There are a number of choices the author have made which delineate some characteristics of
the research: (1) dealing with single respondent for organizational-level data, not multiple
respondents; (2) focusing on one accounting-related ERP construct, perceived accounting
benefit only; (3) additionally analyzing difference of accounting professionals’ perceptions by
the length of ERP adoption, by organizational size, and by ERP vendors; (4) additionally
investigating indirect effects existing in the ESMAP; and (5) employing effective use scale of
Deng et al. (2004) instead of the original of William J Doll and Torkzadeh (1998). These
delineations allow identifying a number of potential avenues to extend the research

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riances
assumed
.105 .746 .923 118 .358 .168 .182 -.192 .527
Equal variances
not assumed
.897 75.042 .373 .168 .187 -.205 .540
PAB16 Equal variances
assumed
4.032 .047 2.030 118 .045 .384 .189 .010 .759
Equal variances
not assumed
2.115 90.780 .057 .384 .182 .023 .745
PAB17 Equal variances
assumed
.542 .463 3.535 118 .051 .948 .268 .417 1.479
Equal variances
not assumed
3.402 72.998 .061 .948 .279 .392 1.503
PAB18 Equal variances
assumed
.164 .686 2.547 118 .052 .634 .249 .141 1.127
Equal variances
not assumed
2.506 77.564 .054 .634 .253 .130 1.138
USE1 Equal variances
assumed
3.641 .059 3.280 118 .001 .636 .194 .252 1.020
Equal variances
not assumed
3.079 68.381 .003 .636 .207 .224 1.048
USE2 Equal variances
assumed
.087 .769 2.154 118 .033 .509 .236 .041 .977
Equal variances
not assumed
2.054 71.290 .044 .509 .248 .015 1.003
USE3 Equal variances
assumed
2.648 .106 2.497 118 .054 .447 .179 .093 .802
Equal variances
not assumed
2.347 68.583 .062 .447 .191 .067 .827
USE4 Equal variances
assumed
.000 .986 1.716 118 .089 .315 .184 -.049 .678
Equal variances
not assumed
1.692 78.072 .095 .315 .186 -.056 .685
USE5 Equal variances
assumed
.370 .544 2.163 118 .063 .489 .226 .041 .937
Equal variances
not assumed
2.032 68.528 .076 .489 .241 .009 .969
USE6 Equal variances
assumed
.127 .722 .770 118 .443 .185 .240 -.290 .659
Equal variances
not assumed
.769 80.754 .444 .185 .240 -.293 .662
USE7 Equal variances
assumed
.043 .835 1.246 118 .215 .234 .188 -.138 .605
Equal variances
not assumed
1.260 83.670 .211 .234 .186 -.135 .603
USE8 Equal variances
assumed
.243 .623 1.967 118 .052 .467 .237 -.003 .936
222
Equal variances
not assumed
1.885 72.216 .063 .467 .247 -.027 .960
USE9 Equal variances
assumed
.934 .336 .641 118 .523 .137 .213 -.286 .559
Equal variances
not assumed
.665 89.636 .508 .137 .206 -.272 .546
USE10 Equal variances
assumed
.039 .844 1.410 118 .161 .275 .195 -.111 .662
Equal variances
not assumed
1.406 80.507 .164 .275 .196 -.114 .664
USE11 Equal variances
assumed
.311 .578 .733 118 .465 .196 .267 -.333 .726
Equal variances
not assumed
.756 88.171 .452 .196 .259 -.319 .712
SAT1 Equal variances
assumed
.028 .867 1.880 118 .063 .362 .192 -.019 .743
Equal variances
not assumed
1.875 80.472 .064 .362 .193 -.022 .746
SAT2 Equal variances
assumed
1.249 .266 1.976 118 .051 .377 .191 -.001 .755
Equal variances
not assumed
1.899 72.836 .061 .377 .199 -.019 .773
SAT3 Equal variances
assumed
.400 .528 1.377 118 .171 .244 .177 -.107 .594
Equal variances
not assumed
1.416 87.690 .160 .244 .172 -.098 .585
SAT4 Equal variances
assumed
.929 .337 1.876 118 .063 .355 .189 -.020 .729
Equal variances
not assumed
1.824 75.115 .072 .355 .194 -.033 .742
AP1 Equal variances
assumed
.010 .919 2.980 118 .064 .394 .132 .132 .656
Equal variances
not assumed
3.075 88.461 .063 .394 .128 .139 .649
AP2 Equal variances
assumed
3.451 .066 3.209 118 .072 .454 .142 .174 .735
Equal variances
not assumed
3.079 72.484 .073 .454 .148 .160 .749
AP3 Equal variances
assumed
.849 .359 2.717 118 .068 .379 .140 .103 .656
Equal variances
not assumed
2.712 80.666 .068 .379 .140 .101 .658
AP4 Equal variances
assumed
.004 .951 2.505 118 .054 .321 .128 .067 .574
Equal variances
not assumed
2.529 83.203 .053 .321 .127 .068 .573
OP1 Equal variances
assumed
.207 .650 1.682 118 .095 .267 .159 -.047 .582
Equal variances
not assumed
1.665 78.738 .100 .267 .161 -.052 .587
OP2 Equal variances
assumed
4.361 .039 .362 118 .718 .049 .135 -.218 .316
Equal variances
not assumed
.402 106.710 .688 .049 .121 -.192 .289
OP3 Equal variances
assumed
1.446 .232 .639 118 .524 .087 .136 -.182 .356
Equal variances
not assumed
.624 76.058 .535 .087 .139 -.190 .364
223
OP4 Equal variances
assumed
.268 .606 .245 118 .807 .035 .144 -.249 .320
Equal variances
not assumed
.249 84.833 .804 .035 .141 -.246 .316
OP5 Equal variances
assumed
.572 .451 .519 118 .605 .070 .136 -.198 .339
Equal variances
not assumed
.552 95.915 .583 .070 .128 -.183 .324
OP6 Equal variances
assumed
.992 .321 2.399 118 .058 .344 .144 .060 .628
