Pengaruh Partisipasi Anggaran Terhadap Kinerja Manajerial dengan Keadilan Prosedural, Motivasi, dan Job Relevant Information (JRI) Sebagai Variabel Intervening (Studi Empiris Pada Perusahaan Jasa di Kota Semarang) - Unika Repository

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  Produktivitas, Jakarta : Bumi Aksara

  pengolahan kayu skala menengah di Jawa Timur”. Jurusan Ekonomi Manajemen, fakultas Ekonomi, Universitas Kristen Petra.

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Lind, E.A. and T. Tyler, (1988), The Social Psychology of Procedural Justice,

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Munandar, M,, 1986, Budgeting : Perencanaan Kerja, Pengkoordinasian,

  Pengawasan , Yogyakarta : BPFE

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Masyarakat , Edisi Kedua, Cetakan Pertama, Jakarta : Salemba Empat.

  

Nafarin, 2000, Penganggaran Perusahaan, Edisi Pertama, Jakarta : Salemba

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P. Agustinus Aryo. (2007). “Pengaruh Partisipasi Dalam Penganggaran terhadap

Kinerja Manajer dengan Motivasi Kerja Sebagai Variabel

  Moderating. Skripsi (tidak dipublikasikan) Program Sarjana Universitas Katolik Soegijapranata Semarang.

P, Agatha Dyah Rukmi. (2006). “ Peran Partisipasi Anggaran Dalam Hubungan

Antara Keadilan Prosedural dengan Kinerja Manajerial. Skripsi (tidak dipublikasikan) Program Sarjana Universitas Katolik Soegijapranata Semarang.

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Sumadiyah, SE, AK & Sri Susanta, SE, Msi, AK.2004. Job Relevant Information

dan Ketidakpastian Lingkungan dalam Hubungan antara Penyusunan

  Partisipasi Anggaran dan Kinerja Manajerial. SNA VII. Denpasar Bali, 23 Desember.

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  Manajerial dan Kepuasan Kerja (Studi Empiris Pada Perusahaan Manufaktur di Semarang). Skripsi (tidak dipublikasikan) Program Sarjana Universitas Katolik Soegijapranata Semarang.

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Partisipasi Anggaran terhadap Kinerja Manajerial melalui Komitmen

  Tujuan Anggaran dan Job Relevant Information (JRI) sebagai Variabel Intervening. SNA VIII. Solo,15-16 September.

Hipotesis awal (PA ke KM) Regression b Variables Entered/Removed

  Variables Variables Model Entered Removed Method

  a

  1 pa . Enter a. All requested variables entered.

  b. Dependent Variable: km

  b Model Summary

  Adjusted R Std. Error of the Model R R Square Square Estimate

  a

  1 .515 .266 .255 6.45851

  a. Predictors: (Constant), pa

  b. Dependent Variable: km

  b ANOVA Model Sum of Squares df Mean Square F Sig. a

  1 Regression 1010.516 1 1010.516 24.226 .000 Residual 2794.730 67 41.712 Total 3805.246

  68

  a. Predictors: (Constant), pa

  a

Residuals Statistics

  Minimum Maximum Mean Std. Deviation N Predicted Value 21.7040 37.1649 28.1594 3.85494

  69 Residual -1.58577E1 12.25246 .00000 6.41085

  69 Std. Predicted Value -1.675 2.336 .000 1.000

  69 Std. Residual -2.455 1.897 .000 .993

  69

  a. Dependent Variable: km

Hipotesis awal Normalitas NPar Tests

  

One-Sample Kolmogorov-Smirnov Test

  Unstandardized Residual

  N

  69 Normal Parameters

  a

  Mean .0000000 Std. Deviation 6.41084785

  Most Extreme Differences Absolute .078 Positive .051 Negative -.078

  Kolmogorov-Smirnov Z .649 Asymp. Sig. (2-tailed) .794 a. Test distribution is Normal.

