THE EFFECT OF PRODUCT QUALITY ON CONSUMER BUYING INTEREST WITH CUSTOMER SATISFACTION AS A VARIABLE INTERVENING ON THE MARKET SHARE OF LONDON PUBLIC HEALTHY CARE PRODUCT

This study aims to find out how the influence of product quality on consumer buying interest with customer satisfaction as an intervening variable on the market share of london public healthy care product. The research method used is the method of qualitative data and quantitative data. While the data used is primary data. The data analysis method in this study uses simple linear regression analysis to obtain a comprehensive picture of the influence of product quality variables on consumer buying interest using the SPSS 25 for Windows program. To find out whether there is a significant effect of the independent variable on the dependent variable, a simple linear regression model is used.


INTRODUCTION
Competition in the increasingly open business world makes entrepreneurs look for the right strategy to market their products. Buying interest is obtained from a learning process and a thought process that forms a perception. According to Sukmawati and Suyono in Pramono quoted from Annafik and Rahardjo (2012), buying interest is part of the behavioral component in consuming attitudes. Consumer buying interest is the stage where consumers form their choices among several brands that are incorporated in the choice set. Then in the end make a purchase at an alternative that he likes the most or the process that consumers go through to buy an item or service based on various considerations. Following are the facts of consumer purchases through the black market which are more every month than the overall percentage data with the following data: The company diversifies its products with consideration to take advantage of existing strengths in the current business in the fields of marketing, production, technology or finance. Meanwhile, Kotler & Keller's translation by Bob Sabran (2012: 145) defines information from marketers and competitors. Marketers who want to excel in competition must pay attention to customer expectations and customer satisfaction.
Satisfaction arises because of the fulfillment of the need for products and services as well as the benefits that consumers get. When consumers use products or services and obtain satisfaction and more value than expected, it can form a positive attitude, namely consumer trust in the company. Consumer satisfaction needs to be maintained and improved to maintain and create customer loyalty.
Richard L. Oliver (in Fandy Tjiptono, 2015: 23) defines customer satisfaction as a feeling of pleasure or disappointment that a person gets from comparing perceived product performance (or results) and expectations. A consumer is satisfied or not, very much depends on product performance (Perceived Performance) compared to customer expectations, and whether consumers interpret there is a deviation or gap between performance and these expectations.Consumer satisfaction is defined as a situation where consumer expectations of a product are in accordance with the reality received by consumers. If the product is far below expectations, consumers will be disappointed. Conversely, if the product meets expectations, consumers will be satisfied. Consumer expectations can be known from their own experiences when using these products, information from other people, and information obtained from advertisements or other promotions (Kotler in Sopiah and Sangadji, 2013: 181) According to Kivetz and Simonon in Sopiah and Sangadji (2013: 182) ), consumer satisfaction can establish a harmonious relationship between producers and consumers, create a good basis for repeat purchases and create consumer loyalty, form word of mouth recommendations that can benefit the company. Customer expectations will continue to develop according to changes in the environment that provide information and increase in customer experience which will affect the level of satisfaction felt by customers. Customer expectations are believed to have a big role in determining the quality and customer satisfaction. Basically there is a close relationship between quality determination and customer satisfaction.

Purchase Interest
Durianto (2013: 58), reveals that "Buying interest is the desire to own a product, buying interest will arise if a consumer has been affected by the quality and quality of a product, information about the product, ex: price, how to buy and the weaknesses and advantages of the product compared to other brands. Buying intention is the selection of two or more alternative choices, which means that a person can make a decision, there must be a variety of alternative choices. The decision to buy can affect how the decision-making process is carried out.

Data Types and Sources 1. Data Type
According to Sugiyono (2015), the types of data are divided into 2, namely qualitative and quantitative. This study uses data types in the form of qualitative and quantitative. a. Qualitative Data Qualitative data according to Sugiyono (2015) is data in the form of words, schemes, and pictures. The qualitative data of this research are the names and addresses of the research objects b. Quantitative Data Quantitative data according to Sugiyono (2015) is data in the form of numbers or qualitative data that is numbered.

Data Source
According to Sugiyono (2012: 193) the types of data are divided into two, namely: a. Primary data is a data source that directly provides data to data collectors. In this study, the primary data was in the form of data from questionnaires and interviews conducted by researchers. b. Secondary data is a source that does not directly provide data to data collectors, for example through other people or through documents.

Data collection technique
The data collection technique used is by: 1. Questionnaire Questionnaires or questionnaires are a number of questions or written statements about factual data or opinions relating to the respondent, which are considered facts or truths that are known and need to be answered by the respondent (Suroyo anwar 2009: 168.). In this questionnaire, a closed question model will be used, namely questions that have been accompanied by alternative answers before so that respondents can choose one of the alternative answers. The processing of data in this study uses a Likert Scale.According to Sugiyono (2013: 132) "Likert scale is used to measure attitudes, opinions and perceptions of a person or group of people about social phenomena". In answering this Likert scale, the respondent only gives a mark, for example a checklist or a cross on the answer chosen according to the statement. The questionnaire that has been filled in by the respondent needs to be scored. The following is the weight of the rating on the Likert scale.

