Spending / Month

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FINDINGS AND DATA ANALYSIS

Demographics

Respondent are important in this research, because they can help researchers in collecting data that need. Table 2 display data from 201 respondents that knew iPhone brand and doesn’t use iPhone as their daily driver. In this table consist of information about characteristic from the respondents to this research; gender, age, profession, domicile city, average expense per month, using and iPhone, and also their type of smartphone at this time.

Figure 4. Gender Static

Source: Primary data collected by researcher, 2022

From the data above we can see that majority respondents are male as much 117, and then following by female by 84 people.

0 20 40 60 80 100 120 140

Male Female

Gender

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Figure 5. Spending Static

From the data above we can see that the most average spending per month is dominating by 116 respondents spent less than Rp 3.000.000, 61 respondents spent Rp 3.000.000 up to Rp 4.900.000, 18 respondents spent Rp 5.000.000 up to 6.900.000, 3 respondents spent Rp 7.000.000 up to Rp 9.000.000, and 2 respondents that spend greater than Rp 9.000.000, - on their expense per month. All of the respondents are doesn’t use iPhone as their daily smartphone.

Table 2 Descriptive Statistics Analysis of Sample

No. Category Sub-category Frequency Percentage

1 Age

20 17 8,46%

21 44 21,89%

22 7 3,48%

23 9 4,48%

24 13 6,47%

25 10 4,98%

26 4 1,99%

27 16 7,96%

28 18 8,96%

0 20 40 60 80 100 120 140

< IDR 3.000.000 IDR 3.000.000 - IDR 4.900.000

IDR 5.000.000 - IDR 6.900.000

IDR 7.000.000 - IDR 9.000.000

> IDR 9.000.000

Spending / Month

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29 23 11,44%

30 11 5,47%

31 6 2,99%

32 6 2,99%

34 10 4,98%

35 4 1,99%

36 1 0,50%

37 1 0,50%

38 1 0,50%

2 Profession

College Student 81 40,30%

Civil Servant 6 2,99%

Company Staff 37 18,41%

Entrepreneur 27 13,43%

Doctor 6 2,99%

Businessman 1 0,50%

BUMN 1 0,50%

Nurse 26 12,94%

Janitor 4 1,99%

Security 8 3,98%

Housewife 1 0,50%

Police 2 1,00%

BUMN 1 0,50%

3 Location

Central Java 182 91%

West Java 4 2%

DKI Jakarta 7 3,50%

East Java 1 3,50%

East Region 7 0,50%

4 Using an iPhone

Yes 0 0,00%

No 201 100,00%

5 Smartphone Type

Huawei 2 1%

Infinix 3 1%

Oppo 29 14%

Pocophone 2 1%

ROG Phone 2 2 1%

Realme 7 3%

Redmi 8 4%

Redmi Note 8 2 1%

Samsung 75 37%

Sonny 2 1%

Vivo 37 18%

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Xiaomi 31 15%

Advance 1 0%

Table 2 provides that the majority of the 201 respondents studied were the group was dominated by 144 people that age between 21 and 29 years old. More respondents were living in Semarang by 73 people or 36.92% of respondents. 81 respondents with the profession as college student as majority (36.32%). The most gadget type that been use as daily driver is Samsung as much 75 people and following by Vivo by 37 people Xiaomi 31 people Oppo and etc.

Validity Analysis

Validity test was done using Pearson’s Product Moment. Each statement items of all variables are valid because each significance value is not more than 0.05 and has an r-count value that exceeds the r-table value of 0.138.