Equal variances
not assumed
2.353 76.901 .061 .344 .146 .053 .636
OP7 Equal variances
assumed
2.844 .094 1.520 118 .131
.174 .115 -.053 .402
Equal variances
not assumed
1.617 96.098 .109 .174 .108 -.040 .389
OP8 Equal variances
assumed
.404 .526 .468 118 .641 .056 .120 -.181 .294
Equal variances
not assumed
.471 82.616 .639 .056 .119 -.181 .293
U1 Equal variances
assumed
4.691 .032 4.421 118 .000 1.416 .320 .782 2.050
Equal variances
not assumed
4.192 70.235 .000
1.416 .338 .742 2.090
U2 Equal variances
assumed
7.042 .009 4.237 118 .000 1.203 .284 .641 1.766
Equal variances
not assumed
3.888 64.568 .000 1.203 .309 .585 1.821
U3 Equal variances
assumed
1.662 .200 3.587 118 .000
1.040 .290 .466 1.615
Equal variances
not assumed
3.485 74.910 .001 1.040 .299 .446 1.635
224
Appendix 5.4
Total Variance Explained for Common Method Bias Test
Component
Initial Eigenvalues Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
1 28.029 42.468 42.468 28.029 42.468 42.468
2 4.274 6.476 48.944
3 3.596 5.449 54.393
4 3.074 4.657 59.050
5 2.291 3.471 62.521
6 2.050 3.107 65.628
7 1.643 2.489 68.117
8 1.591 2.410 70.527
9 1.445 2.190 72.717
10 1.352 2.049 74.766
11 1.008 1.527 76.293
12 .965 1.462 77.755
13 .945 1.432 79.188
14 .878 1.331 80.519
15 .834 1.264 81.783
16 .755 1.144 82.927
17 .734 1.112 84.038
18 .657 .996 85.034
19 .636 .964 85.998
20 .544 .824 86.822
21 .520 .787 87.609
22 .503 .762 88.371
23 .480 .727 89.098
24 .465 .705 89.803
25 .452 .686 90.488
26 .404 .612 91.100
27 .384 .582 91.683
28 .368 .557 92.240
29 .350 .530 92.770
30 .328 .498 93.268
31 .323 .490 93.757
32 .304 .460 94.218
33 .285 .431 94.649
34 .275 .416 95.065
35 .262 .397 95.462
225
36 .241 .365 95.826
37 .214 .324 96.151
38 .208 .315 96.466
39 .186 .282 96.748
40 .178 .269 97.017
41 .169 .255 97.272
42 .154 .234 97.506
43 .145 .220 97.726
44 .139 .211 97.937
45 .138 .208 98.146
46 .120 .181 98.327
47 .113 .171 98.498
48 .110 .167 98.665
49 .106 .160 98.825
50 .091 .137 98.963
51 .084 .128 99.091
52 .078 .118 99.208
53 .072 .109 99.317
54 .067 .101 99.418
55 .058 .088 99.507
56 .049 .074 99.580
57 .046 .069 99.649
58 .040 .061 99.710
59 .037 .056 99.766
60 .033 .051 99.816
61 .030 .045 99.862
62 .026 .039 99.900
63 .022 .033 99.934
64 .019 .029 99.963
65 .013 .020 99.983
66 .011 .017 100.000
226
Appendix 5.5
Development of the final sample
Number of potential respondents emailed 5110
Collected responses 569
Less:
Screening and
cleaning
Organization not-adopting ERP system 177
Irrelevant length of ERP adoption 0
Irrelevant respondent position 78
Respondent without enough working experience year 50
Incomplete cases 102
Response time of less than 10 minutes 26 433
Straight lining 0
Outlier 16
Final sample 120
(Source: by author)
227
Appendix 5.6
Instrument reliability assessment
Internal consistency is adopted to assess reliability through two statistical indicators: (1) item-
total correlation and (2) Cronbach’s alpha. Each statistics examines different aspect of reliability
issue
Item-total correlation indicates if a single item is reliable in representing the instrument.
Specifically, low correlation between items indicates that the items do not represent the
same instrument. A correlation value less than 0.3 indicates that the corresponding item
does not correlate very well with the scale overall and thus may be dropped (Hair Jr. et
al., 2010). High value (>0.95), however, are suspect as they indicate multi-collinearity
and the possibility that respondents have not answered objectively (Straub et al., 2004).
Cronbach’s alpha is a widely used index to assess internal consistency reliability
(Streiner, 2003). It indicates if the items as a whole represent the instrument. Values of
Cronbach’s alpha range from 0 to 1, with higher values indicate greater reliability (Straub
et al., 2004). DeVellis (2016) proposes a common accepted rule describing internal
consistency using Cronbach’s alpha as follow:
The ‘Cronbach’s alpha if item deleted’ presents the hypothetical Cronbach’s alpha
coefficients that would be obtained if each item is deleted. Hence, by examining the
results, a researcher can immediately determine whether deleting a particular item would
increase or decrease overall reliability.
Based on the following Table of results of Conbach’s Alpha Test, the item-total correlation value
of each item to its designated instrument is greater than the cut-off value of 0.3. This indicates
that each item has strong internal consistency with other items of the instrument. In meantime,
the Cronbach’s alpha values of the instruments are almost greater 0.9. The figures confirm that
all items significantly measure the same instrument. Therefore, reliability of each instrument is
extremely excellent and all items are remained with 66 variables from nine instruments.
