Data Collection Method Definition of Operational Research

52 a. H : no autocorrelation ρ = 0 b. H 1 : no autocorrelation ρ ≠ 0 Decision making whether there is autocorrelation: Null Hypothesis Decision If No positive autocorrelation Reject 0dd L No positive autocorrelation No Decesion d L ≤d≤d U No negative autocorrelation Reject 4-d L d4 No negative autocorrelation No Decesion 4-d U ≤d≤4-d L No positive and negative autocorrelation Not Rejected d U d4-d U Note: d U : Watson upper durbin d L : Durbin Watson lower d. Heterocedasticity Regression models should qualify BLUE order accuracy in depicting the actual circumstances, namely 1 the best 2 linear 3 unbiased 4 estimator. To determine eligibility BLUE regression model can be used heteroscedasticity test. Meanwhile, according to Gujarati 2003:321, the regression is still being done on the data containing heteroscedasticity will produce misleading conclusions. According Ghozali 2013:93 there are several statistical tests that can be used to detect heteroscedasticity, 1 Glejser, 2 White, 3 Breusch-Pangan-Godfrey, 4 Harvey 5 Park. In White test, the probability chi-square from ObsR-square 0.05 so heteroscedasticity is rejected. 53

2. Panel Data Regression

According to Ghozali 2013:231 In the analysis of panel data model, there are three kinds of methods that consists of pooled least squares, fixed effect and random effect. Explanation of each of these methods are: 1. Pooled Ordinary Least Squares PLS is the simplest method in data processing and applied to the data panel- shaped pool. If there is the following equation: Y i = α + β X it + € it Where : i = 1,2,3,4,….,N t = 1,2,3,….,N Where N is the number of cross-section data individual and T is the number of time periods. By assuming the error components, we can perform the estimation process separately for each individual unit cross-section. For period t = 1 will be obtained cross-sectional regression equation as follows: Y i = α + β X i1 + € i1 2. Fixed Effect Model FEM PLS approach has the greatest difficulty that is the assumption of the intercept and slope are assumed to be constant between individuals may be unwarranted. FEM into account differences across units parameter values both cross-section and over 54 time by including dummy variables. In general, the approach Fixed Effect Least Square Dummy Variable can be written as follows : In the equation, there is the addition of as many variables N-1 and T-1 as a dummy variable in the model as well as eliminating the two other variables to avoid perfect collinearity among explanatory variables. This led to a degree of freedom of NT - 2 - N-1 - T-1 or a NT - N - T which affect the efficiency of the parameters to be estimated. 3. Random Effect Model REM Random Effect models assume that the sample is randomly in each period, so it is assumed u i and v t in case consideration of heterogeneity intertemporal follow a normal distribution. This model provides benefits in terms of savings compared to the number of variables Fix Effect, so as to improve the efficiency of the model. In Random Effect models, parameters that vary from time put into the component error error component model. The form of a random effects model with two independent variables is Y it = β 1 + β 2 X 2it + w it w it = u i + v t + ɛ it 55 Where, u i is the error component of the cross-section, v t is the error component time series, and εit is the error component of the combination. By using the model of random effect, the use of degrees of freedom can be saved and not reduce the amount as in the fix effect models. The result parameter estimation result will be more efficient. 1 Selection Test Model In Panel Data In choosing a panel data model that will be used, first Chow test to determine whether the use of panel data processing Pooled Least Square method or Fixed Effect. If significant then proceed with the Hausman test to choose between Fixed Effects and Random Effect. If significant Hausman test results it was concluded the processing performed by the FEM method. Fix Effect Random Effect Pooled Least Square Hausman Test LM Test Chow Test 56 1. Chow test Called the F test statistic for selecting the panel data model PLS or FEM. The hypothesis is formed is: H : PLS model H 1 : FE model Basic rejection of the null hypothesis is by using the F statistic. Chow formulated: Chow = – where: RRSS: Restricted Residual Sum Square value Residual Sum Square with PLS method USSR: Unrestricted Residual Sum Square Sum Square Residual value method FE N: Number of cross-section data Q: The number of time series data K: The number of explanatory variables This test follows the distribution of the F statistic is F N-1, NT-NK. If the value of the F statistic Chow greater than F table, it is enough evidence to reject the null hypothesis and FE methods are used. 57 2. Hausman test Hausman test is a statistical test that became the basis of the considerations in choosing a model FE or RE. The test is performed with the following hypothesis: a. Ho: RE model b. H1: FE model Basic rejection of the null hypothesis is by using chi-square statistical considerations. Hausman test can be done in programming eviews as follows: if the result of the significant Hausman test Hausman probability α, the null hypothesis is rejected and the FE method is used.