Table 3 Pearson’s Correlation Validity Test Results

Variable Indicator rcount rtable Remark

X1

X1.1 0,279 0,138 Valid

X1.2 0,561 0,138 Valid

X1.3 0,549 0,138 Valid

X1.4 0,469 0,138 Valid

X1.5 0,188 0,138 Valid

X1.6 0,592 0,138 Valid

X1.7 0,658 0,138 Valid

X1.8 0,575 0,138 Valid

X1.9 0,548 0,138 Valid

X1.10 0,544 0,138 Valid

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X2

X2.1 0,555 0,138 Valid

X2.2 0,771 0,138 Valid

X2.3 0,713 0,138 Valid

X2.4 0,672 0,138 Valid

X2.5 0,613 0,138 Valid

X2.6 0,355 0,138 Valid

X3

X3.1 0,639 0,138 Valid

X3.2 0,789 0,138 Valid

X3.3 0,800 0,138 Valid

Y

Y.1 0,674 0,138 Valid

Y.2 0,663 0,138 Valid

Y.3 0,553 0,138 Valid

Y.4 0,377 0,138 Valid

Y.5 0,609 0,138 Valid

Y.6 0,577 0,138 Valid

Y.7 0,449 0,138 Valid

Source: Primary data processed by researcher, 2022

Based on the results of testing the validity of each statement item that has been filled in by the respondents it is known that for 26 statements on all variables it has a correlation above 0.138 (rcount > rtable) for the total answers so that the item is valid and used for further research.

Reliability Analysis

A validity test is used to determine the validity of a questionnaire. The person correlation coefficient (r) technique is used to evaluate the validity of each item. The question are said to be valid if the value of r-count > r-table (0,138).

Table 4 Cronbach’s Alpha Reliability Test Results

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Variable Indicator Cronbach’s Alpha

Remark Criteria Result

X1

X1.1 > 0.6 0.806 Valid

X1.2 > 0.6 0.806 Valid

X1.3 > 0.6 0.808 Valid

X1.4 > 0.6 0.810 Valid

X1.5 > 0.6 0.805 Valid

X1.6 > 0.6 0.806 Valid

X1.7 > 0.6 0.806 Valid

X1.8 > 0.6 0.807 Valid

X1.9 > 0.6 0.812 Valid

X1.10 > 0.6 0.810 Valid

X2

X2.1 > 0.6 0.801 Valid

X2.2 > 0.6 0.795 Valid

X2.3 > 0.6 0.802 Valid

X2.4 > 0.6 0.805 Valid

X2.5 > 0.6 0.801 Valid

X2.6 > 0.6 0.809 Valid

X3

X3.1 > 0.6 0.805 Valid

X3.2 > 0.6 0.801 Valid

X3.3 > 0.6 0.806 Valid

Y

Y.1 > 0.6 0.802 Valid

Y.2 > 0.6 0.802 Valid

Y.3 > 0.6 0.812 Valid

Y.4 > 0.6 0.814 Valid

Y.5 > 0.6 0.797 Valid

Y.6 > 0.6 0.800 Valid

Y.7 > 0.6 0.804 Valid

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Variable Cronbach’s Alpha

N of Items

CUT

OFF Meaning

Luxury Brand Image 0,672 10 0,6 Reliable

Perceived Price 0,671 6 0,6 Reliable

Perceived Quality 0,601 3 0,6 Reliable

Purchase Intentions 0,617 7 0,6 Reliable

Based on the table it can be seen that the 26 questions posed in this study have Cronbach's Alpha value, which is more than 0.6, so that it can be said that all measuring variables from the questionnaire are reliable, which means that the questionnaire used in this study is a questionnaire. the good one.

Classical Assumptions

After all of the research’s indicators have passed the validity and reliability tests, the classical assumption test will be performed, which must be met to produce a prediction model that is Best Linear Unbiased Estimation.

Normality Analysis

The Kolmogorov-Smirnov (K-S) non parametric statistical test was employed in this normality test. The purpose of the normality test is to determine whether the regression model’s data has a normal distribution.

Table 5 Results of the Normality Test

One-Sample Kolmogorov-Smirnov Test Unstandardiz

ed Residual

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N 201 Normal Parameters^a,b Mean .0000000

Std.

Deviation

2.37326907

Most Extreme Differences

Absolute .040

Positive .026

Negative -.040

Test Statistic .040

Asymp. Sig. (2-tailed) .200^c,d

a. Test distribution is Normal.

b. Calculated from data.

c. Lilliefors Significance Correction.

d. This is a lower bound of the true significance.