228
Results of Conbach’s Alpha Test (Source: by author)
Var. Item
It
em
-T
o
ta
la
C
o
rr
el
at
io
n
A
lp
h
a
if
i
te
m
d
el
et
ed
Alphab Var. Item
It
em
-T
o
ta
l
C
o
rr
el
at
io
n
A
lp
h
a
if
i
te
m
d
el
et
ed
Alpha
SQ SQ1 .659 .834 .858 USE USE1 .571 .927 .928
SQ2 .604 .840 USE2 .757 .919
SQ3 .584 .843 USE3 .663 .924
SQ4 .515 .852 USE4 .742 .921
SQ5 .638 .837 USE5 .795 .918
SQ6 .643 .837 USE6 .716 .921
SQ7 .644 .836 USE7 .660 .924
SQ8 .580 .846 USE8 .817 .916
IQ IQ1 .639 .903 .906 USE9 .695 .922
IQ2 .706 .895 USE10 .727 .921
IQ3 .785 .882 USE11 .676 .924
IQ4 .845 .873 SAT SAT1 .903 .946 .959
IQ5 .721 .892 SAT2 .910 .944
IQ6 .752 .887 SAT3 .864 .957
PAB PAB1 .662 .957 .959 SAT4 .927 .939
PAB2 .740 .956 AP AP1 .817 .885 .915
PAB3 .770 .956 AP2 .808 .889
PAB4 .768 .956 AP3 .833 .880
PAB5 .688 .957 AP4 .766 .903
PAB6 .798 .955 OP OP1 .704 .910 .918
PAB7 .808 .955 OP2 .793 .902
PAB8 .799 .955 OP3 .743 .906
PAB9 .833 .955 OP4 .682 .911
PAB10 .827 .955 OP5 .748 .905
PAB11 .781 .956 OP6 .737 .906
PAB12 .850 .955 OP7 .754 .906
PAB13 .806 .955 OP8 .697 .910
PAB14 .669 .957 Ex_U U1 .899 .917 .946
PAB15 .688 .957 U2 .894 .918
PAB16 .700 .957 U3 .880 .928
PAB17 .624 .959 ITU ITU1 .744 .881 .899
PAB18 .579 .959 ITU2 .877 .836
ITU3 .869 .837
ITU4 .649 .925
a 0.3=< All item-total correlation <=0.95 indicates Reliability (Hair Jr. et al., 2010)
b All instruments’ alpha >0.7 indicates Reliability (DeVellis, 2016)
229
Appendix 5.7
Sample characteristics consideration
Testing for Normality
Normality is degree to which the distribution of the sample data corresponds to a normal
distribution (Hair Jr. et al., 2010). There are two kinds of normality involving into univariate
normality and multivariate normality. “A variable is multivariate normal, it is then univariate
normal while the reverse is not necessarily true However, multivariate normality is more
difficult to test In most cases, assessing and achieving univariate normality for all variables is
sufficient, multivariate normality is addressed only when it is especially critical” (Hair Jr. et al.,
2010, p. 70).
The shape of any distribution can be described by two measures: kurtosis and skewness. Kurtosis
refers to the “peakedness” or “flatness” of distribution compared with the normal distribution
while skewness is used to describe the balance of the distribution (Hair Jr. et al., 2010, p. 70). If
either zskewness and zkurtosis exceeds +/-1.96 at 0.05 significance level, it can be said that the degree
to which the skewness and peakedness of the distribution vary from the normality distribution
(Hair Jr. et al., 2010).
Kurtosis and Skewness test is calculated on SPSS and shows that almost 66 variables except for
ITU3 and ITU4, which are not main variables of the ESMAP, meet the proposed level (-1.96 <=
either zskewness and zkurtosis <= 1.96), so that they are normally distributed. The results of the
normality test are display in the following table.
230
Results of Normal Distribution Test
Var. Mean
Std.
Dev.
zskewness Zkurtosis Var. Mean
Std.
Dev.
zskewness Zkurtosis
SQ1 5.35 1.120 -0.109 -0.823 USE2 5.41 1.247 -0.713 0.495
SQ2 5.01 1.192 0.014 -0.812 USE3 5.73 0.950 -0.396 -0.182
SQ3 5.84 1.021 -0.881 0.463 USE4 5.50 0.961 0.000 -0.683
SQ4 5.02 1.283 -0.444 -0.030 USE5 5.20 1.192 -0.517 0.487
SQ5 5.43 1.083 -0.573 0.115 USE6 5.32 1.243 -0.732 1.022
SQ6 5.61 1.007 -0.453 -0.285 USE7 5.64 0.977 -0.709 0.304
SQ7 5.14 1.197 -0.278 -0.325 USE8 5.26 1.247 -0.637 0.403
SQ8 4.70 1.430 -0.631 0.314 USE9 5.36 1.106 -0.373 -0.175
IQ1 5.49 0.944 -0.403 -0.909 USE10 5.43 1.018 -0.109 -0.557
IQ2 5.37 1.122 -0.259 -0.616 USE11 4.96 1.387 -0.732 0.415
IQ3 5.30 1.034 -0.307 -0.045 SAT1 5.43 1.010 -0.561 0.386
IQ4 5.41 1.025 -0.510 0.204 SAT2 5.52 1.004 -0.882 1.299
IQ5 5.37 1.004 -0.590 0.228 SAT3 5.58 0.923 -0.420 -0.107
IQ6 5.21 1.092 -0.465 0.308 SAT4 5.58 0.993 -0.630 0.680
PAB1 5.68 1.022 -0.557 -0.241 AP1 3.97 0.709 -0.240 -0.204
PAB2 5.38 1.054 -0.628 0.336 AP2 3.93 0.764 -0.346 -0.190
PAB3 5.65 1.157 -1.002 1.149 AP3 3.91 0.745 -0.347 -0.045
PAB4 5.63 1.045 -0.519 -0.068 AP4 3.99 0.680 -0.153 -0.313
PAB5 5.29 1.233 -0.822 1.181 OP1 3.88 0.832 -0.400 0.108
PAB6 5.65 1.042 -0.748 0.639 OP2 3.98 0.698 -0.279 -0.054
PAB7 5.63 1.070 -0.625 -0.014 OP3 4.01 0.704 -0.305 -0.078
PAB8 5.63 1.020 -0.514 -0.106 OP4 3.95 0.743 -0.544 0.396
PAB9 5.55 1.068 -0.490 -0.328 OP5 3.90 0.703 -0.596 0.788
PAB10 5.59 1.024 -0.680 0.260 OP6 3.96 0.760 -0.513 0.199
PAB11 5.50 1.069 -0.860 0.878 OP7 4.04 0.600 -0.14 -0.165
PAB12 5.50 1.004 -0.532 0.404 OP8 4.18 0.622 -0.143 -0.498
PAB13 5.63 0.978 -0.738 0.991 U1 5.23 1.789 -0.918 -0.213
PAB14 5.80 0.913 -0.601 0.221 U2 5.48 1.577 -1.137 0.620
PAB15 5.53 0.943 -0.256 -0.315 U3 5.42 1.580 -1.027 0.261
PAB16 5.38 0.996 -0.348 -0.195 ITU1 6.18 0.976 -1.243 1.354
PAB17 4.92 1.459 -0.795 0.608 ITU2 6.14 0.938 -1.159 1.198
PAB18 4.78 1.323 -0.322 0.161 ITU3 6.18 0.979 -1.528 3.067
USE1 5.83 1.048 -0.684 -0.154 ITU4 5.92 1.142 -1.418 2.886
* (-1.96 <= either zskewness and zkurtosis <= 1.96) indicates that the data set is normal distribution (Hair Jr. et al., 2010)
Testing for homoscedasticity
“When the variance of the error terms ( e) appears constant over a range of predictor variable,
the data are said to be homoscedastic” (Hair Jr. et al., 2010, p. 34).