3. t-Test

T-test shows how much effect of every single dependent variable toward the dependent variable. The hypothesis for t-test is : H = the independent variable are not influence the dependent variable H 1 = the independent variable are influence the dependent variable H is rejected if the t statistic t table or if the probability of t statistic α. The significance level that use is 5 0.05.

4. F-test

F-test used to measure, do the independent variables simultaneously influence the dependent variable. The hypothesis for this test is : H = the independent variable are simultaneously not influence the dependent variable 58 H 1 = the independent variable are simultaneously influence the dependent variable H0 is rejected if F statistic F table or if the probability of F statistic α.

5. Adjusted R

2 Adjusted R 2 is the determination coefficient that explained how much the dependent variable variant described by the model in the whole. The value of adjusted R 2 is in between 0 and 1. More closer the value of R 2 to the 1, it means that the independent variables perfectly influence the dependent variableor with the other word, the model can describe the variant of dependent variable well.

E. Definition of Operational Research

Variable operational research is a concept that had variation point applied in a research and meant to ensure, so variable that wanted to be researched clearly could be seen. As for variable that is meant as follows: 1. Independent The independent variable is the type of variables that explain or influence another variable or variables suspected as the caue of the dependent variable Indriantoro and Supomo, 2009: 64. The independent variables used are: 59 a. Board Size Alternatively, board size increases according to company performance as troubled firms are more likely to add directors to increase their monitoring capacity. However, Linck et al 2008 provides evidence that smaller boards are not necessarily better than larger boards. We measure board size by the total number of executive and non-executive directors of the board Weterings, Josephus P et al, 2011 Board Size = Total number of board director b. Size of Audit Committee An audit committee can improve quality of a firm’s financial reports, as firms with established audit committees are more likely to have reliable financial reporting McMullen 1996. The total member of director must be adjusted with the complexity of firm, but still considering the effectiveness of decision making. According to Gill and Obradovich 2012 the size of audit committee will be measured by total number of audit committee members. c. Family Ownership Family firms usually represent the characteristic of being founded by a family entrepreneur owning most shares in the company. When at the start-up phase they have few numbers of employees, where informal behavior is adopted along with a centralized decision making power, and fewer hierarchical levels. Founders of family firms will feel that they have more direct control over the behavior of employees, as 60 well as the ability to directly export cultural and ethical guidelines to the company through their own behavior Charbel 2013. It is measured by looking at the composition or percentage of ownership owned by family. Family Ownership = of shares owned by family d. Family’s DirectorManager Usually family businesses have high involvement and long tenure in management. Thus, by their high involvement they will succeed at having a better sense of recognition of uncertainties and opportunities and also by establishing a long term focus Zahra, 2005. Several empirical studies have backed the vision that the involvement of the family in business will foster its financial performance. In the study of more than 1600 Western European companies, Maury revealed that constant and active control by family executives was linked to higher profits, justified by the mitigation of agency problems between principals and agents Maury, 2006. Family’s directormanager measure by total number of family’s managerdirector. 2. Dependent Dependent variable is type of variables that explained or influenced by other variables or variable expected as a result of the independent variable Indriantoro and Supomo, 2009:159 . Dependent variable used in this research is firm value. Tobin’s Q is used to measure firm value. According to Ma Tian 2009, Tobin’s Q has the 61 advantage of reflecting the firm’s current value and future profitability potential. According to Vinola Herawati 2008, firm value can be measured by Tobin Q which is formulated as: Tobin’s Q = Where: Tobin’s Q: Firm Value MVE: Market Value Equity D: Liabilities Book Value BVE: Book Value Equity Market Value Equity MVE is obtained from the multiplication of ending share price with the number of outstanding shares. Book Value Equity BVE is obtained from the difference between total assets to total liabilities Table 3.1 Summary of variable operational research No Variable Measure Scale 1 Board Size Total number of board director Ratio 2 Size of Audit Committee The total number of audit committee members Rasio 3 Family Ownership of shares owned by family Rasio 4 Family’s DirectorManager The number of Family’s ManagerDirector Rasio 5 Firm Value Tobin’s Q = Rasio 62