Interview
According to Sugiyono (2015: 231) interviews are a data collection technique if the researcher wants to conduct a preliminary study to find problems that must be studied, but also if the researcher wants to know things from respondents that are more indepth.

Library Studies
Literature study, according to Nazir (2013) data collection technique by conducting a review study of books, literature, notes, and reports that have to do with the problem being solved.

Validity Test
Validity testing uses SPSS version 25.00 with criteria based on the calculated r value as follows: a) If r count > r table orr count < -r table then the statement is declared valid. b) If r count < r table orr count > -r table then the statement is declared invalid.
This test was carried out on 40 respondents, then df = 70-k = 68, with α = 5%, an r table value of 0.235 was obtained (Ghozali, 2016), then the calculated r value would be compared with the r table value as shown in table 4.5 below :  Table 4.5 shows that all statement points, both Product Quality (X), Purchase Interest (Y) and Customer Satisfaction (Z) variables, have a higher r count value than the r table value, so that it can be concluded that all statements for each variable are declared valid .

Reliability Test
Reliability is an index that shows the extent to which a measuring device can be trusted or relied on. According to Sugiyono (2013) A factor is declared reliable if the Cronbach Alpha is greater than 0.6. Based on the results of data processing using SPSS 25.00, the following results are obtained: Table 4.6 Reliability Test Results

Constant Reliability
Product Quality (X) 0.795 0.6 Reliable Buying Interest (Y) 0.814 0.6 Reliable Customer Satisfaction (Z) 0.774 0.6 Reliable Source: Data processed from attachment 3 (2020) Based on the reliability test using Cronbach Alpha, all research variables are reliable/reliable because Cronbach Alpha is greater than 0.6, the results of this study indicate that the measurement tools in this study have fulfilled the reliability test (reliable and can be used as a measuring tool).

Test the Classical Assumptions of Equation 1
As for testing the classical assumptions with the SPSS program25.00 which was carried out in this study included: a. Normality test The Normality Test aims to test whether in the regression model, the confounding or residual variables have a normal distribution (Ghozali, 2016). Data normality testing can be done using two methods, graphics and statistics. The normality test for the graphical method uses the normal probability plot, while the normality test for the statistical method uses the one sample Kolmogorov Smirnov test. The normality test using the graphical method can be seen in the following figure:

Figure 4.3 Normal P Plot
Data that is normally distributed will form a straight diagonal line and residual data plotting will be compared with the diagonal line, if the residual data distribution is normal then the line that describes the actual data will follow the diagonal line (Ghozali, 2016). The test results using SPSS 25.00 are as follows: Source: Data processed from attachment 4 (2020) From the output in table 4.7 it can be seen that the significance value (Monte Carlo Sig.) of all variables is 0.129. If the significance is more than 0.05, then the residual value is normal, so it can be concluded that all variables are normally distributed.

b. Heteroscedasticity Test
The heteroscedasticity test aims to test whether from the regression model there is an inequality of variance from the residuals of one observation to another. A good regression model is one that has homoscedasticity or does not have heteroscedasticity. One way to detect the presence or absence of heteroscedasticity is with the Glejser test, in the glejser test, if the independent variable is statistically significant in influencing the dependent variable then there is an indication of heteroscedasticity occurring. Conversely, if the independent variable is not statistically significant in influencing the dependent variable, then there is no indication of heteroscedasticity. This is observed from the significance probability above the 5% confidence level (Ghozali, 2016).
The results of data processing using SPSS 25.00 show the results in the following table:

Simple Linear Regression Testing
Multiple linear regression testing explains the magnitude of the role of the Product Quality variable (X) on the Customer Satisfaction variable (Z). Data analysis in this study used multiple linear regression analysis usingSPSS 25.0 for windows. The analysis of each variable is explained in the following description: Source: Data processed from attachment 4 (2020) Based on these results, the multiple linear regression equation has the formulation:Z = a + b X + ɛ, so the equation is obtained: Z = 5.104 + 0.681 X + ɛ The description of the multiple linear regression equation above is as follows: a. The constant value (a) of 5.104 indicates the magnitude of the Customer Satisfaction variable (Z) if the Product Quality variable (X) is equal to zero. b. The regression coefficient value of the Product Quality variable (X) (b1) is (0.681) indicating the large role of the Product Quality variable (X) on the Customer Satisfaction variable (Z). This means that if the Product Quality variable factor (X) increases by 1 unit value, it is predicted that the Customer Satisfaction variable (Z) will increase by (0.681) unit.