Homoscedasticity is evaluated for pairs of variables. To conduct the homoscedasticity test, the
variance of the dependent variable values are compared at each value of the independent
variable. If the variance values are relatively equal, it indicates that the dependence relationship
between the dependent variable and the independent variable exist (Osterlind, Tabachnick, &
231
Fidell, 2001). The result of homoscedasticity is represented through either graphical or statistical
methods. This study applies a graphical method.
The examination of homoscedasticity shows that homoscedasticity of the data set is certain.
Some examples of the homoscedasticity test are presented as follow:
AP1 (independent variable) and OP1 (dependent variable). R2 linear = 0.815. Variables are
homoscedastic.
AP1 (independent variable) and OP8 (dependent variable). R2 linear = 0.759. Variables are
homoscedastic.
232
Testing for linearity
Linearity refers to a consistent slope of change that represents the relationship between the
independent variable and the dependent variable. If a relationship is nonlinear, the statistics,
which assume it is linear, will not only underestimate the strength of the relationship but also
cause inaccurate predictions, especially when the result is generalized beyond the range of the
sample (Osterlind et al., 2001).
The linearity test is achieved through either graphical or statistical method. This study utilizes
the statistical method. The statistical test for linearity is conducted by analyzing the correlation
matrices for the dependent variables and the independent variables. If the correlation coefficient
between an independent variable and a dependent variable is not statistically significant
(p>0.05), the relationship is linear.
This study chooses to test randomly linearity between independent variable AP4 and dependent
variables OP1-OP8. The deviation for linearity test available in the ANOVA test on SPSS
indicates that the significant values for deviation from linearity are greater than 0.05 (see the
following table). Therefore, the assumption of linearity in the current data set is valid.
Table of Test of linearity between independent variable AP4 and dependent variables OP1-OP8
ANOVA Table
Sum of
Squares df Mean Square F Sig.
OP1
*
AP4
Between
Groups
(Combined) 25.653 3 8.551 13.629 .000
Linearity 25.041 1 25.041 39.913 .000
Deviation from Linearity .612 2 .306 .488 .615
Within Groups 82.817 132 .627
Total 108.471 135
OP2
*
AP4
Between
Groups
(Combined) 25.072 3 8.357 19.462 .000
Linearity 23.151 1 23.151 53.910 .000
Deviation from Linearity 1.921 2 .961 2.237 .111
Within Groups 56.685 132 .429
Total 81.757 135
OP3
*
AP4
Between
Groups
(Combined) 26.096 3 8.699 17.095 .000
Linearity 25.790 1 25.790 50.682 .000
Deviation from Linearity .307 2 .153 .302 .740
Within Groups 67.168 132 .509
Total 93.265 135
OP4
*
Between
Groups
(Combined) 24.000 3 8.000 14.554 .000
Linearity 23.066 1 23.066 41.962 .000
233
AP4 Deviation from Linearity .934 2 .467 .850 .430
Within Groups 72.559 132 .550
Total 96.559 135
OP5
*
AP4
Between
Groups
(Combined) 26.009 3 8.670 18.533 .000
Linearity 24.812 1 24.812 53.041 .000
Deviation from Linearity 1.197 2 .599 1.280 .282
Within Groups 61.748 132 .468
Total 87.757 135
OP6
*
AP4
Between
Groups
(Combined) 28.122 3 9.374 17.998 .000
Linearity 27.634 1 27.634 53.055 .000
Deviation from Linearity .488 2 .244 .469 .627
Within Groups 68.753 132 .521
Total 96.875 135
OP7
*
AP4
Between
Groups
(Combined) 23.099 3 7.700 21.247 .000
Linearity 21.712 1 21.712 59.913 .000
Deviation from Linearity 1.387 2 .694 1.914 .152
Within Groups 47.835 132 .362
Total 70.934 135
OP8
*
AP4
Between
Groups
(Combined) 20.404 3 6.801 19.542 .000
Linearity 20.191 1 20.191 58.014 .000
Deviation from Linearity .213 2 .106 .306 .737
Within Groups 45.942 132 .348
Total 66.346 135
234
Appendix 5.8
Process of interpreting the factors
(1) Determine the number of factors through the percentage of variance (% Var) and
Variance (Eigenvalue). The percentage of variance (% Var) indicates the amount of
variance that the factors explain. The variance equals eigenvalue when principal
components analysis is used to extract factors. Hair Jr. et al. (2010) and Fornell and
Larcker (1981) suggest retaining the factors that explain an acceptable level of variance,
which means that the percentage of variance is greater than 60% and eigenvalue is
greater than 1.
