The Implementation of Product Quality, Pricing Strategy, and Strategic Location on Purchasing Intention at Matahari Department Store Metropolitan Mall in Bekasi

This study aims to determine the effect of product quality, pricing strategy and strategic location on

This study aims to determine the effect of product quality, pricing strategy and strategic location on purchasing intention at Matahari Department Store Metropolitan Mall Bekasi. This type of research is quantitative. The population in this study amounted to 85 people. Sampling technique using saturated sampling technique. The sample in this study amounted to 85 respondents. Data collection techniques by distributing questionnaires through Google Form. Hypothesis testing in this study using Multiple Linear Regression Analysis. The research subjects are consumers of this Department Store. The results of this study indicate that the independent variables of product quality, pricing strategy and strategic location have a significant effect on dependent variable of purchasing intention (Y).

INTRODUCTION
purchasing decisions. Prices are linked to revenues and define supply and marketing channels. What is most important, however, is that pricing decisions are aligned with the overall marketing plan (Anwar, et al., 2015).
According to (Kotler, 2012), the pricing method can be approached by selecting the final price by incorporating factors such as psychological pricing, where consumers use price as an indicator of quality, and company pricing policies with the aim of providing price quota. to salespeople to be given to consumers and for company profits (Anwar, et al., 2015).

Strategic Location
Place selection and distribution channels are tied to location through marketing channels. Place is a distribution choice that affects how easy it is for potential customers to reach services, claims (Tjiptono, 2015). Location refers to the area where a company must have a head office and run its business (Lupiyoadi, 2013). (Lupiyoadi, 2013) claims that the corporation decides where its activities and personnel are to be located, as well as location. Therefore, it can be said that the location of the business is very important because it affects the success and profits of the company itself. A strategic location also prioritizes the company's goals in order to continue to increase its profits.

Purchasing Intention
Interest is a source of motivation that can inspire someone to do what they want, claims (Hermanto, 2019). The stronger or closer the link, the greater the interest. Interest is essentially a kind of acceptance of a relationship between someone and something outside of himself. According to (Akbar & Suwitho, 2019), buying interest develops after someone sees a product and is stimulated by it, causing a desire to buy and own it. (Ningrum & Indrajaya, 2018) define buying interest as the desire to buy a product. Purchase intention develops when a consumer is influenced by the quality of a product, information about the product, such as price, purchase instructions, and weaknesses, as well as the advantages of the product when compared to the products of other competitors.
Desire to buy or prepare a product to fulfil demand is known as buying interest. According to (Kotler & Keller, 2016), buying interest is a behavior that appears to customers and consists of consumer confidence in the quality of a product and the price given to consumers by business actors. Research markers for determining purchase intention include internal and external needs, purchasing decisions, limited customer participation, and post-purchase behavior.

Conceptual Framework
Based on the literature review and the results of previous research, this research consists of three independent variables, namely Product Quality (X1), Price Strategy (X2), Strategic Location (X3) and one dependent variable, namely Purchasing Intention (Y). The steps of this research can be described as follows:

Research Hyphotesis
Based on the results of the analysis of the framework above, this hypothesis is developed as follows:  Hypothesis 1:  H0: it is suspected that there is no positive and significant influence between product quality and purchase intention.  Ha: it is suspected that there is a positive and significant influence between product quality and purchase intention.  Hypothesis 2:  H0: it is suspected that there is no positive and significant influence between pricing strategies on buying interest.  Ha: it is suspected that there is a positive and significant influence between pricing strategies on buying interest.  Hypothesis 3:  H0: allegedly there is no positive and significant influence between strategic locations on buying interest.  Ha: it is suspected that there is a positive and significant influence between strategic locations on buying interest.  Hypothesis 4:  H0: it is suspected that there is no positive and significant influence between product quality, pricing strategy, and strategic location on buying interest.  Ha: it is suspected that there is a positive and significant influence between product quality, pricing strategy, and strategic location on buying interest.

