 10-25 years old in 2022) (Shalihah, 2021). However, this research restricts the age of the respondents starting from 17 years old, considering that at the age of 17 they are mature enough to fill out a research questionnaire.
Data Collection Method
A questionnaire’s main goals is to collect significant data the most valid and reliable manner. As a research framework, the survey uses luxury brand image, perceived price, perceived quality, and purchase intention to collect data using this tool with five point Likert Scale. 5-point Likert-scale questionnaire was spread online using Google Form. Out of 248 responses, 47 of them are not eligible. Thus, only 201 responses were processed. The data was first checked for its validity and reliability, followed by the classical assumption tests (normality, linearity, multi- collinearity, and heteroscedasticity tests). After passing all of the preliminary tests, then the data was processed using SPSS to perform the multiple linear regression analysis.
The data mainly collected taken at the beginning of July until at December 2022. Through an online questionnaire, by also using through Google forms. The first section accumulated the demographic of respondents in terms of gender, age, profession, domicile city, average expense per month (IDR), using and iPhone, and also their type of smartphone at this time. The second section for the filter from questionnaire starting from luxury brand image items, perceived price, perceived quality, and the last is question for purchase intention from an iPhone. With total, 29 questions respondents must select a number between 1 and 5 based on statement from (1-Strongly disagree, 2-Disagree, 3-Somewhat agree, 4-Agree, 5-Strongly agree). To ensure that all of respondents fully understood and clear the question, the survey was translated into Indonesian language because not all of the respondents master English language.
Operational Variable
The operational table is a concept variables into measurement variables. Operational variables table can assist a researcher for compiling a questionnaire for make measurements based on aspects and indicators.
Table 1 Operational Definition and Measurement of Variables
Variable Definition Indicator Statement
Luxury Brand Image
Luxury brand image are the ability to make
or create a prestige brand image, which often to rely on brand-
consumer communication
(Fei, Hoo, Ng, &
Yew, 2019)
1. Functional Image
1. (a) iPhone has the best quality
(b) iPhone is sophisticated
(c) iPhone is superior
2. Experiental Image
2. (a) iPhone is precious (b) iPhone is unique (c) iPhone is attracting (d) iPhone is stunning
3. Symbolic Image
3. (a) iPhone is conspicuous
(b) iPhone is expensive (c) iPhone) is for the wealthy
(Ku & Lin, 2018)
Perceived Price
Price perception can also be defined as a
description of the product itself; with the
features it offers (Senggetang, Mandey,
& Moniharapon, 2019)
1. The price of an item following its brand image
1. (a) The price of iPhone following its brand image 2. The price of an
item is very reasonable
2. (a) I think the price of iPhone is very reasonable 3. An item delivers
more value than (the money value) that spend
3. (a) An iPhone delivers more value than (the money value) I would spend
4. Buy an item, albeit at a higher price
4. (a) I want to buy iPhone, albeit at a higher price
5. Buying an item can provide more
significant benefit than that which would be paid
5. (a) I think buy an iPhone can provide more benefit than which would be paid
6. The price of an item is worth to buy
6. (a) I think the price of an iPhone is worth to buy
(Suhud, Allan, Rahayu, &
Prihandono, 2022)
Perceived Perceived quality is also defined as a global
1. The product is reliable
1. (a) iPhone is reliable product
product on the core of the quality of goods
(Rivai & Wahyudi, 2017)
3. The workmanship of the product is good
3. (a) the workmanship of iPhone is good
(Gök, Ersoy, &
Börühan, 2019)
Purchase Intention
Purchase Intention can also be defining by
evoking from psychological response
in each people and subsequent cognitive evaluation it refers to value for money in each people perception
(Cheng-Xi Aw, Huie- Wen Chuah, Sabri, &
Basha, 2021)
1. I have a great interest to buy an item in the future
1. (a) I have a great interest to buy an iPhone in the future
2. I’m willing to pay money to buy an item someday
2. (a) I'm willing to pay money to buy an iPhone someday.
3. There is a
significant possibility that I would buy an item
3. (a) There is a
significant possibility that I would buy an iPhone 4. I have a firm
intention to buy an item
4. (a) I have a firm intention to buy an iPhone
5. I would
recomended an item e to my freinds if I had bought
5. (a) I would recommend an iPhone to my friends if I had bought
6. I have desire to buy and item in the future
6. (a) I have a desire to buy an iPhone in the future.
7. I have desire to buy a kind of an item than others
7. (a) I have a desire to buy a kind of an iPhone than others
(Vinh & Phuong,
2020)
Data Analysis Method
Validity Test
This test is aiming to determine the extent to which the question contained inside the questionary items can define a variable. The research questionnaires can be state valid if when the value of the r-count is greater than the value from r-table (r-count> r-table).
Reliability Test
The reliability test determined by testing for both consistency and stability. It describes how well a set of items used to measure a concept are fixed together. When Cronbach’s alpha value is greater than 0,6 it can be said that every variable in the questionnaire are reliable
Classical Assumption
This study also conducted several classic assumption test, including normality, heteroscedasticity, and multicollinearity test.
1. Normality test
Firstly, the normality test in the regression model that will be used in this study aims to test
• If the Significance Value > 0.05, then it can be concluded that the data is normally distributed.
• If the significance value is 0.05, then the data is not normally distributed Linearity Test
This test was carried out in this study aimed to determine whether there are independent variables that have similarities between other independent variables in the regression model. The Linearity test is carried out by looking at the tolerance value and the Variance Inflation Factor (VIF) value. H0: VIF < 10 means that there is no multicollinearity. H0: VIF >10 means that there is multicollinearity (Ghozali, 2016).
Heteroscedasticity Test
In this test, it is aimed at whether in this study there is an inequality of variance from the residuals. By using the Glesjer test, if the significant value is < 0.05 then heteroscedasticity occurs, if on the contrary the significance value 0.05 then homoscedasticity occurs (Ghozali, 2016).
Multicollinearity test
The last for the classical assumption test is the multicollinearity tests. This method is use for testing the variance inflation factors. The rule for this multicollinearity test the VIF value must be less than 10. If the tolerance value > 0.1, and the VIF < 10, the hypothesis is not rejected.
Hypothesis Test Technique
This test is aimed to ascertain whether the variable had any effect on dependent variable as partially by T-test, jointly, or simultaneously (with the F-test).
F-Test
This test aims to prove the first to third hypotheses. T test to find out the significance of the influential value of the three independent variables (luxury brand image, perceived price, and perceived quality) on the dependent variable (Purchase intention) simultaneously. (Ghozali, 2016) states that the significant level for this study F test is 5%, or <0.05.
T-Test
This test aims to prove the first to third hypotheses. T test to find out the significance of the influential value of the three independent variables (luxury brand image, perceived price, and
perceived quality) on the dependent variable (Purchase intention) partially individually, the degree of significance a = 0.05. In this test, we will use tools with the SPSS program (Ghozali, 2016).
Multiple Linear Regression Analysis
This study will use an analytical technique that uses multiple linear regression data to determine the effect of luxury brand image, price perception, and quality perception on purchase intention. Meanwhile, for testing the hypothesis in this study, it will apply a partial test (t-test). to determine the effect of X1 on Y, X2 on Y, and X3 on Y. The multiple regression equation is as follows:
Y= α+b1X1+b2X2+b3X3+e
Description:
Y = Purchase Intention a = Constant Value
b = Regression Coefficient Value e = Error standard
X1 = Luxury Brand Image X2 = Perceived Price X3 = Perceived Quality