Based on the table above, it shows that the results of the normality test carried out show that the data is normally distributed. This is indicated by the Asymp value. Sig. (2- tailed) namely 0.200 > 0.05, it can be concluded that the data is normally distributed.

Heteroscedasticity Analysis

The heteroscedasticity test is a test to see whether there is an inequality of variance from residuals of one observation in the regression model. There are no symptoms of heteroscedasticity if the significant value above 5% and there is symptom of heteroscedasticity if it is below 5%

Table 6 Heterosedasticity Test Result (Glejser)

Model Unstandardized

Coefficients

Standardized

Coefficients t Sig.

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B Std. Error Beta

1 (Constant) 1.454 0.945 1.539 0.125

Luxury Brand Image

-0.004 0.020 -0.016 -0.215 0.830

Perceived Price -0.032 0.044 -0.071 -0.727 0.468 Perceived Quality 0.106 0.076 0.133 1.392 0.166 a. Dependent Variable: abs_res

From the table above it can be seen that the significance value (Sig.) of all variables has a value greater than 0.05. From output above, it appears that the three variables have no heteroscedasticity symptoms because Sig. > 0.05 and has fulfilled the classical assumptions.

Linearity Analysis

Table 7 Linearity Test Results

ANOVA Table Sum of

Squares df

Mean

Square F Sig.

Purchase Intentions * Luxury Brand Image

Between Groups

(Combined) 469.115 26 18.043 1.996 .005 Linearity 291.420 1 291.420 32.234 .000 Deviation

from Linearity

177.696 25 7.108 .786 .756

Within Groups 1573.104 174 9.041

Total 2042.219 200

Squares df

Mean

Square F Sig.

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Purchase Intentions * Perceived Price

Between Groups

(Combined) 936.731 15 62.449 10.451 .000 Linearity 797.552 1 797.552 133.46

8

.000

Deviation from Linearity

139.179 14 9.941 1.664 .066

Within Groups 1105.488 185 5.976

Total 2042.219 200

Squares df

Mean

Square F Sig.

Purchase Intentions * Perceived Quality

Between Groups

(Combined) 352.785 9 39.198 4.432 .000 Linearity 226.335 1 226.335 25.588 .000 Deviation

from Linearity

126.449 8 15.806 1.787 .082

Within Groups 1689.434 191 8.845

Total 2042.219 200

Based on the test results in the table above, it is known that the probability value of all variables is above 0.05 so that it can be concluded that between luxury brand image and purchase intentions, Perceived Price and purchase intentions, and Perceived Quality and purchase intentions have a linear relationship.

Multi-collinearity Analysis

Table 8 Multi-collinearity Test Result

Coefficients^a

Model Collinearity Statistics Tolerance VIF

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(Constant)

Luxury Brand Image 0.924 1.083

Perceived Price 0.525 1.904

Perceived Quality 0.552 1.811 a. Dependent Variable: Purchase Intentions

Based on the test results in the table above, because the VIF value for all variables has a value less than 10 and a tolerance value greater than 0.10, it can be concluded that there is no multicollinearity between the independent variables in the regression model.

Hypothesis Analysis

T-Test (Partial) Analysis

Partial hypothesis testing I used to ascertain the impact and the importance in each of variables.

Table 9 Result of T-Test

Coefficients^a

Model t Sig.

(Constant) 7.209 0.000 Luxury Brand Image 3.997 0.000 Perceived Price 9.083 0.000 Perceived Quality -2.076 0.039

a. Dependent Variable: Purchase Intentions

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According to the statistics literature, if the value of t-count > t-table, and the value of significance should be <0,05, it can be concluded that the independent variables have influenced the dependent variable. The proposed equation table is as follows

(𝑎⁄2; 𝑛 − 𝑘 − 2) = (0.05⁄2; 252 − 2 − 1) = (0.025; 198)

The value of table was determined from the calculation above to be 1.972. the following explanation explanations are given for each variables results:

a) The influences of product placement (X1) on purchase intention (Y) H0: p-value > α (0,05)