METHODOLOGY
The research location is the place where the researcher conducts research, especially in capturing phenomena or research that actually occurs from the object under study in order to obtain accurate research data. The determination of the research location was done deliberately. The research time used in this study is from April to June 2022. The research location is at Matahari Metropolitan Mall Bekasi.
This study uses quantitative methods, where research Quantitative researchers test the hypothesis with differential semantics. In this study there are 3 (three) types of independent variables that will be examined, namely Product Quality (X1), Price Strategy (X2), and Strategic Location (X3), on Purchasing Intention (Y).
The population is the whole element that will be used as a area of generalization, according to (Sugiyono, 2017). The conclusion is that the population element is the whole thing to be assessed. This population was selected with the aim of simplifying sample size estimates and reducing the scope of generalizations that might be made.
The sample is a representation of the size and composition of the population (Sugiyono, 2017). Purposive sampling was used as a sample strategy in this study, namely a sampling technique with several considerations (Sugiyono, 2017). Purposive sampling is a sampling approach that taking into account a number of factors or a set of criteria, and used in this study. This sampling approach, which according to (Sugiyono, 2017) is based on the nonprobability sampling method and cannot provide equal opportunities for elements or members of the sample, is based on the non-probability sampling method.
Determination of the number of samples according to (Hair et al., 1998) where the number of samples depends on the number of indicators, which is multiplied by 5 -10. The number of samples in this study are: Sample = number of indicators x 5 = 17x5 = 85. Based on the calculation above, there is a minimum sample using 85 minimum samples.
In a study where data collection is the main objective, the data collection procedure is the most important phase. Researchers decided to use a questionnaire to collect data. The questionnaire itself is a tool for data collection, where respondents fill out questions or statements given by researcher (Sugiyono, 2017). Matahari Metropolitan Mall Bekasi customers will be the respondents in this survey.
One of the difficulties in research is obtaining precise and objective data. This is important because conclusions can only be trusted if they are based on reliable data. Therefore, in this study it is very important to know how high the validity and reliability of the measuring instruments (instruments) used are:

Validity Test
The validity of a test indicates how well the test assesses what it is designed to measure. The accuracy of measuring instruments in carrying out their work and achieving their goals is called validity. The following criteria are used to determine validity testing:

Normality Test
The normality test is used in this study to determine whether a data is normally distributed or not. Factors of production quality (X1), pricing strategy (X2), strategic location (X3), and product purchase intention (Y) are tested in this normality test. The Kolmogrov-Smirnov Goodness of Fit Test was used in this investigation to determine whether the data is normally distributed. This data is also compared with the Normality Probability Plot.

Multicollinearity Test
The term multicollinearity refers to the existence of a perfect or definite linear relationship between some or all of the variables that describe the regression model. Tolerance and Variance Inflation Factor (VIF) variables can be used to detect the presence or absence of multicollinearity by comparing the following:  VIF < 5, there is no multicollinearity.  Tolerance > 0.1 means there is no multicollinearity.

Heteroscedasticity Test
The heteroscedasticity test determines whether individuals from a group have the same variance or not. That is, if the variance of the independent variable is constant (same) for any given value, the independent variable is said to be homoscedasticity.

Multiple Linear Regression Analysis
Multiple linear regression analysis was used by researchers to test the effect of the independent variables on the dependent variable. The SPSS (Statistical Product and Service Solution) software application was used by the researchers. The following is the multiple regression equation used: Y = a + b1X1 + b2X2 + e Information : Y = Dependent variable X1, X2, X3 = Independent variables b1, b2 = Regression coefficient (slope) Coefficient a = intercept coefficient or the cut between the Y vertical axis and the linear function line for the value of Y e = Error

Coefficient of Determination (R 2 )
According to (Ghozali, 2016), the coefficient of determination (R 2 ) is used to find out how well the model can explain variations in the dependent variable. The coefficient of determination test (R 2 ) is used to determine how much influence the proportion of independent factors has on the dependent variable. Adjusted R Square (Adjusted R 2 ) is used in this study because it is adjusted for the number of variables included in the study. When one independent variable is added to the model, the value of Adjusted R 2 Square can grow or decrease.

