Scope of Research Variable Operational Research

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CHAPTER III RESEARCH METHODOLOGY

A. Scope of Research

In writing this essay were made respondents in the study is the consumers who shopped at Carrefour Lebak Bulus. Research conducted at Carrefour Lebak Bulus, when the study began on August 23, 2016. By providing a questionnaire to the customer Carrefour. As will be discussed is about how much the Service Quality, Sales Promotion and Customer Satisfaction infuencing Customer Loyalty on Carrefour Lebak Bulus . As the independent variable in this study was given the symbol of Service Quality X1, Sales Promotion X2 and Customer Satisfaction X3. While the dependent variable in this study was Customer Loyalty given the symbol Y.

B. Determination Sample Method

1. Population

Population is the generalization region consisting of the objects or subjects that have certain qualities and characteristics defined by the researchers to learn and then be inferred Sugiono, 2013:115. The population in this study is member or non-member customer carrefour Lebak Bulus. 38

2. Sample

The sample is part of the number and characteristics possessed by the population, to be investigated and considered to be representative of the overall population and a smaller number of populations Sugiyono, 2013:116. The sample in this study was the visitors who shop at Carrefour Lebak Bulus. In this study writing, researchers used a method of Purposive sampling. Purposive sampling is which the population elements are purposively selected based on the judgment of the researcher. The researcher, exercising judgment or expertise, chooses the elements to be included in the sample, because researcher believe that they are representative of interest or are otherwise appropriate Malhotra 2009: 377. Due to the number of population is not known for sure to know the size of the sample that is using a convenience sampling technique. Based on this, researchers select to filter the existing questionnaires, if these people are known. For example, used the sample to estimate the mean value, If used to estimate μ, we can 1- α confident that the error does not exceed a certain value е when the sample size of n, Where: Riduwan and Kuncoro, 2013:255 39 Information: = Number of samples Zα = Size trust level with α = 0,05 confidence level 95 means Found in table 1,96 = Standard deviation = Standart error that can be tolerated 5 = 0,05 By calculation: n = From the calculation results, the samples obtained in the amount of 96.04 to make it easier then rounded to 100 respondents. So in this study will use the 100 respondents to the research sample.

C. Data Collection Technique

Data processing techniques are ways that can be used by researchers to collect data. In this study, researchers will gather data could be a source of primary and secondary sources:

1. Primary Data

Data used in this study are primary data. Primary data is data obtained directly from the source first. Data collection is done by: 40 a. Interview method Interview method is a method of data collection with a question and answer directly to the respondents to obtain more accurate data respecting with issues to be discussed. b.Questionnaire method Questionnaire method is a method to obtain data that is done by providing a list of questions that will be filled by respondents including questions about the variable service quality, sales promotion and customer satisfaction toward customer loyalty on Carrefour Lebak Bulus. Questioner is a formalized set of questions for obtaining information for respondents. It has three specific objectives Malhotra, 2009: 330 Three specific objectives Malhotra, 2009: 330: 1 The overriding objective is to translate the researcher‘s information needs into a set of specific questions that respondents are willing and able to answer. 2 A questionnaire should be written to minimize demands imposed on respondents. It should encourage them to participate in the entire interview, without biasing their responses. 41 3 A questionnaire should minimize response error. These errors can arise from respondents who give inaccurate answers or from researchers incorrectly recording or analyzing their answer. The questionnaire for this research will be filled out by the respondents and will be include question about the variable service quality, sales promotion and customer satisfaction toward customer loyalty on Carrefour Lebak Bulus. In this questionnaire there are two parts, namely: Part I: Concerning the respondent data those are name, gender, level of education and monthly income. Part II: On the list of questions that will be filled by the respondent. This study used a Likert scale measuring agreement and disagreement of respondents in responses proposed the statement. Likert scale is a measurement with five response categories ranging from ―strongly disagree‖ to ―strongly agree‖, which requires the respondents to indicate a degree of agreement or disagreement with each of a series of statements related to the stimulus objects Maholtra, 2009:264. The score of questionnaire assessment figures resulted in this study is according to the Likert scale described in the methods used to measure attitudes, opinions, and perceptions of a person or group of persons on a social phenomenon Sugiyono, 2013:93. 42 A measurement of each variable in this research using Likert scale to measure attitudes, opinion and perceptions of individuals or groups social phenomena Sugiyono, 2013:94. By using a Likert scale, the measurement variable is an indicator variable that will be outlined. Using a Likert scale of five 5 levels to express the attitude or the respondent‘s answer as follows: Table 3.1 Likert Scale Strongly agree always very positive very satisfactory 5 Agree often positive satisfying 4 Neutral hesitant sometimes 3 Disagree never negative unsatisfactory 2 Strongly disagree strongly never very dissatisfy 1 Source: Sugiyono, 2013:133