CHAPTER IV ANALYSIS AND DISCUSSION

A. Overview of Research Object 1. Description of Research Object

This chapter presents and discusses the findings of the research. The population of this research is family firm listed in IDX within period 2010-2013. The selection of sample is chosen by criteria of population that have explained in research methodology in previous chapter that is taken as annually in 2010-2013. In 2010-2013, family firm that go public in Indonesia Stock Exchange are 32 companies. This research is used purposive sampling. And from 32 companies, regarding on criteria of sampling in previous chapter, so the amount of sampling are 20 companies. This research will use pooling data method in which the variable will be tasted in four year, so the total sample are 80 samples of annual report. 63 Table 4.1 Sample Selection No. Criteria Number 1 Population 32 2 Family firm that had not complited published it’s annual report wuthin period 2010 – 2013 4 3 Family firm that had not use IDR currency within period 2010 – 2013 5 4 The company that did not have the complete and detail data that will be tested in its annual report. 3 Total sample of companies 20 Total sample of annual report used in this Research 80 Table 4.2 List of Companies’ Sample No Company Code 1 PT. Bakrie Brother Tbk BNBR 2 PT. Bakrie Sumatera Plantation Tbk UNSP 3 Agung Podomoro Land Tbk APLN 4 PT Gudang Garam Tbk GGRM 5 PT. Bakrie Telekomunication Tbk BTEL 6 PT Indofood Sukses Makmur Tbk INDF 7 PT. Kalbe Farma Tbk KLBF 8 PT. Lippo Insurance Tbk LPGI 9 PT. Ramayana Tbk RALS 64 No Company Code 10 PT. Mitra Adi Perkasa Tbk MAPI 11 PT. Lippo Securities Tbk LPPS 12 PT. Summarecon Tbk SMRA 13 PT. Ciputra Properti Tbk CTRP 14 PT. Ciputra Development Tbk CRTA 15 PT. CIMB Niaga Tbk BNGA 16 PT. Lippo Cikarang Tbk LPCK 17 PT. Lippo Karawaci Tbk LPKR 18 PT. Sinarmas Multiarta Tbk SMMA 19 PT. Sinarmas Agro Resource Tbk SMAR 20 PT. Smartfren Tbk FREN

B. Data Descriptive

The data processing in this research is conducted by the statistical application. The application used for data processing in this research is Eviews 8.0 version.

1. Descriptive Statistic

The descriptive statistical testing obtains a picture or describes data that can be seen from mean, median, maximum, minimum and standar deviation. Variable used in this test are variable of good corporate governance board size, size of audit committee, family involvement family ownership and family directormanager and firm value. Based on the result, it is obtained the descriptive statistic as much as 80 65 observation data coming from multiple of study period 4years, from 2010-2013, by th e number of company’s sample 20 companies. Table 4.3 Describes Data from Statistic Analysis BOD CA FO FDM Mean 6.275000 3.362500 0.399301 1.812500 Median 6.000000 3.000000 0.378800 1.000000 Maximum 12.00000 6.000000 0.972000 5.000000 Minimum 2.000000 2.000000 0.000200 1.000000 Std. Dev. 0.392359 0.225147 1.595129 0.588858 Source : Output Eviews 8.0 Based on table 4.3, the descriptive statistic can be described as follows : a. Board of Director The result of the analysis using descriptive statistic on board size indicates the minimum value is 2.00 for PT Lippo Securities Tbk 2011, while the maximum velue is 12.00 for PT CIMB Niaga Tbk 2010. The average value of tobins-q variable is 6.27 with the standar deviation of 0.39 which is lower than average value, indicating that data have low deviation which means the data is distributed normally. b. Size of Committee Audit The result of the analysis using descriptive statistic on size of committee audit indicates the minimum value is 2.00 for PT Lippo Securities Tbk 2010, while the 66 maximum velue is 6.00 for Gudang Garam Tbk 2010. The average value of tobins-q variable is 3.36 with the standar deviation of 0.97 which is lower than average value, indicating that data have low deviation which means the data is distributed normally. c. Family Ownership The result of the analysis using descriptive statistic on family ownership indicates the minimum value is 0.0002 for PT Bakrie Sumatera Plantation 2012, while the maximum velue is 0.97 for PT. Summarecon 2011 . The average value of tobins-q variable is 0.39 with the standar deviation of 0.28 which is lower than average value, indicating that data have low deviation which means the data is distributed normally. d. Family DirectorManager The result of the analysis using descriptive statistic on family ownership indicates the minimum value is 1.00 for PT. Bakrie Brother Tbk, PT. Bakrie Sumatera Plantation Tbk 2010, while the maximum velue is 5.00 for PT Ciputra Property Tbk 2010. The average value of tobins-q variable is 1.81 with the standar deviation of 1.22 which is lower than average value, indicating that data have low deviation which means the data is distributed normally.