Then the luxury brand image does not influence purchase intention H1: p-value < α (0,05)

Then, luxury brand image influences purchase intention

In the Luxury Brand Image variable (X1) it is found that the count is 3.997 >

table 1.972 and a significant value is 0.000 <0.05, which means that there is a significant influence between Luxury Brand Image on Purchase Intentions. H0 is rejected and Hα1 is accepted. Then it can be concluded that luxury brand image partially influences purchase intention (Y).

b) The influences of perceived price (X2) on purchase intention (Y) H0: p-value > α (0,05)

Then the perceived price does not influence purchase intention H2: p-value < α (0,05)

Then, perceived price influences purchase intention

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In the variable Perceived Price (X2) it is found that the count is 9.083 > table

1.972 and a significant value is 0.000 <0.05, which means that there is a significant influence between Perceived Price on Purchase Intentions. H0 is rejected and Hα2 is accepted. Then it can be concluded that perceived price partially influences purchase intention (Y).

c) The influences of perceived quality (X3) on purchase intention (Y) H0: p-value > α (0,05)

Then the perceived quality does not influence purchase intention H3: p-value < α (0,05)

Then, perceived quality influences purchase intention

In the variable Perceived Quality (X3) it is found that the count is -2.076 <

table -1.972 and a significant value is 0.039 <0.05, which means that there is a significant influence between Perceived Quality on Purchase Intentions.

H0 is rejected and Hα3 is accepted. Then it can be concluded that perceived quality partially influences purchase intention (Y).

F-Test (simultaneously) Analysis

Simultaneously hypothesis testing I used to ascertain the impact and the importance in each of variables.

Table 10 Result of F-Test ANOVA^a

Model Sum of

Squares df Mean Square F Sig.

Regression 915.738 3 305.246 53.382 .000^b

Residual 1126.481 197 5.718

Total 2042.219 200

a. Dependent Variable: Purchase Intentions

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b. Predictors: (Constant), Quality Perception, Luxury Brand Image, Price Perception

Based on the results of the F statistical test using analysis of variance or ANOVA it can be seen that the value of Fcount 53,382 >Ftable 2.65 and a significance value (0.000

<0.05) which means significant, it can be concluded that the variables Luxury Brand Image, Perceived Price and Perceived Quality simultaneously and Confidence (simultaneously) have a significant influence on the Purchase Intentions (Y) variable.

Table 11 Coefficient of Correlation & Determination

Model Summary^b

Model R R Square

Adjusted R Square

Std. Error of the Estimate

1 0.670^a 0.448 0.440 2.391

a. Predictors: (Constant), Perceived Quality, Luxury Brand Image, Perceived Price

b. Dependent Variable: Purchase Intentions

From the calculation above, it is obtained that the coefficient of multiple determination (R2) Adjusted R Square is 0.448. This can be interpreted that the variables Luxury Brand Image, Perceived Price and Perceived Quality affect 44.8%

of Purchase Intentions while the remaining 55.2% is influenced by other factors

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Multiple Linear Regression Test

When there are two or more independent variables and one dependent variable, multiple linear regression s used to estimated and predict how depend variable would change as one or more independent variables changed.

Table 12 Multiple Linear Regression Result

Coefficients^a

Model

Unstandardized Coefficients

Standardized

Coefficients t Sig.

B Std. Error Beta

(Constant) 11.213 1.555 7.209 .000

Luxury Brand Image

.133 .033 .220 3.997 .000

Price Perception .652 .072 .663 9.083 .000

Quality Perception -.260 .125 -.148 -2.076 .039 a. Dependent Variable: Purchase Intentions

Based on the results of multiple regression, the regression equation is obtained, namely:

𝑌 = 𝑎 + 𝛽1𝑋1 + 𝛽2𝑋2 + 𝛽3𝑋3 + 𝛽$𝑋$ + 𝛽5𝑋5 + 𝑒

Y = 11.213 + 0.133 X1 + 0.652 X2 + (-0.260) X3 + e

From the regression equation above it can be interpreted as follows:

a) The constant value is 11.213, this indicates that if the variables Luxury Brand, Perceived Price and Perceived Quality are considered constant (0), then Purchase Intentions is 11.213.