Uji Heterokedastisitas Regression b Variables Entered/Removed

  Variables Variables Model Entered Removed Method

  a

  1 pa . Enter a. All requested variables entered.

  b. Dependent Variable: abs

  Model Summary

  Adjusted R Std. Error of the Model R R Square Square Estimate

  a

  1 .000 .000 -.015 3.61737

  a. Predictors: (Constant), pa

  b ANOVA Model Sum of Squares df Mean Square F Sig. a

  1 Regression .000 1 .000 .000 .997 Residual 876.720 67 13.085 Total 876.721

  68

  a. Predictors: (Constant), pa

Uji Hipotesis 1 MODEL 1 (PA ke KP) Regression b Variables Entered/Removed

  Variables Variables Model Entered Removed Method

  a

  1 pa . Enter a. All requested variables entered.

  b. Dependent Variable: kp

  b Model Summary

  Adjusted R Std. Error of the Model R R Square Square Estimate

  a

  1 .327 .107 .093 4.83312

  a. Predictors: (Constant), pa

  b. Dependent Variable: kp

  b ANOVA Model Sum of Squares df Mean Square F Sig. a

  1 Regression 186.858 1 186.858 7.999 .006 Residual 1565.056 67 23.359 pa .317 .112 .327 2.828 .006

  a. Dependent Variable: kp

  a Residuals Statistics

  Minimum Maximum Mean Std. Deviation N Predicted Value 18.0502 24.6986 20.8261 1.65768

  69 Residual -1.07990E1 8.00005 .00000 4.79745

  69 Std. Predicted Value -1.675 2.336 .000 1.000

  69 Std. Residual -2.234 1.655 .000 .993

  69

  a. Dependent Variable: kp

Uji Normalitas NPar Tests

  

One-Sample Kolmogorov-Smirnov Test

  Unstandardized Residual

  N

  69 Normal Parameters

  a

  Mean .0000000 Std. Deviation 4.79744958

  Most Extreme Differences Absolute .091 Positive .091 Negative -.090

  Kolmogorov-Smirnov Z .753 Asymp. Sig. (2-tailed) .622 a. Test distribution is Normal.

Uji Heterokedastisitas Regression b Variables Entered/Removed

  Variables Variables Model Entered Removed Method

  a

  1 pa . Enter a. All requested variables entered.

  b. Dependent Variable: abs_pakp

  Model Summary

  Adjusted R Std. Error of the Model R R Square Square Estimate

  a

  1 .108 .012 -.003 2.43902

  a. Predictors: (Constant), pa

  b

ANOVA

Model Sum of Squares df Mean Square F Sig.

  1 (Constant) 4.992 1.046 4.774 .000 pa -.050 .056 -.108 -.885 .379 a. Dependent Variable: abs_pakp

UJI HIPOTESIS 1 MODEL 2 (PA,KP,KM) Regression b Variables Entered/Removed

  Variables Variables Model Entered Removed Method

  a

  1 KP, PA . Enter a. All requested variables entered.

  b. Dependent Variable: KM

  b Model Summary

  Adjusted R Std. Error of the Model R R Square Square Estimate

  a

  1 .656 .431 .414 5.72803

  a. Predictors: (Constant), KP, PA

  b. Dependent Variable: KM

  Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig.

  1 (Constant) 5.439 3.298 1.649 .104 PA .535 .140 .375 3.815 .000 KP .634 .145 .430 4.379 .000

  a. Dependent Variable: KM

  a Residuals Statistics

  Minimum Maximum Mean Std. Deviation N Predicted Value 18.5018 39.2583 28.1594 4.91062

  69 Residual -1.34823E1 11.25764 .00000 5.64316

  69 Std. Predicted Value -1.967 2.260 .000 1.000

  69 Std. Residual -2.354 1.965 .000 .985

  69

  a. Dependent Variable: KM

  

One-Sample Kolmogorov-Smirnov Test

  Unstandardized Residual

  N

  69 Normal Parameters

  a

  Mean .0000000 Std. Deviation 5.64316460

  Most Extreme Differences Absolute .114 Positive .072 Negative -.114

  Kolmogorov-Smirnov Z .951 Asymp. Sig. (2-tailed) .327 a. Test distribution is Normal.