(2) Item consideration (Garver & Mentzer, 1999; Hair Jr. et al., 2010):
Retain items with high loadings ( ³0.5)
Consider items with medium loadings (0.4,0.5):
Loading difference ³0.3: keep item
Loading Difference <0.3: delete item
The deletion of items is undertaken with careful consideration. In terms of
statistic aspect, the elimination is reasonable if it makes increase the percentage
of variance of the scale. In terms of conceptualization aspect, the elimination
will be able to lead losing the content validity of the corresponding instrument.
Therefore, an item will be deleted if the remaining items on the scale cover all
the important dimensions of the instrument.
Eliminate items with low loadings ( £0.4)
(3) When deciding to delete any item of an instrument, EFA will be re-run, the process of
interpreting the factors will be repeated.
235
Appendix 5.9
EFA Test Results - Scales without modifications
Items
Factor
loadinga
% Variance
extractedb
Eigen-
valuec
Instrument
structure
Information quality
IQ4
IQ3
IQ6
IQ5
IQ2
IQ1
0.91
0.83
0.80
0.77
0.74
0.67
68.26 4.10 One factor
extractedd
Satisfaction
SAT4
SAT2
SAT1
SAT3
0.96
0.94
0.93
0.88
89.21 3.57 One factor
extracted
Accountant performance
AP3
AP1
AP2
AP4
0.89
0.87
0.86
0.81
79.71 3.19 One factor
extracted
Organizational performance
OP2
OP7
OP5
OP6
OP3
OP1
OP8
OP4
0.83
0.80
0.79
0.78
0.77
0.74
0.73
0.71
64.22 5.14 One factor
extracted
Extent of Use
U1
U2
U3
0.94
0.93
0.91
90.37 2.72 One factor
extracted
Intend to Use 78.23 3.13 One factor
extracted ITU1 0.80
ITU2 0.96
ITU3 0.94
ITU4 0.67
a All item Factor loadings >=0.5 indicates related items are retained (Hair Jr. et al., 2010)
b All % Var. >= 60% indicates the number of factors extracted (Fornell & Larcker, 1981)
c All eigenvalue >1 indicates the number of factors extracted (Fornell & Larcker, 1981)
d All instrument extracting one factor are first-order instruments (Hair Jr. et al., 2010)
236
Appendix 5.10
Summary after Factor Analysis
Construct Factor Items
Number
of items
Cronbach’s
alpha*
System quality (SQ)
System related quality
(SQ_system)
SQ1, SQ2, SQ7 3 0.83
Task related quality
(SQ_task)
SQ5, SQ6, SQ8 3 0.80
Information quality
(IQ)
IQ
IQ1, IQ2, IQ3, IQ4,
IQ5, IQ6
6 0.90
Perceived Accounting
Benefit (PAB)
Operational accounting
benefit
(PAB_operation)
PAB6, PAB7,
PAB8, PAB9,
PAB10
5 0.96
Organizational accounting
benefit
(PAB_organization)
PAB11, PAB12,
PAB13, PAB14,
PAB15, PAB16,
PAB17, PAB18
8 0.91
Use (USE)
Decision support
(USE_decision)
USE1, USE3, USE4,
USE7
4 0.85
Work integration
(USE_work)
USE5, USE6, USE8,
USE11
4 0.89
Satisfaction (SAT) SAT
SAT1, SAT2, SAT3,
SAT4
4 0.95
Accountant
performance (AP)
AP
AP1, AP2, AP3,
AP4
4 0.91
Organizational
performance (OP)
OP
OP1, OP2, OP3,
OP4, OP5, OP6,
OP7, OP8
8 0.92
Extent of Use (Ex_U) Ex_U U1, U2, U3 3 0.95
Intend to Use (ITU) ITU ITU1, ITU2, ITU3, ITU4 4 0.90
* All Cronbach’s alpha >= 0.7 indicate that all factors are reliability (DeVellis, 2016)
237
Appendix 5.11.1
Results of fit consideration of higher-factor instruments – PAB
Amos model fit indicators (Hu & Bentler, 1999)
Indicators Abbr. Best
Construct:
Perceived Accounting Benefit
Model 1 Model 2
First-order
construct
Second-order
construct
Chi-square/df CMIN/DF <3 good;
<5 sometimes permissible
7.452 3.382
p-value for the
model
p-value >0.05
0.00 0.00
Comparative fit
index
CFI >0.95 great
>0.90 traditional
>0.8 sometimes permissible
0.735 0.904
Goodness of Fit
index
GFI >0.95
0.50 0.782
Adjusted
Goodness of Fit
index
AGFI >0.80
0.301 0.691
Root Mean
Square
RMR Close 0
0.143 0.098
Root Mean
Squared error of
approximation
RMSEA <0.05 good
0.05-0.10 moderate
>0.10 bad
0.233 0.141
PCLOSE PCLOSE >0.05 0.00 0.000
238
Appendix 5.11.2
Results of fit consideration of higher-factor instruments - USE
Amos model fit indicators (Hu & Bentler, 1999)
Construct: Use
Indicators Abbr. Best Model 1 Model 2
First-order
construct
Second-order
construct
Chi-square/df CMIN/DF <3 good;
<5 sometimes permissible
7.612 3.277
p-value for the
model
p-value >0.05
0.00 0.00
Comparative fit
index
CFI >0.95 great
>0.90 traditional
>0.8 sometimes permissible
0.784 0.929
Goodness of Fit
index
GFI >0.95
0.711 0.898
Adjusted
Goodness of Fit
index
AGFI >0.80
0.480 0.806
Root Mean
Square
RMR Close 0
0.119 0.085
Root Mean
Squared error of
approximation
RMSEA <0.05 good
0.05-0.10 moderate
>0.10 bad
0.236 0.138
PCLOSE PCLOSE >0.05 0.00 0.02
239
Appendix 5.12
