شناسايي عو امل بازدارنده فرهنگي بر بهره‌وری در شركت پست جمهوري اسلامي ايران، پايان‌نامه كارشناسي ارشد مديريت دولتي، مؤسسه آموزش و پژوهش مديريت و برنامه‌ريزي.
فرازی بر SPSS، ۱۳۸۴، مترجمین: علی افش‍انی، مرتضی نوریان، زینب حسین‌رامش‍ه، ته‍ران، انتشارات بیشه،
قاسمي، ليلا، 1383، شناخت عوامل مديريتي مؤثر بر بهره‌وری نيروي انساني از نظر مديران ارشد و مياني بخش صنعت آذربايجان‌شرقي، پايان‌نامه كارشناسي ارشد مديريت دولتي، مؤسسه آموزش و پژوهش مديريت و برنامه‌ريزي.
انگلیسی
Almeida, R. & Carneiro, P., 2009, The Return to Firm Investments in Human Capital. Labour Economics, 116, pp. 97-106̷
Connel, Julia and Hannif, Zeenobiyah, 2009 “Call Centers, Quality of Work life and HRM Practices”, Employee Relations, 31/4, 2009, pp. 363-381,
Hersey P, Blanchard K.H ,1998, Management of organizational behavior: Utilizing human resources, (5th ed.) prentice hall Inc, Englewood cliffs, New Jersey.
Huselid, M, 1995,The Impact of Human Resource Management Practices on Turnover, Productivity and Corporate Financial Performance, Academy of Management Journal. 38(5): 672-635
Liu,J.and A. Sakamoto, 2005, Reiative deprivation, efficiency wages, and labor productivity in Taiwanese manufacturing industries, Research in Social Stratification and Mobility, 23:303-341
Yang, Seung-Bum and Choi,Sang, 2009 “Employee Empowerment and Team Performance”,Team Performance Management,5/15, pp. 289-30.
پیوست
پیوست الف خروجی ازمون اسمیرنف و کولوموگرف:
One-Sample Kolmogorov-Smirnov Test
bahrevari
niazasasi
mosharekatkarkonan
rabari
N
121
121
121
121
Normal Parametersa,b
Mean
2.8292
2.6299
2.6074
2.7421
Std. Deviation
.87572
.56681
.77246
.68675
Most Extreme Differences
Absolute
.142
.075
.137
.097
Positive
.142
.075
.137
.097
Negative
-.078
-.069
-.084
-.071
Kolmogorov-Smirnov Z
1.078
.826
1.083
1.072
Asymp. Sig. (2-tailed)
.215
.502
.222
.201
a. Test distribution is Normal.
b. Calculated from data.
پیوست ب:خروجی نرم افزار EQS
EQS, A STRUCTURAL EQUATION PROGRAM MULTIVARIATE SOFTWARE, INC.
COPYRIGHT BY P.M. BENTLER VERSION 6.1 (C) 1985 – 2005 (B85).
04-Apr-15 PAGE : 2 EQS Licensee:
TITLE: Model built by EQS 6 for Windows
53 E9 = *;
54 E10 = *;
55 E11 = *;
56 E12 = *;
57 E13 = *;
58 E14 = *;
59 E15 = *;
60 E16 = *;
61 E18 = *;
62 E19 = *;
63 E20 = *;
64 E21 = *;
65 E22 = *;
66 E23 = *;
67 E24 = *;
68 E25 = *;
69 D1 = *;
70 /COVARIANCES
71 /PRINT
72 FIT=ALL;
73 TABLE=EQUATION;
74 /OUTPUT
75 Parameters;
76 Standard Errors;
77 RSquare;
78 Codebook;
79 Listing;
80 DATA=’EQSOUT.ETS’;
81 /END
81 RECORDS OF INPUT MODEL FILE WERE READ
DATA IS READ FROM D:workeqs pourhasanpourhasan.ess
THERE ARE 29 VARIABLES AND 121 CASES
IT IS A RAW DATA ESS FILE
04-Apr-15 PAGE : 3 EQS Licensee:
TITLE: Model built by EQS 6 for Windows
SAMPLE STATISTICS BASED ON COMPLETE CASES
UNIVARIATE STATISTICS
———————
VARIABLE V1 V2 V3 V4 V5
V1 V2 V3 V4 V5
MEAN 2.7521 2.7603 2.5702 2.2314 2.1570
SKEWNESS (G1) .0938 .0355 -.1432 .3354 .4833
KURTOSIS (G2) .5794 .0504 -.5141 -.5640 -.1928