Regression b Variables Entered/Removed

  Variables Variables Model Entered Removed Method

  a

  1 KP, PA . Enter a. All requested variables entered.

  b. Dependent Variable: abs_pakpkm

  Model Summary

  Adjusted R Std. Error of the Model R R Square Square Estimate

  a

  1 .127 .016 -.014 3.65745

  a. Predictors: (Constant), KP, PA

  b ANOVA Model Sum of Squares df Mean Square F Sig. a

  1 Regression 14.472 2 7.236 .541 .585 Residual 882.879 66 13.377 Total 897.351

  68

  a. Predictors: (Constant), KP, PA

  b. Dependent Variable: abs_pakpkm

Regression b Variables Entered/Removed

  Variables Variables Model Entered Removed Method

  a

  1 KP, PA . Enter a. All requested variables entered.

  b. Dependent Variable: KM

  Model Summary

  Adjusted R Std. Error of the Model R R Square Square Estimate

  a

  1 .656 .431 .414 5.72803

  a. Predictors: (Constant), KP, PA

  b ANOVA Model Sum of Squares df Mean Square F Sig. a

  1 Regression 1639.766 2 819.883 24.989 .000 Residual 2165.481 66 32.810 Total 3805.246

  68

  a. Predictors: (Constant), KP, PA

  b. Dependent Variable: KM

  Variance Proportions Dimensi

  Model on Eigenvalue Condition Index (Constant) PA KP

  1 1 2.924 1.000 .01 .01 .01 2 .048 7.780 .08 .95 .26 3 .028 10.299 .92 .04 .73

  a. Dependent Variable: KM

Hipotesis 2 MODEL 1 (PA ke M) Regression b Variables Entered/Removed

  Variables Variables Model Entered Removed Method

  a

  1 PA . Enter a. All requested variables entered.

  b. Dependent Variable: M

  b Model Summary

  Adjusted R Std. Error of the Model R R Square Square Estimate

  a

  1 .521 .271 .260 5.25087

  a. Predictors: (Constant), PA

  a

Coefficients

  Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig.

  1 (Constant) 13.671 2.251 6.072 .000 PA .607 .122 .521 4.995 .000

  a. Dependent Variable: M

  a Residuals Statistics

  Minimum Maximum Mean Std. Deviation N Predicted Value 19.1378 31.8937 24.4638 3.18051

  69 Residual -1.64640E1 9.61023 .00000 5.21211

  69 Std. Predicted Value -1.675 2.336 .000 1.000

  69 Std. Residual -3.135 1.830 .000 .993

  69

  a. Dependent Variable: M

Uji Normalitas NPar Tests

  

One-Sample Kolmogorov-Smirnov Test

  Unstandardized Residual

  N

  69 Normal Parameters

  a

  Mean .0000000 Std. Deviation 5.21211341

  Most Extreme Differences Absolute .106 Positive .070 Negative -.106

  Kolmogorov-Smirnov Z .880 Asymp. Sig. (2-tailed) .421 a. Test distribution is Normal.

Uji Heterokedastisitas Regression b Variables Entered/Removed

  Variables Variables Model Entered Removed Method

  a

  1 PA . Enter a. All requested variables entered.

  b. Dependent Variable: abs_pam

  Model Summary

  Adjusted R Std. Error of the Model R R Square Square Estimate

  a

  1 .004 .000 -.015 3.05152

  a. Predictors: (Constant), PA

  Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig.

  1 (Constant) 4.250 1.308 3.248 .002 PA -.002 .071 -.004 -.031 .975

  a. Dependent Variable: abs_pam

HIPOTESIS 2 MODEL 2 (PA, M , KM) Regression b Variables Entered/Removed

  Variables Variables Model Entered Removed Method

  a

  1 M, PA . Enter a. All requested variables entered.

  b. Dependent Variable: KM

  b Model Summary

  Adjusted R Std. Error of the Model R R Square Square Estimate

  b ANOVA Model Sum of Squares df Mean Square F Sig. a

  1 Regression 1208.771 2 604.386 15.363 .000 Residual 2596.475 66 39.341 Total 3805.246

  68

  b. Dependent Variable: KM

  a

Coefficients

  Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig.