Cross-factor loadings
SQ_t
ask
SQ_sys
tem
IQ
PAB_oga
nizational
PAB_oper
ational
USE_
work
USE_d
ecision
SAT AP OP
SQ5 0.908 0.424 0.634 0.594 0.443 0.291 0.348 0.66 0.397 0.392
SQ6 0.892 0.47 0.686 0.606 0.507 0.32 0.402 0.667 0.425 0.463
SQ8 0.773 0.447 0.586 0.447 0.445 0.243 0.216 0.537 0.234 0.369
SQ2 0.386 0.863 0.503 0.433 0.501 0.413 0.42 0.389 0.346 0.213
SQ1 0.458 0.88 0.465 0.434 0.365 0.372 0.356 0.44 0.27 0.286
SQ7 0.498 0.847 0.527 0.416 0.452 0.179 0.285 0.42 0.343 0.295
IQ4 0.654 0.422 0.902 0.597 0.529 0.36 0.446 0.61 0.465 0.437
IQ3 0.628 0.499 0.857 0.564 0.493 0.375 0.406 0.572 0.428 0.396
IQ6 0.598 0.515 0.838 0.595 0.547 0.349 0.467 0.586 0.451 0.379
IQ5 0.598 0.425 0.81 0.615 0.589 0.283 0.382 0.538 0.376 0.395
IQ2 0.524 0.449 0.792 0.513 0.459 0.241 0.41 0.481 0.306 0.344
IQ1 0.654 0.544 0.748 0.555 0.524 0.331 0.46 0.563 0.396 0.407
PAB15 0.47 0.334 0.456 0.83 0.536 0.46 0.602 0.597 0.446 0.557
PAB16 0.567 0.272 0.544 0.799 0.534 0.522 0.494 0.653 0.512 0.54
PAB17 0.477 0.368 0.523 0.762 0.486 0.462 0.444 0.527 0.517 0.575
PAB14 0.473 0.376 0.493 0.781 0.538 0.284 0.593 0.574 0.512 0.495
PAB12 0.565 0.565 0.648 0.891 0.733 0.451 0.634 0.688 0.492 0.502
PAB13 0.539 0.468 0.645 0.867 0.695 0.374 0.605 0.68 0.484 0.526
PAB11 0.528 0.443 0.586 0.838 0.67 0.61 0.62 0.636 0.503 0.561
PAB18 0.515 0.309 0.566 0.637 0.477 0.437 0.446 0.516 0.456 0.538
PAB7 0.476 0.412 0.57 0.662 0.956 0.345 0.467 0.582 0.49 0.416
PAB6 0.48 0.445 0.566 0.646 0.951 0.356 0.444 0.54 0.474 0.388
PAB8 0.477 0.399 0.571 0.638 0.95 0.321 0.431 0.523 0.435 0.44
PAB9 0.492 0.498 0.573 0.718 0.929 0.434 0.554 0.644 0.585 0.465
PAB10 0.586 0.596 0.664 0.742 0.857 0.507 0.562 0.618 0.515 0.448
USE11 0.223 0.332 0.3 0.411 0.265 0.869 0.432 0.352 0.313 0.275
USE8 0.326 0.338 0.398 0.534 0.445 0.918 0.585 0.463 0.467 0.354
USE5 0.302 0.281 0.385 0.507 0.409 0.905 0.576 0.462 0.481 0.384
USE6 0.308 0.347 0.293 0.497 0.353 0.81 0.625 0.469 0.36 0.335
USE3 0.316 0.38 0.443 0.529 0.454 0.51 0.851 0.455 0.472 0.338
USE4 0.312 0.368 0.393 0.604 0.424 0.628 0.875 0.481 0.459 0.429
USE1 0.404 0.34 0.496 0.64 0.49 0.414 0.785 0.424 0.385 0.432
USE7 0.242 0.269 0.415 0.549 0.408 0.545 0.813 0.494 0.53 0.442
SAT1 0.704 0.463 0.65 0.708 0.552 0.488 0.507 0.945 0.634 0.651
SAT2 0.679 0.465 0.648 0.752 0.594 0.509 0.562 0.951 0.688 0.625
SAT3 0.667 0.429 0.635 0.73 0.639 0.453 0.537 0.922 0.586 0.586
SAT4 0.692 0.467 0.633 0.684 0.586 0.441 0.502 0.959 0.672 0.621
AP1 0.332 0.326 0.424 0.574 0.514 0.436 0.509 0.595 0.896 0.532
AP2 0.296 0.267 0.368 0.464 0.427 0.359 0.49 0.557 0.887 0.505
AP3 0.478 0.35 0.508 0.573 0.46 0.437 0.483 0.693 0.917 0.643
240
AP4 0.353 0.368 0.448 0.549 0.525 0.428 0.508 0.583 0.87 0.562
OP2 0.466 0.267 0.437 0.633 0.489 0.275 0.41 0.584 0.507 0.846
OP7 0.444 0.235 0.408 0.532 0.306 0.288 0.402 0.605 0.574 0.834
OP5 0.309 0.199 0.274 0.449 0.324 0.286 0.317 0.531 0.532 0.817
OP6 0.345 0.174 0.385 0.474 0.279 0.362 0.381 0.53 0.551 0.818
OP3 0.337 0.284 0.382 0.537 0.45 0.363 0.408 0.495 0.452 0.794
OP1 0.433 0.321 0.508 0.527 0.441 0.269 0.388 0.507 0.453 0.77
OP8 0.388 0.209 0.384 0.604 0.346 0.298 0.49 0.466 0.519 0.777
OP4 0.325 0.322 0.289 0.505 0.388 0.347 0.359 0.48 0.435 0.747
Appendix 5.13
Internal consistency and convergent validity results of the first order factor
“PAB_operational” after eliminating indicator PAB18
PAB_operational with
PAB18 (the original
measurement model)
PAB_operational
without PAB18
Composite Reliability 0.935 0.939
AVE 0.646 0.688
241
Appendix 5.14
Decision-making process for keeping or deleting formative indicators basing
on outer weight and outer loading Hair Jr et al. (2014)
242
Appendix 5.15
Results of alternative models analysis
Table 1: Direct relationships for Hypothesis testing (alternative model related to Extent of Use)
Hypothesis Relationship Std Beta Std Error [t - value]^ Decision
H1: AP -> OP 0.6379 0.0667 9.4765*** Supported
H2: Ex_U -> AP 0.0636 0.093 0.6202 No
H3: SAT -> AP 0.6503 0.0643 10.2516*** Supported
H4a: SQ -> Ex_U 0.4461 0.1483 3.0417** Supported
H4b: SQ -> SAT 0.2909 0.0899 3.2691** Supported
H5a: IQ -> Ex_U -0.1327 0.1622 0.8722 No
H5b: IQ -> SAT 0.124 0.0979 1.2363 No
H6a: PAB -> Ex_U 0.0343 0.1626 0.2278 No
H6b: PAB -> SAT 0.4674 0.1012 4.6101*** Supported
*p<0.05, **p<0.01, ***p<0.001
Table 2: Direct relationships for Hypothesis testing (alternative model related to Extended Use)