Hyphotesis Test Partially t test
The t test was carried out by comparing the significance level of each independent variable with a significance level of α = 0.05. The t test is used to prove, then the hypothesis test is used to find out whether the hypothesis is rejected or accepted. The hypothesis test consists of a partial test (t test), simultaneous test (F test) and test of the Coefficient of Determination R 2 . In this study, partial testing (t test) and simultaneous testing used for hypothesis testing (F test). Test objectives this hypothesis is to see whether there is a relationship between the independent variables of manufacturing quality (X1), pricing strategy (X2), and strategic location on purchasing intention (Y). The research variables were analyzed by calculating dividends per share and capital structure, which were then placed on the work table and assessed for their influence on product purchase intentions using SPSS software. The t test is performed as follows: To test the research hypothesis above, we must first know the basis for decision making in the partial t test. In this case there are two references that we can use as a basis for decision making, first by looking at the significance value (Sig), and secondly by comparing the calculated t value with the t table.
1. Based on the significance value (Sig). a. If the value is Significance (Sig). <probability 0.05 then there is an influence of the independent variable (X) on the dependent variable (Y) or the hypothesis is accepted. b. If the value is Significance (Sig). > probability 0.05, so there is no effect of the independent variable (X) on the dependent variable (Y) or the hypothesis is rejected.
2. Based on the comparison of the value of t count with t table a. If the value of t count > t table, then there is an influence of the independent variable (X) on the dependent variable (Y) or the hypothesis is accepted. b. If the value of t count < t table then there is no effect of the independent variable (X) on the dependent variable (Y) or the hypothesis is rejected.

Validity Test
The validity of this questionnaire was tested using this validity test. Validity is the ability of the questionnaire used to measure what is to be measured. If someone consistently or steadily responds to the questions on the questionnaire, then it can be said that the questionnaire is valid.
By comparing the estimated r value with r table, validity test can be done. SPSS (Statistical Product and Service Solution) output is used to determine the value of r.  (2023) Based on the information in the table 1 above, it can be concluded that each question indicator is genuine because each output shows significant findings for all indicators (0.000) and the data is valid.

Reliability Test
Reliability test can be used to determine the degree of reliability of a measuring instrument. Cronbach's alpha coefficient was used in this study to assess the reliability of all statement items. The reliability of an instrument whose score is in the form of a range of values or in the form of a scale is determined using this method. The following is the basis for reliability testing: 1. If the Cronbach Alpha value is > 0.60, the data to be tested is declared reliable. 2. If the Cronbach Alpha value is <0.60, the data to be tested is declared unreliable. Based on the data in the table above, it shows that the results of the Cronbach's Alpha value are 0.907 > 0.60, it can be concluded that the questionnaires distributed in this study are reliable.

Normality Test
The normality test is useful for determining whether the dependent variable and independent variable in a regression model are normally distributed or not. The Kolmogrov-Smirnov statistical test was used in checking the normality of this study. The table below shows the findings of the Kolmogrov-Smirnov test-based normality test:  (2023) Based on the table data above, it can be seen that the Asymp. Significant (2-tailed) on variable (X1) is 0.000 < 0.05, then variable (X2) is 0.000 < 0.05, then variable (X3) is 0.000 < 0.05, and on variable (Y) of 0.000 < 0.05. So it can be concluded that the data in this study are not normally distributed.

Multicollinearity Test
To determine whether the regression model identifies a relationship between the independent variables, the multicollinearity test is helpful. By examining the Tolerance and VIF of each independent variable, it is possible to determine whether a multicollinearity test holds. If the Tolerance value > 0.10 and the VIF value < 10, then the data is free from multicollinearity. Based on the table output coefficients in the collinearity section from statistics, it is known that the tolerance value for the Product Quality variable (X1) is 0.414 > 0.10, for the Price Strategy variable (X2) is 0.322 > 0.10, and for the Strategic Location variable is 0.490 > 0.10. Meanwhile, the Factor Inflation Variance (VIF) value for the Product Quality variable (X1) is 2.413 < 10.00 for the Price Strategy variable (X2) is 3.106 < 10.00, and for the Strategic Location variable (X3) 2.041 < 10.00. Then referring to the basis of decision making in the multicollinearity test, it can be concluded that there is no multicollinearity, which means that the model developed is correct.