  1 (Constant) 10.599 3.348 3.165 .002 PA .537 .170 .376 3.157 .002 M .328 .146 .267 2.245 .028

  a. Dependent Variable: KM

  a Residuals Statistics

  Minimum Maximum Mean Std. Deviation N Predicted Value 19.6933 37.6453 28.1594 4.21617

  69 Residual -1.41011E1 10.97909 .00000 6.17928

  69 Std. Predicted Value -2.008 2.250 .000 1.000

  69 Std. Residual -2.248 1.750 .000 .985

  69

Uji Normalitas NPar Tests

  

One-Sample Kolmogorov-Smirnov Test

  Unstandardized Residual

  N

  69 Normal Parameters

  a

  Mean .0000000 Std. Deviation 6.17927645

  Most Extreme Differences Absolute .080 Positive .051 Negative -.080

  Kolmogorov-Smirnov Z .666 Asymp. Sig. (2-tailed) .767 a. Test distribution is Normal.

  

One-Sample Kolmogorov-Smirnov Test

  Unstandardized Residual

  N

  69 Normal Parameters

  a

  Mean .0000000 Std. Deviation 6.17927645

  Most Extreme Differences Absolute .080 Positive .051 Negative -.080

  Kolmogorov-Smirnov Z .666 Asymp. Sig. (2-tailed) .767

Uji Heterokedastisitas Regression b Variables Entered/Removed

  Variables Variables Model Entered Removed Method

  a

  1 M, PA . Enter a. All requested variables entered.

  b. Dependent Variable: abs_pamkm

  Model Summary

  Adjusted R Std. Error of the Model R R Square Square Estimate

  a

  1 .080 .006 -.024 3.34244

  a. Predictors: (Constant), M, PA

  a

Coefficients

  Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig.

  1 (Constant) 5.338 1.784 2.991 .004 PA .052 .091 .082 .574 .568

Uji Multikolinearitas Regression b Variables Entered/Removed

  Variables Variables Model Entered Removed Method

  a

  1 M, PA . Enter a. All requested variables entered.

  b. Dependent Variable: KM

  Model Summary

  Adjusted R Std. Error of the Model R R Square Square Estimate

  a

  1 .564 .318 .297 6.27220

  a. Predictors: (Constant), M, PA

  a

Coefficients

  Standardized Unstandardized Coefficients Coefficients Collinearity Statistics

  Model B Std. Error Beta t Sig. Tolerance

  VIF 1 (Constant) 10.599 3.348 3.165 .002 PA .537 .170 .376 3.157 .002 .729 1.372

Hipotesis 3 MODEL 1 (PA ke JRI) Regression b Variables Entered/Removed

  Variables Variables Model Entered Removed Method

  a

  1 PA . Enter a. All requested variables entered.

  b. Dependent Variable: JRI

  b Model Summary

  Adjusted R Std. Error of the Model R R Square Square Estimate

  a

  1 .363 .132 .119 3.52720

  a. Predictors: (Constant), PA

  b. Dependent Variable: JRI

  b

ANOVA

Model Sum of Squares df Mean Square F Sig.

  1 (Constant) 11.319 1.512 7.484 .000 PA .260 .082 .363 3.185 .002

  a. Dependent Variable: JRI

  a Residuals Statistics

  Minimum Maximum Mean Std. Deviation N Predicted Value 13.6606 19.1247 15.9420 1.36237

  69 Residual -6.56351 7.07917 .00000 3.50117

  69 Std. Predicted Value -1.675 2.336 .000 1.000

  69 Std. Residual -1.861 2.007 .000 .993

  69

  a. Dependent Variable: JRI

  

One-Sample Kolmogorov-Smirnov Test

  Unstandardized Residual

  N

  69 Normal Parameters

  a

  Mean .0000000 Std. Deviation 3.50116863

  Most Extreme Differences Absolute .094 Positive .078 Negative -.094

  Kolmogorov-Smirnov Z .781 Asymp. Sig. (2-tailed) .576 a. Test distribution is Normal.