H: Relationship Std Beta Std Error [t - value]^ Decision
H1: AP -> OP 0.6369 0.0656 9.6359*** Supported
H2: Extended_U -> AP 0.2008 0.08 2.3949* Supported
H3: SAT -> AP 0.5533 0.0775 7.2462*** Supported
H4a: SQ -> Extended_U 0.082 0.1085 0.822 No
H4b: SQ -> SAT 0.2906 0.0874 3.3731*** Supported
H5a: IQ -> Extended_U 0.0764 0.1089 0.5993 No
H5b: IQ -> SAT 0.1285 0.1024 1.1609 No
H6a: PAB -> Extended_U 0.4787 0.1081 4.4775*** Supported
H6b: PAB -> SAT 0.4644 0.101 4.6328*** Supported
*p<0.05, **p<0.01, ***p<0.001
243
Figure 1: Hypotheses Testing: Bootstrapping Direct Effect Results (alternative model related to Extent of Use)
244
Figure 5.8: Hypotheses Testing: Bootstrapping Direct Effect Results (alternative model related to Extended Use
245
Appendix 6.1
Demographic characteristics of surveyed companies
Frequency %
Type of ownership
100% foreign-owned enterprises 23 19.2
State-owned enterprises (>=51% government capital) 22 18.3
Private enterprises/ limited enterprises 54 45
Joint venture with foreign partners 14 11.7
Joint venture with domestic partners 7 5.8
Total 120 100.0
Type of industry sectors
Manufacture 72 60.0
Commerce 44 36.7
Services 42 35.0
Total 120
Type of industry
Bank, insurance, investment 2 1.7
Chemical & Pharmaceuticals 3 2.5
Dairy, food & meat products 28 23.3
Electrical & Electronics 7 5.8
Medical & healthcare 8 6.8
Information technology (IT) 10 8.3
Manufacturing 12 10.0
Retail/ Wholesale/ Distribution 25 20.8
Telecommunications 3 2.5
Transportation, logistics & courier 7 5.8
Construction 6 5.0
Others (beverages, fashion, design, FMCG,) 9 7.5
Total 120 100.0
Company size (paid-in capital) in VND billion
< 10 3 2.5
10 – 50 6 5.0
>50 – 100 11 9.2
>100 – 200 12 10.0
>200 – 500 14 11.7
>500 – 1000 22 18.3
>1000 52 43.3
Total 120 100.0
Company size (number of employees)
<=50 8 6.7
50 – 200 13 10.8
201 – 500 29 24.2
501 – 1000 23 19.2
1001 – 5000 32 26.7
5001 – 10000 9 7.5
>10000 6 5.0
Total 120 100.0
(Source: by author)
246
Appendix 6.2
Demography characteristics of ERP system
Frequency %
Type of ERP software
Oracle 20 16.7
SAP 43 35.8
XMAN (ERP) 2 1.7
SalesUp ERP 2 1.7
Navision 3 2.5
Microsoft Dynamic 4 3.3
Lemon 3 2.5
FAST (ERP) 3 2.5
Others (AMIS – MISA, Bamboo, Bravo, Bross, IMAS,
Maconomy, MMIS, Peoplesoft, PERP)
40 33.3
Total 120 100.0
Years that ERP is implemented and used at current company
<1 year 0 0.0
1- 2 years 21 17.5
>2 – 4 years 16 13.3
>4 – 6 years 37 30.8
>6 – 8 years 13 10.8
>8 years 33 27.5
Total 120 100.0
(Source: by author)
247
Appendix 6.3
Demographic characteristics of informants
Frequency % Min Max Mean
Position in the firm (job title)
Chief Finance Officer CFO 15 12.5
Chief accountant 39 32.5
Internal controller 45 37.5
Internal auditor 15 12.5
Management accountant 6 5.0
Total 120 100.0
Position in organization’s hierarchy
Top management position 27 22.5
Mid-level personnel 51 42.5
Senior staff 39 32.5
Staff 3 2.5
Total 120 100.0
Gender
Female 63 52.5
Male 57 47.5
Total 120 100.0
Education background
College degree 0 0.0
University bachelor degree 101 84.2
Master degree 19 15.8
Total 120 100.0
Age
< 25 3 2.5
25 – 34 66 55.0
35 – 44 51 42.5
>44 0 00.0
Total 120 100.0
Experience
Years at current position (years) 1 20 6.5
Years using ERP at current position (years) 1 5 2.7
Extent of using ERP system (the degree users perceive the following statements
according to 7-likert scale ranging from strongly disagree to strongly agree)
Many hours per day at work 1 7 5.2
Many times per day at work 1 7 5.5
Overall, use ERP a lot 1 7 5.4
Intend to continuously use (the degree users perceive the following statements
according to 7-likert scale ranging from strongly disagree to strongly agree)
We intend to continue using the ERP in our job 3 7 6.2
We intend to use more functions of the ERP 3 7 6.1
We intend to continue using the ERP for processing more tasks 2 7 6.2
We intend to suggest that our company should continue to use the
current ERP system
1 7 5.9
(Source: by author)
248
Appendix 6.4
Comparing sample means
The Independent Samples T test is generally used for comparing sample means to indicate
whether there is sufficient evidence to infer that the means of the two sample distributions
significantly differ from each other. The two samples are measured on common some
variables of interest but there is no overlap of memberships between two groups. These
variables are the length of ERP adoption, organizational size and utilized ERP packages.