2. Secondary Data

A secondary source is a source that does not directly provide data to data collectors, for example through others or through documents. Secondary data were generally obtained by the founders to provide additional information and images for further processing. Secondary data used in this study was obtained from books, journals, literature or other writings that are considered related to the problems studied by using written reports or 43 documentation of previous studies and other information that can be retrieved through the system online internet. In the process of secondary data collection, the researchers collected data related to and associated with the research. So it can support materials to support this research. D. Methods of Data Analysis 1. Validity Test Validity test used to measure the validity of the data in the study. According Ghozali, 2011:52 validity test is used to measure the legitimacy of a questionnaire. Validity test used to measure the validity of a questionnaire. Testing was conducted using Pearson correlation, guidelines for a model is said to be valid if the significance level below 0,05 then the questions can be said to be valid. In addition, the criteria used in determining whether or not valid questions or statements used in this study is the 95 confidence level α = 5, the number of respondents as many as 30 respondents to pre-test, and compared with the value of r table = 0,361 in the can of degree of freedom df = n - 2, in this case n is the number of pre-test sample of 30 respondents. A questionnaire is said to be valid when the value of r count larger than r table. Test will test the validity of each variable used in this study. Here are the results to test the validity of the variable service quality, 44 sales promotion and customer satisfaction with 30 samples of respondents.

2. Reliability Test

Reliability is a tool to measure a questionnaire which is an indicator of variables or constructs. A questionnaire said to be reliable or reliable if someone answers to questions are consistent or stable over time. SPSS provides the facility to measure the reliability of the statis tical test Cronbach Alpha α. a construct or variable said to be reliable if it provides value α 0,70 Ghozali, 2011:47-48. In other words able to obtain precise data on the variables studied. Testing of each item used item analysis, the reliability test is a measure of stability and reliability testing instruments used in this study using Cronbachs Alpha formula.

E. Classical Assumption Test

1. Normality Test

Normality test aims to test whether the regression model or residual confounding variables have a normal distribution. Studies that use a more reliable method to test the data have a normal distribution or not by looking at the Normal Probability Plot. A good regression model is to have a normal data distribution or dissemination of statistical data on a diagonal axis of the graph of a normal distribution Ghozali, 2011:160. 45 There are several ways to detect normality to see the spread of the data points on the diagonal axis of the graph. There are two ways to detect whether residual normal distribution or not is by analysis of graphs and statistical tests test Kolmogorov - Smirnov, with the following explanation Ghozali, 2011:147. a. Normality Test in Charts One of the easiest ways to see the residual normality is to look at the histogram graph that compares the distribution of observation data with which to detect the normal distribution. However, just by looking at the histogram this can be misleading, especially to the small sample size. More reliable method is to look at normal probability plots comparing the cumulative distribution of the normal distribution. The normal distribution will form a straight diagonal line and residual plotting the data will be compared with a diagonal line Ghozali, 2011:147. Basis for a decision in the normality test is: 1 If the data is spread around the diagonal line and follow the direction of the diagonal line, the regressions meet the assumption of normality. 2 If the data spread of the diagonal line and did not follow directions or diagonal line, the regression model did not meet the assumption of normality. 46 b. Normality Test in Statistics Normality test graphically can be misleading if not carefully look at it. Therefore it is recommended to complete normality test graphically statistical normality test Ghozali, 2011:163. In addition to seeing the normal curve P-plot, the normality test can also be performed using the Kolmogorov- Smirnov test. In Kolmogorov Smirnov test the hypotheses that apply are: H = Samples derived from data or population v normally distributed. Ha = Samples derived from data or populations that are not normally distributed. In this test if sig. 0,05 then the data is not distributed normally. However, if the value of sig. 0,05 then normally distributed data Santoso, 2011:193-196.