STANDARD DEV. .7559 .8758 .9204 .8828 .9575
KURTOSIS (ETA) 1.0923 1.0084 .9103 .9011 .9673
VARIABLE V6 V7 V8 V9 V10
V6 V7 V8 V9 V10
MEAN 2.9174 2.1157 3.0826 3.0826 3.4711
SKEWNESS (G1) -.2766 .2944 -.3876 -.3942 7.0464
KURTOSIS (G2) .0811 -.9882 .3537 .1090 65.1752
STANDARD DEV. .9272 .9504 .8717 .8620 2.1644
KURTOSIS (ETA) 1.0134 .8189 1.0573 1.0180 4.7671
VARIABLE V11 V12 V13 V14 V15
V11 V12 V13 V14 V15
MEAN 2.4545 3.5207 2.7355 2.8017 2.9256
SKEWNESS (G1) .0396 9.1225 -.0094 .0156 .1932
KURTOSIS (G2) -.3155 91.8650 -.1162 .0125 -.4181
STANDARD DEV. .9309 2.7631 .8638 .8720 1.0422
KURTOSIS (ETA) .9460 5.6233 .9804 1.0021 .9277
VARIABLE V16 V18 V19 V20 V21
V16 V18 V19 V20 V21
MEAN 2.9752 2.3223 2.6694 3.0909 2.8099
SKEWNESS (G1) 9.0314 .1875 .1032 -.3993 -.0890
KURTOSIS (G2) 90.4312 -.6841 -.2053 -.0448 -.1212
STANDARD DEV. 2.8268 .8871 .8793 .9747 .9066
KURTOSIS (ETA) 5.5807 .8786 .9652 .9925 .9796
VARIABLE V22 V23 V24 V25
V22 V23 V24 V25
MEAN 2.5372 2.6033 2.1818 2.0165
SKEWNESS (G1) .1625 .2564 .3168 .4010
KURTOSIS (G2) -.6953 -.2049 -.7959 -1.0727
STANDARD DEV. 1.0804 .9702 .9309 .9745
KURTOSIS (ETA) .8765 .9653 .8572 .8015
MULTIVARIATE KURTOSIS
———————
MARDIA’S COEFFICIENT (G2,P) = 217.8578
NORMALIZED ESTIMATE = 33.9179
ELLIPTICAL THEORY KURTOSIS ESTIMATES
————————————
MARDIA-BASED KAPPA = .3491 MEAN SCALED UNIVARIATE KURTOSIS = 3.3573
MARDIA-BASED KAPPA IS USED IN COMPUTATION. KAPPA= .3491
CASE NUMBERS WITH LARGEST CONTRIBUTION TO NORMALIZED MULTIVARIATE KURTOSIS:
—————————————————————————
CASE NUMBER 16 34 53 104 112
ESTIMATE 1919.7664 180.2130 291.5309 1858.0754 1649.2849
04-Apr-15 PAGE : 4 EQS Licensee:
TITLE: Model built by EQS 6 for Windows
COVARIANCE MATRIX TO BE ANALYZED: 24 VARIABLES (SELECTED FROM 29 VARIABLES)
BASED ON 121 CASES.
V1 V2 V3 V4 V5
V1 V2 V3 V4 V5
V1 V1 .571
V2 V2 .340 .767
V3 V3 .193 .329 .847
V4 V4 .150 .131 .167 .779
V5 V5 .256 .246 .235 .372 .917
V6 V6 .229 .230 .048 .319 .471
V7 V7 .121 .153 .283 .365 .482
V8 V8 .171 .253 .227 .214 .329
V9 V9 .121 .203 .219 .189 .270
V10 V10 -.099 .106 -.021 -.010 .417
V11 V11 .080 .218 .280 .427 .370
V12 V12 .097 .067 -.099 .370 .376
V13 V13 .151 .253 .194 .312 .284
V14 V14 .100 .152 .147 .221 .340
V15 V15 .081 .174 .184 .317 .362
V16 V16 .127 .227 .273 .256 .012
V18 V18 .081 .078 .156 .216 .216
V19 V19 .076 .220 .190 .235 .186
V20 V20 .164 .197 .139 .187 .402
V21 V21 .094 .112 .068 .378 .313
V22 V22 .093 .096 .124 .266 .373
V23 V23 .059 .129 .153 .426 .354
V24 V24 .062 .111 .229 .374 .371
V25 V25 .071 .196 .174 .396 .406
V6 V7 V8 V9 V10
V6 V7 V8 V9 V10
V6 V6 .860
V7 V7 .418 .903
V8 V8 .299 .299 .760
V9 V9 .232 .340 .535 .743
V10 V10 .189 .220 .352 .169 4.685
V11 V11 .271 .339 .229 .287 .434
V12 V12 .218 .373 .257 .007 .203
V13 V13 .261 .214 .305 .272 .076
V14 V14 .242 .240 .342 .308 .219
V15 V15 .269 .300 .298 .223 .110
V16 V16 -.210 -.022 .210 .152 -.172
V18 V18 .144 .196 .181 .106 .147
V19 V19 .181 .180 .269 .161 .374
V20 V20 .291 .156 .209 .101 .173
