CHAPTER 3
RESEARCH METHODOLOGY
3.1 Research Type
In this research, quantitative analysis was selected as the research method.
The quantitative study is a type of testing that collects data in numerical form and analyzes it using quantitative methods along with conjoint, psychometric, correlations, analysis of variance, structural equation modeling, and so on to describe a phenomenon or a problem (Valtakoski, 2020).
3.2 Research Population and Sampling 3.2.1 Population
A population is a classification area made up of an entity or subject with a certain quantity or feature (Orton et al., 2019). Based on the definition of population, the customers of Citinine Property Indonesia from 2019 to 2021 compose the population for this study, and the size of the population is 154 customers. The reason why the writer choose Citinine Property Indonesia customers from 2019 to 2021 is because the data of customer is still accessible and complete.
3.2.2 Sample
Based on Sugiyono (2017: 81), the sample is part of the sum and characteristic that is owned by the population. Since if the population is a lot, it is impossible to study the whole population as the time and limitation are not
probability sampling unit selection is chosen because it has a more representative value for solving testing problems. The sampling technique used here is purposive sampling, the sample taken in this research will be based on certain criteria and this is the reason why the researcher choose purposive sampling where the pre- determined criteria have to be fulfilled by respondents to enable their answers to be used in this research. The respondent criteria are that they need to have previous transactions with Citinine Property Indonesia. Based on the data, the number of Citinine Property Indonesia from 2019 to 2021 in 154 customers, and to know the number of samples, Slovin formula is used:
𝑛 = 𝑁
1 + 𝑁(𝑒)⬚2 n = sample size
N = population
(e)2 = error tolerance, percentage of leniency precision due to sampling error (1%, 5%, or 10%) (Albert Kurniawan 2014: 72)
𝑛 = 154
1 + 154(0.05)⬚2
𝑛 = 111,19(𝑟𝑜𝑢𝑛𝑑𝑒𝑑𝑡𝑜112𝑟𝑒𝑠𝑝𝑜𝑛𝑑𝑒𝑛𝑡𝑠)
Based on the calculation, in this research number of the samples that will be used is 112 respondents.
3.3 Data Types, Data Source, and Measurement Scales 3.3.1 Data Types and Sources
1. Primary Data
Primary data defined as the source of data that is obtained through the source without any medium or agents (Indriantoro, 2009: 146). The researchers collect primary data to answer research questions or statements.it can be obtained through the customers of Citinine Property Indonesia through the questionnaire.
2. Secondary Data
Secondary data is data that can be obtained by the researcher indirectly and through media or agents (Indriantoro, 2009: 147). Secondary data in this research is literature, books, and government publications are used to support the primary data.
3.3.2 Measurement Scales
To measure the questions and statements in this research, the Likert Scale is a way of measuring statements in an agree-disagree axis. It is used as a method to quantify statements in surveys where the scale will be scaled from 1 to 5 (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree) (Allen, 2017).
3.4 Operational Definition and Variable Measurement Table 3.1 Operational Definition
VARIABLE CONCEPTUA
L DEFINITION
INDICATORS OPERATIONAL DEFINITION
SOURCES
CORPORATE SOCIAL
RESPONSIBILITY
Corporate Social Responsibility captures a viewpoint in
which a
company sees added value in
meeting a
broader range of community
needs and
expectations, as well as seeing net gains from socially responsible behavior (Jamali and Sidani, 2008)
Sustainable Society
I believe Citinine Property Indonesia serves the needs of the current without endangering future generations' capacity to fulfill their own needs.
(Brown and Dacin, 1997; Matten and Moon, 2020)
Trustworthines s
I recognize Citinine Property Indonesia as trustworthy from its ability, competence, and also benevolence (goodwill).
Charities I recognize Citinine Property Indonesia gives enough contributions to charities.
Business Ethics I recognize Citinine Property Indonesia believe in the ethical business
CORPORATE REPUTATION
Organizational experts have researched corporate reputation extensively and believe it to be one of the company's most valuable intangible properties (Jang et al., 2016).
Emotional Appeal
I believe Citinine Property Indonesia has an outstanding reputation.
(Fombrun et al., 2000)
Industry Leadership
I believe Citinine Property Indonesia is well known.
Familiarity I can recognize Citinine Property Indonesia
Value creation/Qualit y.
I am familiar with the products that Citinine Property Indonesia provides to consumers.
CUSTOMER TRUST
Trust is
explained as
“the
presumption of ethically justifiable behavior”
Operational competence
I trust in the quality of Citinine Property Indonesia.
(Sirdeshmukh et al., 2002)
Operational benevolence
I trust on quality assurance of Citinine Property Indonesia.
confidence is considered a central construct for establishing a long-lasting relationship (Salindal, 2018;
Pratminingsih et al., 2013).
Customer Satisfaction
I trust that Citinine Property Indonesia is interested in its customers.
CUSTOMER LOYALTY
Customer loyalty is a long- term investment that provides a clear indication of relationship management between the customer and operators (Raza et al., 2020).
Desirability I wish to continue my connection with Citinine Property Indonesia
(Sirdeshmukh et al., 2002; Zeithaml, 1988)
Loyalty Competence
I encourage friends and relatives to purchase from Citinine Property Indonesia.