2. Multicollinearity Test

Multicollinearity test aims to test whether the regression model found a correlation between free variables of service quality, sales promotion, and customer satisfaction. In the regression model is a good should not happen correlation between independent variables Ghozali, 2011:105. 47 A good regression model should not happen correlation between independent variables. If the independent variables are correlated, then these variables are not orthogonal. Orthogonal variable is the independent variable correlation values between the members of the independent variables equal to zero. To detect the presence or absence multicollinearity in the regression model are as follows: a. The value of R 2 generated by an empirical regression model estimate is very high, but individually many independent variables were not significantly affecting the dependent variable. b. Analyze the correlation matrix of the independent variables. If there is correlation between the independent variables are quite high generally above 0,90, then this is an indication of multicollinearity . The absence of a high correlation between the independent variable does not mean free of multicollinearity . Multicollinearity may be due to the combined effect of two or more independent variables. c. Multicollinearity can also be seen from: 1 The value of tolerance and the opponent; 2 Variance Inflation Factor VIF. Both these measurements indicate each independent variable which explained by other independent variable. In simple terms each independent variable the dependent 48 variable and regressed against other independent variables. Tolerance measures the variability of independent variables was chosen that are not explained by other independent variable. So a low tolerance value equal to the value of a high VIF for VIF = 1Tolerance. Value cutoff commonly used to indicate the presence multicollinearity is the tolerance value 10 or equal to VIF 10 Ghazali, 2011:106.

3. Heteroskedasticity

Heteroskedasticity test was conducted to test whether a regression model occurred inequality residual variance from one observation to another observation remains, and then called heteroskedasticity . If the points spread above and below the number 0 on the Y axis without forming a particular pattern, then there is no heteroskedasticity Ghozali, 2011: 139. A good regression model is that homoskesdaticity or did not happen heteroskidastity. Most of the data resection containing situation because this data collect data representing a variety of sizes small, medium and large. There are several ways to detect the presence or absence of h eteroskedasticity . In this study to see Graph Plot between the predicted values of the dependent variable dependent is ZPRED with residual SRESID. Heteroskedasticity detection of the presence 49 or absence can be done by looking at whether there is a specific pattern on a scatterplot graph between SRESID and ZPRED wherein Y is a Y axis that has been predicted, and the X axis is the residual prediction Y - Y in fact who have in-studentized. With the analysis if there is a specific pattern of regular wavy, widened and then narrowed, then identifying been going heteroskedasticity and if there is no clear pattern, as well as the points spread above and below the number 0 on the Y axis, then there is no heteroskedasticity Ghozali, 2011:139.

F. Hypothesis Test

1. t- Test Partial Test

To determine whether the independent variables partially individual have a significant influence on the dependent variable. The statistical test T basically shows how far the influence of the independent variables individually in explaining the variation of the dependent variable Ghozali, 2011:98. The t-test was used to test the partial each variable. T test results can be seen in the table on the column sig coefficient significance. If the t value or significance probability 0,05, it can be said that there are significant independent variable on the dependent variable partially. 50 However, if the probability value or significance t 0,05, it can be said that there is no significant effect of each variable on the dependent variable Besas. t test formula: to = Where: to = t value bi = coefision regression Sbi = standart error Hypothesis based Significance namely: a. If the number sig. 0,05, then Ho is accepted b. If the number sig. 0,05, then Ho is rejected

2. F

– Test Simultaneous Test This test aims to prove whether the independent variables X simultaneously together have an influence on the dependent variable Y Ghozali, 2011:88. If F count F table, then Ho is rejected and Ha accepted, which means that the independent variable has a significant effect on the dependent variable using a significant level of 0,05 if the value of F count F table then together all independent variables affect the dependent variable. Additionally, you can also see the value of probability. If the probability value less than 0,05 for a significance level of = 0,05, the independent variables jointly 51 affect the dependent variable. Meanwhile, if the probability value is greater than 0,05, the independent variables simultaneously has no effect on the dependent variable. Formula F test F = ⁄ ⁄ Where: R 2 = multiple correlation coefficient squared n = number of sample Then it will be known whether this hypothesis simultaneously rejected or accepted, while the form of simultaneous hypothesis is: H : β1 = β2 = β3 = 0 ; service quality, sales promotion, customer satisfaction simultaneously does not affect the customer loyalty. H : β1 ≠ β2 ≠ β3 ≠ 0 ; service quality, sales promotion, customer satisfaction simultaneously influence the customer loyalty. G. Multiple Linear Regression 1. Similarity Multiple Linear Regression Analysis method in this research is a multiple linear regression that is used to test service quality, sales promotion and customer satisfaction toward customer loyalty. The equation of multiple linear regressions is as follows: Y = a + β 1 X 1 + β 2 X 2 + β 3 X 3 + e 52 Where: Y = Customer Loyalty a = Constanta e = Error sampling X 1 = Service Quality X 2 = Sales Promotion X 3 = Customer Satisfaction β 1 , β 2 , β 3 = Regression coefficient