V21 V21 .217 .281 .216 .116 .199
V22 V22 .353 .354 .147 .055 .570
V23 V23 .350 .288 .283 .233 .280
V24 V24 .232 .337 .177 .235 -.061
V25 V25 .335 .406 .315 .340 .142
V11 V12 V13 V14 V15
V11 V12 V13 V14 V15
V11 V11 .867
V12 V12 .303 7.635
V13 V13 .346 .056 .746
V14 V14 .341 .412 .380 .760
V15 V15 .267 .372 .380 .360 1.086
V16 V16 .086 -.054 .368 .087 .698
V18 V18 .136 .522 .194 .164 .208
V19 V19 .285 .340 .329 .226 .325
V20 V20 .283 .394 .241 .185 .282
V21 V21 .362 .508 .224 .270 .327
V22 V22 .245 .176 .110 .082 .307
V23 V23 .398 .067 .419 .362 .487
V24 V24 .400 -.004 .207 .245 .255
V25 V25 .376 -.150 .321 .270 .385
V16 V18 V19 V20 V21
V16 V18 V19 V20 V21
V16 V16 7.991
V18 V18 .375 .787
V19 V19 .042 .216 .773
V20 V20 .186 .079 .289 .950
V21 V21 .112 .128 .262 .367 .822
V22 V22 .180 .259 .271 .342 .353
V23 V23 .032 .296 .418 .436 .399
V24 V24 .063 .116 .152 .225 .285
V25 V25 .334 .220 .247 .323 .237
V22 V23 V24 V25
V22 V23 V24 V25
V22 V22 1.167
V23 V23 .432 .941
V24 V24 .418 .464 .867
V25 V25 .358 .523 .522 .950
BENTLER-WEEKS STRUCTURAL REPRESENTATION:
NUMBER OF DEPENDENT VARIABLES = 25
DEPENDENT V’S : 1 2 3 4 5 6 7 8 9 10
DEPENDENT V’S : 11 12 13 14 15 16 18 19 20 21
DEPENDENT V’S : 22 23 24 25
DEPENDENT F’S : 1
NUMBER OF INDEPENDENT VARIABLES = 28
INDEPENDENT F’S : 2 3 4
INDEPENDENT E’S : 1 2 3 4 5 6 7 8 9 10
INDEPENDENT E’S : 11 12 13 14 15 16 18 19 20 21
INDEPENDENT E’S : 22 23 24 25
INDEPENDENT D’S : 1
NUMBER OF FREE PARAMETERS = 51
NUMBER OF FIXED NONZERO PARAMETERS = 29
*** WARNING MESSAGES ABOVE, IF ANY, REFER TO THE MODEL PROVIDED.
CALCULATIONS FOR INDEPENDENCE MODEL NOW BEGIN.
*** WARNING MESSAGES ABOVE, IF ANY, REFER TO INDEPENDENCE MODEL.
CALCULATIONS FOR USER’S MODEL NOW BEGIN.
3RD STAGE OF COMPUTATION REQUIRED 70422 WORDS OF MEMORY.
PROGRAM ALLOCATED 2000000 WORDS
DETERMINANT OF INPUT MATRIX IS .53006D-03
04-Apr-15 PAGE : 5 EQS Licensee:
TITLE: Model built by EQS 6 for Windows
ITERATIVELY REWEIGHTED LEAST SQUARES SOLUTION (HETEROGENEOUS KURTOSIS DISTRIBUTION THEORY)
FOLLOWING TECHNICAL INFORMATION HAS BEEN STORED IN EQSOUT.ETS
—————————————————————————–
TAIL PROBABILITIES NOT APPLICABLE TO THIS ANALYSIS ARE WRITTEN AS -1.
OTHER STATISTICS WHICH ARE NOT APPLICABLE ARE WRITTEN AS -9.
SUMMARY SECTION CONTAINS–
LINE 1 BEGINNING: ANALYSIS …
LINE 2 CONTAINING THESE 11 ELEMENTS OF MODEL STATISTICS:
ESTIMATION METHOD (LS,GLS,ML,ELS,EGLS,ERLS,AGLS,HKGLS,HKRLS)
CONDITION CODE (0 FOR NORMAL CONDITION)
CONVERGENCE (0 FOR MODEL CONVERGED)
NUMBER OF ITERATIONS FOR CONVERGENCE
DEGREES OF FREEDOM
NUMBER OF CONSTRAINTS
DENOMINATOR DEGREES OF FREEDOM FOR F-TESTS
DEGREES OF FREEDOM FOR POTENTIAL STRUCTURED MEANS MODEL TEST
D.F. FOR GLS TEST OF HOMOGENEITY OF MEANS
D.F. FOR GLS TEST OF HOMOGENEITY OF COVARIANCE MATRICES
D.F. FOR GLS COMBINED TEST OF HOMOGENEITY OF MEANS/COVAS.
LINE 3 CONTAINING THESE 10 ELEMENTS OF MODEL STATISTICS:
TAIL PROBABILITY FOR MODEL CHI-SQUARE
TAIL PROBABILITY FOR RESIDUAL-BASED TEST STATISTIC