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b) The regression coefficient of the Luxury Brand Image variable (X1) is 0.133. This means that every increase in Luxury Brand Image by 1 unit will increase Purchase Intentions by 0.133.

c) The regression coefficient of the Perceived Price variable (X2) is 0.652. This means that every 1 unit increase in Perceived Price will increase Purchase Intentions by 0.652 units.

d) The regression coefficient of the Perceived Quality variable (X3) is -0.260. This means that every 1 unit increase in Perceived Quality will decrease Purchase Intentions by 0.260 units.

e) Based on the B value above, it can be concluded that Perceived Price has a greater influence on Purchase Intentions than Luxury Brand Image and Perceived Quality.

Descriptive Static Analysis

The main objective of this statistical descriptive analysis is to evaluate how the respondents responded to each indicator question. Likert scale is used in this study, the researcher wants to examine value under interval for each variable. The interval calculation for the categories below is obtained as follows:

𝐼𝑛𝑡𝑒𝑟𝑣𝑎𝑙 = (𝑀𝑎𝑥𝑖𝑚𝑢𝑚 𝑉𝑎𝑙𝑢𝑒 − 𝑀𝑖𝑛𝑖𝑚𝑢𝑚 𝑉𝑎𝑙𝑢𝑒)

𝑇𝑜𝑡𝑎𝑙 𝐶𝑎𝑡𝑒𝑔𝑜𝑟𝑖𝑒𝑠 = (5 − 1)

5 = 0.8

Then, the researcher arranged it into the following 5 categories:

Table 13 Descriptive Static Interval

Interval Classification

1.00-1.80 Very Low/Strongly Disagree

1.81-2.60 Low/Somewhat Disagree

2.61-3.40 Medium/Neutral

3.41-4.20 High/Somewhat Agree

4.21-5.00 Very High/Strongly Agree

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Source: Primary data processed by researcher, 2022 Following are the average for each question indicator:

Table 14 Descriptive Static of Luxury Brand Image

Indicator Statement Mean Remark

X1.1 iPhone has the best quality 4 Very High

X1.2 iPhone is sophisticated 3,7 High

X1.3 iPhone is superior 2,6 Low

X1.4 iPhone is precious 3,1 Medium

X1.5 iPhone is unique 3,6 Medium

X1.6 iPhone is attracting 3,3 Medium

X1.7 iPhone is stunning 3 Medium

X1.8 iPhone is conspicuous 3,3 Medium

X1.9 iPhone is expensive 2,8 Medium

X1.10 iPhone) is for the wealthy 2,9 Medium

Table 15 Descriptive Static of Perceived Price

X2.1 The price of an iPhone following its brand image 4,2 Very High X2.2 I think the price of an iPhone is very reasonable 4,1 High X2.3 An iPhone delivers more value than (the money value)

I would spend

3,9 High X2.4 I want to buy an iPhone, albeit at a higher price 4,5 Ver High X2.5 I think buying an iPhone can provide more significant

benefit than that which would be paid

3,9 High X2.6 I think the price of an iPhone is worth to buy 4,2 Very High

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Table 16 Descriptive Static of Perceived Quality

X3.1 The product is reliable 4,5 Very High

X3.2 The product is a high-quality product 4 High

X3.3 The workmanship of the product is good 4,2 High Source: Primary data processed by researcher, 2022

Table 17 Descriptive Static of Purchase Intention

Y.1 I have a great interest to buy an iPhone in the future 4,1 High Y.2 I'm willing to pay money to buy an iPhone someday. 4,1 High Y.3 There is a significant possibility that I would buy an

iPhone

4,2 High Y.4 I have a firm intention to buy an iPhone 4,2 High Y.5 I would recommend an iPhone to my friends if I had

bought

3,9 High Y.6 I have a desire to buy an iPhone in the future. 4,1 High Y.7 I have a desire to buy a kind of an iPhone than others. 4 High

