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International Conference on Business & Banking and Corporate Social Responsibility, University Network (ICBB and CSR-­‐UN) 23 – 24 February 2010 Surabaya Joy Elly Tulung 1-­‐1

2.
• Upper Echelons Theory -­‐ Hambrick & Mason, 1984 • Various studies show that organizations are a reTlection of its top managers (Finkelstein and Hambrick) 1996 • Carpenter et al (2004) also repeats the composition of the TMT, in terms of diversity of the upper echelons theory due to the duties of internal and external management. 1-­‐2

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† Tihanyi et al (2000), 126 companies in the electronics industry † Kilduff et al (2000), data from 35 Tirms and 159 managers † Herrmann and Datta (2005), based on a sample of 112 manufacturing Tirms in the United States † Staples (2005) whereas conducted a study of the largest TNC 80, and found that 60/80 or 75% of these companies had at least one foreigner in their councils. 1-­‐3

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† Glunk et al (2001) who study and explore the TMT differences and similarities in England, Dutch and Denmark † Heijltjes et al (2003) in their study look at the national scale TMT diversity in two countries in Europe, i.e. Netherlands and Sweden, † Hendriks (2004) conducted research on TMT diversity and Tirm performance in IT companies of small and medium sizes in the Netherlands and Belgium. † van Veen and Marsman (2008) n their study explain the diversity of nationalities in 15 countries in Europe 1-­‐4

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† Ping (2007) who conducted an empirical study of data from 2001-­‐2002 of 356 Chinese companies † Julian et al (2003), International Joint Venture team in Thailand † Kusumastuti et al (2007), 48 manufacturing companies registered in Jakarta Stock Exchange in 2005 1-­‐5

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Does the composition of the Top Management Team in the industry affect the banks performance in Indonesia? 1-­‐6

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Age • Pegels and Yang (2000:697) state that older managers tend to avoid risk (Vroom and Pahl, 1971) while the young one tend to pursue more risky and innovative growth strategies. Gender • Glunk et al (2001) found that gender distribution is very different in three countries: there are few women executives in the 30 countries, with the exception of the UK, Denmark and Dutch 1-­‐7

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Educational Level and Educational Backgroud • Dahlin et al (2005) found that the education diversity in TMT affects the range and depth of the use of positive information, and may negatively affect the combination of information. • Herrmann and Datta, 2005; Hambrick and Mason, 1984, explained also in the previous subsection should be a complementary. 1-­‐8

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AGE GENDER ROA EDUCATIONAL LEVEL EDUCATIONAL BACKGROUND 1-­‐9

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† Deductive † 9 banks listed in Indeks kompas 100 † Annual Report 2008 † 134 top executives 1-­‐10

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Age of TMT s 50 – 7.5% The rest 73,8% 54 – 51 – 6.0% 6,7% 52 – 6.0% 1-­‐11

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Age Gender 0.51 0.020 ROA Educational Educational Level Background -­‐0.265 0.022 1-­‐15

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Normal P-P Plot of Regression Standardized Residual† Normality Dependent Variable: ROA Expected Cum Prob 1.0 0.8 0.6 0.4 0.2 0.0 0.0 0.2 0.4 0.6 0.8 1.0 Observed Cum Prob 1-­‐16

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Scatterplot Regression Standardized Residual† Heterogeneity Dependent Variable: ROA 2 1 0 -1 -2 -2 -1 0 1 2 3 Regression Standardized Predicted Value 1-­‐17

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† CoefIicient Coefficientsa Unstandardized Standardized Coefficie nts Coefficie nts Correla tions Collinearity StatisticsModel B Std. Error Beta t Sig. Zero-order Partial Part Tolerance VIF1 (Constant) .014 .007 2.088 .039 Age 9.27E-005 .000 .089 .906 .367 .082 .092 .088 .970 1.031 Gender .002 .003 .077 .778 .439 .022 .079 .076 .956 1.046 Educatio nal level -.003 .001 -.277 -2.810 .006 -.266 -.274 -.273 .976 1.025 Content of education -9.48E-007 .000 -.001 -.009 .993 .001 -.001 -.001 .987 1.013 a. Dependent Variable: ROA † Equation Y = 0.014 + 0.0000927 X1 + 0.002 X2 -­‐ 0.003 X3 -­‐ 0.000000948 X4 1-­‐18

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† Every one point increase in X1, Y will increase as much as 0.0000927 in which other variables are considered constant † Every one point increase in X2, Y will increase as much as 0.002 where the other variables are considered constant † Every one point increase in X3, Y will be reduced by 0.003 when other variables are considered constant † Every one point increase in X4, Y will be reduced by 0.000000948 when other variables are considered constant 1-­‐19

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† ANOVA (b) Sum ofModel Squares df Mean Square F Sig.1 Regression .000 4 .000 2.183 .077(a) Residual .004 97 .000 Total .005 101† a Predictors: (Constant), Educational Background, Educational level, Age , Gender † b Dependent Variable: ROA 1-­‐20

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† Partial Test † Age (X1) † From the coefTicient table the signiTicant value = 0.367>α show that the null hypothesis was not rejected. The conclusion of this is that the independent variable X1 (Age) does not affect the dependent variables (ROA) † Gender (X2) † From the coefTicient table, the signiTicant value = 0.439>α, show that the null hypothesis was not rejected. The conclusion from this is that the independent variable X2 (Gender) did not affect the dependent variables (ROA) 1-­‐21

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† Educational Level(X3) † From the coefTicient table, the signiTicant value = 0.006<α, which shows the null hypothesis was rejected, with the conclusion being that the independent variable X3 (Education Level) affect the dependent variable (ROA) † Educational Background (X4) † From the coefTicient table, the signiTicant value = 0.993>α, the null hypothesis was not rejected. The conclusion is that the independent variable X4 (Education Sector) does not affect the dependent variable (ROA). 1-­‐22

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† The conclusion was that the independent variables simultaneously does not affect the dependent variables, so top management team composition does not affect the company performance. † Also only the educational level of the top management team had an inTluence on the performance of banking companies in Indonesia, with age, gender and educational background having no effect on the company performance in Indonesia. 1-­‐23

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† Limited sample † Just 9 banks 1-­‐24