Coefficient of Determination (R2)
The coefficient of determination is used to see how much the independent variable contributes to the dependent variable. The greater the value of the coefficient of determination, the better the ability of the independent variable to explain the dependent variable. If the determination (R 2 ) the greater (closer to 1), it can be said that the influence of the variableProduct Quality (X)is big againstvariable Customer Satisfaction (Z).
The value used in viewing the coefficient of determination in this study is in the adjusted R square column. This is because the value of the adjusted R square is not susceptible to the addition of independent variables. The value of the coefficient of determination can be seen in Table 4.10 below:

Test the Classical Assumptions of Equation 2
As for testing the classical assumptions with the SPSS program25.00 which was carried out in this study included: a. Normality test The Normality Test aims to test whether in the regression model, the confounding or residual variables have a normal distribution (Ghozali, 2016). Data normality testing can be done using two methods, graphics and statistics. The normality test for the graphical method uses the normal probability plot, while the normality test for the statistical method uses the one sample Kolmogorov Smirnov test. The normality test using the graphical method can be seen in the following figure:

Figure 4.4 Normal P Plot
Data that is normally distributed will form a straight diagonal line and residual data plotting will be compared with the diagonal line, if the residual data distribution is normal then the line that describes the actual data will follow the diagonal line (Ghozali, 2016). The test results using SPSS 25.00 are as follows Source: Data processed from attachment 4 (2020) From the output in table 4.11 it can be seen that the significance value (Monte Carlo Sig.) of all variables is 0.657. If the significance is more than 0.05, then the residual value is normal, so it can be concluded that all variables are normally distributed.

b. Multicollinearity Test
The multicollinearity test aims to determine whether there is a correlation between the independent variables in the regression model. The multicollinearity test in this study was seen from the tolerance value or variance inflation factor (VIF). The calculation of the tolerance value or VIF with the SPSS 25.00 program for windows can be seen in Table 4.12 below: than 10. Based on the results of the calculation above it can be seen that the tolerance value of all independent variables is greater than 0.10 and the VIF value of all independent variables is also less than 5 so that no correlation symptoms occur in the independent variables. So it can be concluded that there are no symptoms of multicollinearity between independent variables in the regression model.

c. Heteroscedasticity Test
The heteroscedasticity test aims to test whether from the regression model there is an inequality of variance from the residuals of one observation to another. A good regression model is one that has homoscedasticity or does not have heteroscedasticity. One way to detect the presence or absence of heteroscedasticity is with the Glejser test, in the glejser test, if the independent variable is statistically significant in influencing the dependent variable then there is an indication of heteroscedasticity occurring. Conversely, if the independent variable is not statistically significant in influencing the dependent variable, then there is no indication of heteroscedasticity. This is observed from the significance probability above the 5% confidence level (Ghozali, 2016).
The results of data processing using SPSS 25.00 show the results in the following table:  Data processed from attachment 4 (2020) Based on these results, the multiple linear regression equation has the formulation:Y = a + b1X + + b3Z + ɛ, so the equation is obtained: Y = 1.156 + 0.638 X + 0.307 Z + ɛ The description of the multiple linear regression equation above is as follows: a. The constant value (a) of 1.156 indicates the amount of Purchase Minutes (Y) if Product Quality (X) and Customer Satisfaction (Z) are equal to zero. b. The regression coefficient value of Product Quality (X) (b1) is 0.638 indicating the large role of Product Quality (X) on Purchase Intention (Y) assuming the variable Customer Satisfaction (Z) is constant. This means that if the Product Quality factor (X) increases by 1 value unit, it is predicted that Purchase Interest (Y) will increase by 0.638 value units assuming constant Customer Satisfaction (Z). c. The regression coefficient value of Customer Satisfaction (Z) (b3) of 0.307 indicates the large role of Customer Satisfaction (Z) on Purchase Intention (Y) assuming the variable Product Quality (X) is constant. This means that if the Customer Satisfaction factor (Z) increases by 1 value unit, it is predicted that Purchase Interest (Y) will increase by 0.307 value units assuming constant Product Quality (X).

Coefficient of Determination (R2)
The coefficient of determination is used to see how much the independent variable contributes to the dependent variable. The greater the value of the coefficient of determination, the better the ability of the independent variable to explain the dependent variable. If the determination (R 2 ) the greater (closer to 1), it can be said that the effect of variable X is large onCustomer Satisfaction(Z).
The value used in viewing the coefficient of determination in this study is in the adjusted R square column. This is because the value of the adjusted R square is not susceptible to the addition of independent variables. The value of the coefficient of determination can be seen in Table 4  Source: Data processed from attachment 4 (2020) Based on table 4.15, it can be seen that the value of the adjusted R square is 0.812 or 81.2%. This shows that Customer Satisfaction (Z) and Product Quality (X) can explain Purchase Intention (Y) of 81.2%, the remaining 18.8% (100% -81.2%) is explained by other variables outside the model this research.Like between products, price, location, and quality of service have a correlation. For product quality variables, interest is influenced by products that provide the greatest contribution compared to other product variable question items.