Source: Primary data processed by researcher, 2022 Discussion

Partial Influence

a) The influence of luxury brand image on purchase intention

Luxury brand image variable’s statistical analysis revealed that the regression coefficient was positive at 0.133. Luxury brand image variable’s T-test result show the p-value is 0,000. The p-value is less than the significant level (0,00 <

0,05). According to the findings, luxury brand image influences consumers purchase intention on iPhone. This result is relevant to (Vijaranakorn &

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Shannon, 2016) that fluent luxury brand image form positive attitudes of consumers toward purchase intention. However, these findings are different from results of recent research by (Durand, 2017) & (Chou, 2017) which asserts that luxury brand image was not affected purchase intentions. Notwithstanding, based on the data from questionnaire asked on luxury brand image indicator, the respondents generally agreed with the high average statement “iPhone has the best quality”, and “iPhone is sophisticated”, with each average value are 4 and 3,7 (Table 13). Moreover, the statement “iPhone is precious”, “iPhone is unique”, “iPhone is attracting”, “iPhone is stunning”, “iPhone is conspicuous”,

“iPhone is expensive”, “iPhone is for the wealthy”, have medium averages 2,9 – 3,6.

b) The influence of luxury perceived price on purchase intention

Perceived price variable’s statistical analysis revealed that the regression coefficient was positive at 0.652. Perceived price variable’s T-test result show the p-value is 0,000. The p-value is less than the significant level (0,00 < 0,05).

According to the findings, perceived price influences consumers purchase intention on iPhone. This result is relevant to (Weisstein, Asgari, & Siew, 2014) (Ra & Narwa, 2021) that fluent perceived price form positive attitudes of consumers toward purchase intention. However, these findings ar different from results of recent research by (Lee & Stoel, 2014) & (Son & Jin, 2019) which asserts that perceived price was not affected purchase intentions.

Notwithstanding, based on the data from questionnaire asked on perceived price indicator, the respondents generally agreed with the high average statement “The price of an iPhone following its brand image”, “I want to buy an iPhone, albeit at a higher price”, and “I think the price of an iPhone is worth to buy”, with each average value are 4,2-4,5 (Table 14). Moreover, the statement “I think the price of an iPhone is very reasonable”, “An iPhone delivers more benefits than (the

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money) I would spend”, and “I think buying an iPhone can provide more significant benefit than that which would be paid”, have high averages 3,9 – 4,1.

c) The influence of luxury perceived quality on purchase intention

Perceived quality variable’s statistical analysis revealed that the regression coefficient was negative at -.260. Perceived quality variable’s T-test result show the p-value is 0,039. The p-value is less than the significant level (0,00 < 0,05).

According to the findings, perceived quality influences consumers purchase intention on iPhone. This result is relevant to by (Setiawan, Aryanto, &

Andriyansah, 2017) that fluent perceived quality form positive attitudes of consumers toward purchase intention. However, these findings are different from results of recent research by (Justin Beneke, 2013)& (Son & Jin, 2019) which asserts that perceived quality was not affected purchase intentions.

Notwithstanding, based on the data from questionnaire asked on perceived quality indicator, the respondents generally agreed with the very high average statement “The product is reliable”, with each average value are 4,5 (Table 15).

Moreover, the statement “The product is a high-quality product”, and “The workmanship of the product is good”, have high averages 4,-4,2.

Simultaneously Influences

The purpose of this study is to determine the simultaneous influence of luxury brand image, perceived price, and perceived quality on purchase intention. According to the analysis above, the regression analysis shows an R Square of 0.448. This indicates that 44,8% of purchase intention can be accounted for by luxury brand image, perceived price, and perceived quality, with the remaining 67% being influenced by factors outside the scope of the study. This finding also demonstrates that the 0.000 p-value is less than the 0.05 significance level. In conclusion, luxury brand image, perceived price, perceived product quality all influence consumers intention to purchase.