2. Coefficient of Determination Adjusted R

² The coefficient of determination Adjusted R² aims to determine how much ability of independent variables and the dependent variable explained to know how big the ability of the independent variable dependent variable explained viewed through Adjusted R² for thorough independent variables in this study of more than two. In SPSS output, coefficient of determination lies on the table and writing Summary Model Adjusted R². R² value of 1, meaning the influence entirely dependent variable can be explained by the independent variables and no other factors that lead to influence the dependent variable. The coefficient of determination is between zero and one. R² small value means the ability of the independent variables in explaining the dependent variable is very limited. Values close to the mean of independent variables provide almost 53 all the information needed to predict the variation of the dependent variable Ghozali, 2011:97.

H. Variable Operational Research

Based on the core issues and hypotheses, research on effects of service quality, sales promotion and customer satisfaction toward customer loyalty on Carrefour Lebak Bulus. The variables and indicators of this study can be seen in the table below: 54 Table 3.2 Operational Variable VARIABLE DIMENSION INDICATOR SCALE Service Quality X 1 Lovelock and Wirtz, 2011: 406 Tangibles 1. Appearance of physical facilities 2. Equipment 3. Personnel 4. Communication materials. Likert Likert Likert Likert Reliability 5. Ability to perform the promised service. Likert Responsiveness 6. Willingness to help customers 7. Provide prompt service. Likert Likert Assurance Credibility: 8. Trustworthiness, believability 9. Honesty of service provider. Security: 10. Freedom from dangerous, risk or doubt. 11. Competence: Possession of the skills and knowledge require performing the service. 12. Courtesy: Politeness, respect, consideration and friendliness of contact personnel. Likert Likert Likert Likert Likert Empathy 13. Access: Approachability and ease of contact. 14. Communications: Listening to the customer and keeping them informed in a language the can understand. 15. Understanding the customer: Making the effort to know customers and their needs. Likert Likert Likert 55 VARIABLE DIMENSION INDICATOR SCALE Sales Promotion X 2  Kotler and Armstrong 2014: 503- 504  Solomon et. al 2010 16. Samples to introduce a new product or create new excitement for an existing one Likert 17. Coupons are certificates that give buyers a saving when they purchase specified products Likert 18. Price packs offer consumers savings off the regular price of a product Likert 19. Premiums are goods offered either free or at low cost as an incentive to buy a product Likert 20. Point-of-purchase POP promotions include displays and demonstrations that take place at the point of sale Likert 21. Event marketing they can create their own brand- marketing events or serve as sole or participating sponsors of events created by others Likert 22. Loyalty programs reward consumers for their frequent, continuing purchase of a product Likert 56 VARIABLE DIMENSION INDICATOR SCALE Customer Satisfaction X 3 Thakur and Singh 2012 Experiences 23. The product good quality 24. Give the good result 25. Price same like quality Likert Likert Likert Expectation 26. Product selling like my expectations 27. Result after use the product same like expectations Likert Likert Service Performances 28. Good service performance Likert Customer Loyalty Y Griffin 2009:31 29. Makes regular repeat purchase Likert 30. Purchases across product and service Likert 31. Refers to others customers willingly recommend the company to friends and colleagues Likert 32. Demonstrates an immunity to the full of the competition Likert 57

CHAPTER IV RESULT AND ANALYSIS

A. General Overview Research Object

1. History of Carrefour

The history of Carrefour Indonesia began in October 1998 by opening the first unit in Cempaka Putih. At the end of 1999, Carrefour and Pramodes Parent Company Continent agreed to merge all his efforts throughout the world. This merger to form a group of the world largest retail business under the name Carrefour. With the formation of this new Carrefour, then all the resources owned by the two groups had to be focused to better meet and satisfy customer needs. This incorporation enables to improve market performance benefited from the expertise of employees in Indonesia and in the world and anticipate the evolution of retail in the national and global scale. The focus on consumers this translates into 3 main pillars, which are believed to be able to make the Carrefour shopping options for consumers Indonesia. These three main pillars are as follows: a. Competitive prices b. Complete choice c. Satisfactory service