Hypothesis testing a. t test (Partial)
The t statistical test is also known as the individual significance test. This test shows how far the influence of the independent variables partially on the dependent variable.
In this study, partial hypothesis testing was carried out on each independent variable as shown in Table 4.16 below:  From table 4.16, a tcount value of 11.107 is obtained. With α = 5%, ttable (5%; nk = 68) a ttable value of 1.995 is obtained. 0.000 <0.05, it can be concluded that the first hypothesis is accepted, meaning that the product quality variable(X) positive and significant effecton Customer Satisfaction (Z). In line with research conducted by (Kotler and Armstrong, 1997;in Rizan and Andika, 2011) product quality reflects the product's ability to carry out its duties which include durability, reliability or progress, strength, ease of packaging and product repair and other characteristics . Source: Data processed from attachment 4 (2020) a. Hypothesis Test of the influence of Product Quality(X)to Buying Interest (Y) The form of hypothesis testing based on statistics can be described as follows: Decision Making Criteria: a) Accept H0 If tcount < ttable or -tcount> -ttable orSig value. >0.05 b) Reject H0 If tcount ≥ ttable or -tcount ≤ -ttable orSig. < 0.05 From table 4.17 the value of t is obtained count of 7.644 With α = 5%, ttable (5%; nk = 68) obtained a ttable value of 1.995. From this description it can be seen that tcount (7.644) > ttable (1.995), and a significance value of 0.000 <0.05, it can be concluded the second hypothesis is accepted, that isProduct quality(X) positive and significant effecton Buying Interest (Y). In line with what was stated by (Mowen and Minor, 2010), product quality is defined as a customer's overall evaluation of the good performance of goods or services. Companies must have good quality or according to the price offered when selling products or services in running a business. b. Hypothesis Test of the effect of Customer Satisfaction (Z) on Purchase Intention (Y) The form of hypothesis testing based on statistics can be described as follows: Decision Making Criteria: a) Accept H0 If tcount < ttable or -tcount> -ttable orSig value. >0.05 b) Reject H0 If tcount ≥ ttable or -tcount ≤ -ttable orSig. < 0.05  From table 4.17, a tcount value of 3.121 is obtained. With α = 5%, ttable (5%; nk = 68) a ttable value of 1.995 is obtained. From this description it can be seen that tcount (3.121) > ttable (1.995), and its significance value is 0.003 < 0.05, it can be concluded that the third hypothesis is accepted, meaningCustomer Satisfaction (Z)influentialpositive andsignificanton Buying Interest (Y). In line with research on customer satisfaction is a person's feeling of pleasure or disappointment that comes from a comparison between his impression of the performance (outcome) of a product with his expectations. Satisfied consumers will buy other "products" sold by the company, as well as become effective "marketers" through positive word of mouth.

b. Path Analysis
In order to prove that whether a variable is capable of being a variable that mediates the relationship between the independent variable and the dependent variable, a direct and indirect effect calculation will be carried out between the independent variable and the dependent variable. If the indirect effect of the independent variable on the dependent variable through the intervening variable is greater than the direct effect of the independent variable on the dependent variable, then this variable can be a variable that mediates between the independent variable and the dependent variable (Ghozali, 2016). To carry out direct and indirect calculations, it is carried out from the standardized values of the regression coefficients equations I and II as follows:  Furthermore, the value of standardized coefficients beta will be entered into the path analysis image as follows:

Figure 4.5 Path Analysis
In Figure 4.3 the path analysis shows the direct effect of variable X on variable Y of 0.670. While the indirect effect through variable Z is 0.803 x 0.273 = 0.2192, the calculation results obtained show that the direct effect through variable X is greater than the indirect effect on variable Y. These results can be seen in table 4.20 below.

Conclusion
Based on the results of the research and discussion in the previous chapter, it can be concluded as follows: 1. Based on the results of the study, the researchers concluded that product qualityon market share of london public healthy care product is descriptively in the high classification or in the good category. This can be seen from the variable product quality(X) significant effecton customer satisfaction (Z). In theorycustomer satisfaction needs efforts to improve good product quality when customer satisfaction decreases by providing the right product quality will be able to increase customer satisfaction. 2. Based on the results of the study, the researchers concluded that product qualityon market share of london public healthy care product is descriptively in a high classification or has a lot of influence on buying interest. Where is product quality (X) significant effecton buying