Heteroscedasticity Test
The purpose of the heteroscedasticity test is to find out whether there is an inequality of variance between the residuals of one observer and the residuals of other observers in the regression model. Heteroscedasticity does not occur, that is, if there is no visible pattern, and the points spread above and below the number 0 on the Y axis, according to the scatter plot between the predicted values of the independent variables, ZPRED, and the SRESID residual.

Figure 2. Heteroscedasticity Test
Because the points are evenly spaced above and below the value on the Y axis, it can be seen from the figure above that there is no heteroscedasticity. Given the independent variables, including product quality, pricing strategy, and strategic location, it can be argued that the regression model is practical to use to forecast product purchase intentions.

Multiple Linear Regression Analysis Test
Multiple Linear Regression Analysis is used to measure the impact of location, price, and product quality on consumer interest in making a purchase. The SPSS program was used to perform statistical analysis for this study. c. The coefficient of 1 variable X2 = 0.185 means that every 1% increase in X1 will cause a 0.185% increase in Y. d. The coefficient of 1 variable X3 = 0.409 means that every 1% increase in X1 will cause a 0.409% increase in Y.

Hyphotesis Test Partially t Test
The t statistical test shows how far the influence of one independent variable has on the dependent variable. The t test in this study was carried out by comparing the significance of t with an α = 0.05, buying interest is as follows: With n = 85, df 85-2 = 83 is obtained, then t table is 1.988. Based on the data above, it can be concluded that the variable product quality, pricing strategy, and strategic location obtained a t count of 2.618 > t table of 1.988. So it can be concluded that there is a significant effect partially.

Simultaneous F Test (Test F)
The F test is used to meet the effect simultaneously or together of the independent variables of product quality (X1), pricing strategy (X2), and strategic location (X3) on purchasing intention. With n = 85, we get df 85-3 = 82 F table = 2.72. Based on the data above, it can be concluded that the sig value influences the variable product quality (X1), pricing strategy (X2), and strategic location (X3) have simultaneously effect on purchasing interest (Y) is 0.000 < 0.05 and the value of F count of 67.338 > F table 2.72. So that it can be stated that H4 is accepted, which means that there is an effect on variable X simultaneously on variable Y.

Determination Coefficient Test (R 2 )
To determine how much the variation in the dependent variable can be explained by fluctuations in the independent variable, regression analysis is also required. The coefficient of determination (R 2 ) is used as the result. The coefficient of determination has a value between 0 and 1. The potential for independent variables to influence the dependent variable in this study is very limited if the coefficient value is close to zero. If the coefficient value is close to one, then almost all of the independent variables can explain the dependent variable, indicating that it makes a perfect contribution to the dependent variable.  (2023) Based on the Coefficient of Determination Test (R 2 ) in the table above, it can be seen that the Adjusted R Square value is 0.703 (70.3%). This shows that the three independent variables are Product Quality (X1), Pricing Strategy (X2), and Strategic Location (X3) have a significant effect on dependent variable of purchasing intention (Y). In this study, it was able to explain a value of 0.703 (70.3%) for the Dependent variable, namely purchasing intention (Y). Assess the remainder (100% -70.3% = 29.7%) which is influenced by other variables outside the regression model in this study.

CONCLUSIONS AND RECOMMENDATIONS
The conclusions that can be drawn from the results of this study are as follows: 1. The results of partial testing of product quality variables affect the interest in buying fashion products. Product quality will make the company benefit and gain customer loyalty. Shows that Matahari Metropolitan Mall Bekasi always prioritizes product quality to increase profits. 2. The partial test results of the pricing strategy variable affect the interest in buying fashion products. If the tempting pricing strategy is implemented at Matahari Metropolitan Mall Bekasi, it will increase the interest in buying fashion products. 3. The partial test results of the strategic location variable affect the interest in buying fashion products. The strategic location determines the buying interest of consumers to buy fashion products at Matahari Metropolitan Mall Bekasi. 4. That the results of simultaneous testing of product quality, pricing strategy, and strategic location variables simultaneously have a significant effect on the dependent variable, namely the intention to buy fashion products by 70.3% and the remaining 29.7% are other variables not examined in this study. So the size of the interest in buying fashion products is very influential if the company has product quality, pricing strategy, and strategic location.