  b Variables Entered/Removed

  Variables Variables Model Entered Removed Method

  a

  1 PA . Enter a. All requested variables entered.

  b. Dependent Variable: abs_pajri

  Model Summary

  Adjusted R Std. Error of the Model R R Square Square Estimate

  a

  1 .188 .035 .021 1.81195

  a. Predictors: (Constant), PA

  b ANOVA Model Sum of Squares df Mean Square F Sig. a

  1 Regression 8.057 1 8.057 2.454 .122 Residual 219.972 67 3.283 Total 228.028

  68

  a. Predictors: (Constant), PA

  b. Dependent Variable: abs_pajri

  a

Coefficients

Hipotesis 3 MODEL 1 (PA ke JRI) Regression b Variables Entered/Removed

  Variables Variables Model Entered Removed Method

  a

  1 PA . Enter a. All requested variables entered.

  b. Dependent Variable: JRI

  b Model Summary

  Adjusted R Std. Error of the Model R R Square Square Estimate

  a

  1 .363 .132 .119 3.52720

  a. Predictors: (Constant), PA

  b. Dependent Variable: JRI

  b ANOVA Model Sum of Squares df Mean Square F Sig. a

  1 Regression 126.212 1 126.212 10.145 .002 Residual 833.556 67 12.441

  a

Coefficients

  Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig.

  1 (Constant) 11.319 1.512 7.484 .000 PA .260 .082 .363 3.185 .002

  a. Dependent Variable: JRI

  a Residuals Statistics

  Minimum Maximum Mean Std. Deviation N Predicted Value 13.6606 19.1247 15.9420 1.36237

  69 Residual -6.56351 7.07917 .00000 3.50117

  69 Std. Predicted Value -1.675 2.336 .000 1.000

  69 Std. Residual -1.861 2.007 .000 .993

  69

  a. Dependent Variable: JRI

Uji Normalitas NPar Tests

  

One-Sample Kolmogorov-Smirnov Test

  Unstandardized Residual

  N

  69 Normal Parameters

  a

  Mean .0000000 Std. Deviation 3.50116863

  Most Extreme Differences Absolute .094 Positive .078 Negative -.094

  Kolmogorov-Smirnov Z .781 Asymp. Sig. (2-tailed) .576 a. Test distribution is Normal.

Uji Heterokedastisitas Regression b Variables Entered/Removed

  Variables Variables Model Entered Removed Method

  a

  1 PA . Enter a. All requested variables entered.

  b. Dependent Variable: abs_pajri

  Model Summary

  Adjusted R Std. Error of the Model R R Square Square Estimate

  a

  1 .188 .035 .021 1.81195

  a. Predictors: (Constant), PA

  b ANOVA Model Sum of Squares df Mean Square F Sig. a

  1 Regression 8.057 1 8.057 2.454 .122 Residual 219.972 67 3.283 Total 228.028

  68

  a. Predictors: (Constant), PA

Hipotesis 3 MODEL 1 (PA ke JRI) Regression b Variables Entered/Removed

  Variables Variables Model Entered Removed Method

  a

  1 PA . Enter a. All requested variables entered.

  b. Dependent Variable: JRI

  b Model Summary

  Adjusted R Std. Error of the Model R R Square Square Estimate

  a

  1 .363 .132 .119 3.52720

  a. Predictors: (Constant), PA

  b. Dependent Variable: JRI

  b

  Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig.

  1 (Constant) 11.319 1.512 7.484 .000 PA .260 .082 .363 3.185 .002

  a. Dependent Variable: JRI

  a Residuals Statistics

  Minimum Maximum Mean Std. Deviation N Predicted Value 13.6606 19.1247 15.9420 1.36237

  69 Residual -6.56351 7.07917 .00000 3.50117

  69 Std. Predicted Value -1.675 2.336 .000 1.000

  69 Std. Residual -1.861 2.007 .000 .993

  69

  a. Dependent Variable: JRI

NPar Tests

  

One-Sample Kolmogorov-Smirnov Test

  Unstandardized Residual

  N

  69 Normal Parameters

  a

  Mean .0000000 Std. Deviation 3.50116863

  Most Extreme Differences Absolute .094 Positive .078 Negative -.094

  Kolmogorov-Smirnov Z .781 Asymp. Sig. (2-tailed) .576 a. Test distribution is Normal.