Value I wish to contain a long-term relationship with Citinine Property Indonesia.
Satisfaction Altruism
I say positive things about Citinine Property Indonesia to other people.
Source: Research Data (2021)
3.5 Data Collection Procedure
The data collection technique that is used in this research is simple random sampling. Based on Sugiyono (2017), simple random sampling techniques are a simple technique since the sample used from the population is taken randomly without paying attention to the similarities or strata that exist in the population.
This study's data collection approach is to distribute a questionnaire to the sample through a google form. The questions going to be a closed questionnaire where the answer is provided and the respondent needs to pick the one they believe represents them. The questionnaire used five Likert Scale scales and included English and Indonesian translations to assist the sample in comprehending the
have promoted Citinine to relatives and friends to be considered as a valid respondent to represent the population.
3.6 Data Analysis Method
3.6.1 Validity and Reliability Test 1. Validity
The degree to which a definition is correctly assessed in the scientific analysis is referred to as validity. A coefficient of correlation test is used to assess whether or not an object can be used, with a significance level of 0.01 and a confidence level of 99 % (Ghozali 2016).
2. Reliability
Reliability is another metric used in quantitative analysis. Reliability checking is used to ensure that scientific findings are consistent. Cronbach's test is used to assess an instrument's internal accuracy. The Cronbach result is a number between 0 and 1 that is called reliable if it is 0.7 or higher (Ghozali 2016).
3.6.2 Classical Assumption Test 1. Normality Test
A normality test, in particular, is necessary to determine whether data distributed follows a normal distribution or not. The Shapiro-Wilk test is the most often used method to determining normality (Albano et al., 2019; Duwi
Multicollinearity is the connection between two variables in a multiple regression equation where the variables have a strong correlation. It can be defined in two ways in the analysis model: tolerance values and variance inflation factor (VIF) values, where VIF values greater than 10 and tolerance values less than 0.1 indicate a multicollinearity problem (Gray et al., 2017;
Imam Ghozali, 2011: 105).
3. Heteroscedasticity Test
The heteroscedasticity test is used to determine if the regression model exhibits inequality variation from one observation to another (Proceeding: 2nd Sriwijaya Economic, Accounting, and Business Conference, 2016).
4. Linearity Test
The linearity test is a test used to figure out if the study’s independent variables and dependent variables have a linear relationship where it can be concluded to have a linear correlation by the value of significance that should be below 0.05 (Duwi Priyanto, 2010).
5. Autocorrelation Test
The autocorrelation test is used to see if in a linear regression model there is a correlation between the error or residual within period t with error within period t-1(Duwi Priyanto, 2010: 71). There is a lot of ways to do an autocorrelation test and in this research, the writer going to use Durbin- Watson test. When decision making is in progress, the autocorrelation test uses the Durbin-Watson test table as follow:
Ho Decision If No positive autocorrelation Reject 0<d<d1 No positive autocorrelation No Decision d1≤d≤ du No negative autocorrelation Reject 4-d1<d<4 No negative autocorrelation No Decision 4-du≤d≤4- d1 No positive and negative
autocorrelation
Not Rejected du<d<4-du
Source: Modul Komputer Statistik (2016)
3.6.3 Multiple Regression Analysis
A mathematical method for evaluating the association between dependent and independent variables is known as multiple regression analysis. Multiple linear regression analysis is used whenever there are more than two independent variables (Hurriyati et al., 2020). The following is the regression equation model:𝑌 = 𝛼 + 𝛽⬚1𝜒⬚1+ 𝛽⬚2𝜒⬚2+ 𝛽⬚3𝜒⬚3+ 𝑒
Y : Customer Loyalty
𝛼 : Constant
𝛽⬚1, 𝛽⬚2, 𝛽⬚3 : Regression Coefficient 𝜒⬚1 : Corporate Social Responsibility 𝜒⬚2 : Corporate Reputation
𝜒⬚3 : Customer Trust
The ratio of variances is calculated using a simulant significant test. The basic concept in regression analysis is that if it is greater than 25, researchers should reject the null hypothesis. The F-test is used to see how all of the influence of the independent variables with the dependent variable or to see whether the regression model researchers construct is significant or not (Kissell and Poserina, 2017). This research will conduct a Simulant Significance Test or F Statistics test.
2. Partial Significance Test (T Statistics test)
The Partial Significance Test, also known as the T-test, is used to measure two independent entities. It will encourage researchers in assessing whether or not there is a statistically meaningful difference in mean scores between the two groups. The T-test is a partial test that tests the effect of each independent variable on the dependent variable separately (Gerald, 2018). This research will conduct a partial significance test or T Statistics test to assess the variables.
3. Coefficient of Determination (𝑅⬚2)
The coefficient of Determination measure how far a model able to apply the variation to the dependent variable. When the R2 value close to number 1 means that the independent variable provides almost all the information needed to predict the variation in the dependent variable. However, if the R2 value is getting closer to 0 then the smaller the ability of the independent variable to explain the dependent variable (Polgar and Thomas, 2019). This
research will conduct coefficient tests of determination to assess the relationship of the variables.
