Your SlideShare is downloading. ×
0
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 100 KOMPAS INDEKS

550

Published on

Upper Echelons Theory - Hambrick & Mason, 1984 …

Upper Echelons Theory - Hambrick & Mason, 1984
Various studies show that organizations are a reflection 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.

Published in: Business, Economy & Finance
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
550
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
2
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. 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  
  • 3. †  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  
  • 4. †  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  
  • 5. †  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  
  • 6. Does  the  composition  of  the  Top  Management   Team  in  the  industry  affect  the  banks   performance  in  Indonesia?     1-­‐6  
  • 7. 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  
  • 8. 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  
  • 9. AGE   GENDER   ROA  EDUCATIONAL  LEVEL   EDUCATIONAL   BACKGROUND   1-­‐9  
  • 10. †  Deductive  †  9  banks  listed  in  Indeks  kompas  100  †  Annual  Report  2008  †  134  top  executives     1-­‐10  
  • 11. Age  of  TMT s   50  –  7.5%   The  rest   73,8%   54  –   51  –   6.0%   6,7%   52  –   6.0%   1-­‐11  
  • 12. 1-­‐12  
  • 13. 1-­‐13  
  • 14. 1-­‐14  
  • 15. Age     Gender      0.51   0.020   ROA   Educational  Educational  Level     Background      -­‐0.265    0.022   1-­‐15  
  • 16. 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  
  • 17. 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  
  • 18. †  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  
  • 19. †  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  
  • 20. †  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  
  • 21. †  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  
  • 22. †  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  
  • 23. †  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  
  • 24. †  Limited  sample    †  Just  9  banks   1-­‐24  

×