Regression b Variables Entered/Removed

  Variables Variables Model Entered Removed Method

  a

  1 PA . Enter a. All requested variables entered.

  b. Dependent Variable: abs_pajri

  Model Summary

  Adjusted R Std. Error of the Model R R Square Square Estimate

  a

  1 .188 .035 .021 1.81195

  a. Predictors: (Constant), PA

  b ANOVA Model Sum of Squares df Mean Square F Sig. a

  1 Regression 8.057 1 8.057 2.454 .122 Residual 219.972 67 3.283 Total 228.028

  68

  a. Predictors: (Constant), PA

  b. Dependent Variable: abs_pajri

Hipotesis 3 MODEL 2 (PA, JRI, KM) Regression b Variables Entered/Removed

  Variables Variables Model Entered Removed Method

  a

  1 JRI, PA . Enter a. All requested variables entered.

  b. Dependent Variable: KM

  b Model Summary

  Adjusted R Std. Error of the Model R R Square Square Estimate

  a

  1 .700 .490 .475 5.42039

  a. Predictors: (Constant), JRI, PA

  b. Dependent Variable: KM

  b

ANOVA

  a

Coefficients

  Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig.

  1 (Constant) 3.610 3.149 1.146 .256 PA .473 .135 .331 3.509 .001 JRI 1.013 .188 .509 5.396 .000

  a. Dependent Variable: KM

  a Residuals Statistics

  Minimum Maximum Mean Std. Deviation N Predicted Value 17.9953 38.5923 28.1594 5.23861

  69 Residual -1.31613E1 11.57370 .00000 5.34008

  69 Std. Predicted Value -1.940 1.992 .000 1.000

  69 Std. Residual -2.428 2.135 .000 .985

  69

  a. Dependent Variable: KM

Uji Normalitas NPar Tests

  

One-Sample Kolmogorov-Smirnov Test

  Unstandardized Residual

  N

  69 Normal Parameters

  a

  Mean .0000000 Std. Deviation 5.34008039

  Most Extreme Differences Absolute .103 Positive .086 Negative -.103

  Kolmogorov-Smirnov Z .858 Asymp. Sig. (2-tailed) .454 a. Test distribution is Normal.

Uji Heterokedastisitas Regression b Variables Entered/Removed

  Variables Variables Model Entered Removed Method

  a

  1 JRI, PA . Enter a. All requested variables entered.

  b. Dependent Variable: abs_pajrikm

  Model Summary

  Adjusted R Std. Error of the Model R R Square Square Estimate

  a

  1 .142 .020 -.010 3.52809

  a. Predictors: (Constant), JRI, PA

  b

ANOVA B Std. Error Beta 1 (Constant) 4.032 2.050 1.967 .053 PA .092 .088 .137 1.046 .299 JRI -.105 .122 -.112 -.856 .395

  a. Dependent Variable: abs_pajrikm

Uji Multikolinearitas Regression b Variables Entered/Removed

  Variables Variables Model Entered Removed Method

  a

  1 JRI, PA . Enter a. All requested variables entered.

  b. Dependent Variable: KM

  Model Summary

  Adjusted R Std. Error of the Model R R Square Square Estimate

  a

  1 .700 .490 .475 5.42039

  a. Predictors: (Constant), JRI, PA

  b

  Standardized Unstandardized Coefficients Coefficients Collinearity Statistics

  Model B Std. Error Beta t Sig. Tolerance

  VIF 1 (Constant) 3.610 3.149 1.146 .256 PA .473 .135 .331 3.509 .001 .868 1.151 JRI 1.013 .188 .509 5.396 .000 .868 1.151

  a. Dependent Variable: KM

  a Collinearity Diagnostics

  Variance Proportions Dimensi

  Model on Eigenvalue Condition Index (Constant) PA JRI

  1 1 2.927 1.000 .00 .01 .01 2 .046 7.953 .12 .99 .20 3 .026 10.566 .87 .01 .80

  a. Dependent Variable: KM