Based on Trọng and Ngọc (2008), if the significance level of Levene’s Test is less than 0.05,
the significance level of T-test is the value of hypothesis “Equal variances assumed”, in
contrast, if the significant level of Levene’s Test is greater than 0.05, the significance level of
T-test is the value of hypothesis “Equal variances not assumed”. And the significance level of
T-test must be below 0.05 to assert that there exists the difference to population between two
groups.
Accordingly, in terms of p-values greater than 0.05, there are not enough proofs to prove that
there exist the differences between each group set (see Table 1, 2 and 3).
Table 1: Results of the Independent Samples T-test by the length of ERP adoption
Levene's Test
for Equality of
Variances t-test for Equality of Means
F Sig. t df Sig. (2-tailed) Mean Difference
SQ Equal variances
assumed
.094 .760 -1.565 118 .120 -.32780
Equal variances
not assumed
-1.533 28.514 .136 -.32780
IQ Equal variances
assumed
3.433 .066 .048 118 .962 .00986
Equal variances
not assumed
.039 24.545 .970 .00986
PAB Equal variances
assumed
1.782 .184 -1.070 118 .287 -.21645
Equal variances
not assumed
-.907 25.344 .373 -.21645
USE Equal variances
assumed
.991 .322 -1.011 118 .314 -.21122
Equal variances
not assumed
-.899 26.242 .377 -.21122
249
Table 2: Results of the Independent Samples T-test by the organizational size
Levene's Test
for Equality of
Variances t-test for Equality of Means
F Sig. t df Sig. (2-tailed) Mean Difference
SQ Equal variances
assumed
1.549 .216 -.503 118 .616 -.10833
Equal variances
not assumed
-.554 30.120 .583 -.10833
IQ Equal variances
assumed
.571 .451 -.230 118 .819 -.04833
Equal variances
not assumed
-.241 28.532 .811 -.04833
PAB Equal variances
assumed
.030 .864 -1.089 118 .278 -.22462
Equal variances
not assumed
-1.050 26.259 .303 -.22462
USE Equal variances
assumed
2.699 .103 -.240 118 .811 -.05125
Equal variances
not assumed
-.207 24.116 .838 -.05125
SAT Equal variances
assumed
.082 .775 -.131 118 .896 -.03000
Equal variances
not assumed
-.140 29.015 .889 -.03000
AP Equal variances
assumed
.111 .740 .661 118 .510 .10500
Equal variances
not assumed
.690 28.358 .496 .10500
OP Equal variances
assumed
.987 .322 .964 118 .337 .13375
Equal variances
not assumed
1.097 31.339 .281 .13375
SAT Equal variances
assumed
.474 .493 -.717 118 .475 -.16017
Equal variances
not assumed
-.656 26.844 .518 -.16017
AP Equal variances
assumed
15.533 .000 .481 118 .631 .07504
Equal variances
not assumed
.361 23.541 .721 .07504
OP Equal variances
assumed
.698 .405 -.639 118 .524 -.08712
Equal variances
not assumed
-.621 28.336 .540 -.08712
250
Table 3: Results of the Independent Samples T-test by the adopted ERP packages
Levene's
Test for
Equality of
Variances t-test for Equality of Means
F Sig. t df Sig. (2-tailed) Mean Difference
SQ Equal variances
assumed
.000 .997 .165 118 .869 .02659
Equal variances
not assumed
.165 114.399 .870 .02659
IQ Equal variances
assumed
.075 .785 -.281 118 .779 -.04414
Equal variances
not assumed
-.280 114.036 .780 -.04414
PAB Equal variances
assumed
.196 .658 1.575 118 .118 .24099
Equal variances
not assumed
1.568 114.151 .120 .24099
USE Equal variances
assumed
1.260 .264 .083 118 .934 .01326
Equal variances
not assumed
.082 109.653 .935 .01326
SAT Equal variances
assumed
.476 .491 .624 118 .534 .10610
Equal variances
not assumed
.619 111.277 .537 .10610
AP Equal variances
assumed
.441 .508 -.239 118 .811 -.02840
Equal variances
not assumed
-.238 113.786 .812 -.02840
OP Equal variances
assumed
1.360 .246 .837 118 .404 .08678
Equal variances
not assumed
.828 107.842 .409 .08678
251
Appendix 6.5
Testing the D&M IS Success model (1992) without PAB
Results of direct effects in the D&M IS success model (1992)
H Relationship Std Beta Std Error [t - value]^ Decision
95% CI
LL
95% CI
UL
H1: AP -> OP 0.6416 0.0613 10.3101*** Supported 0.4982 0.7124
H2: USE -> AP 0.2506 0.0715 3.5106*** Supported 0.124 0.3555
H3: SAT -> AP 0.536 0.0657 8.1686*** Supported 0.426 0.6462
H4a: SQ -> USE 0.2052 0.1193 1.7689 No 0.0039 0.3971
H4b: SQ -> SAT 0.4277 0.0849 5.0957*** Supported 0.2896 0.5708
H5a: IQ -> USE 0.3485 0.1186 2.9128** Supported 0.1428 0.532
H5b: IQ -> SAT 0.3575 0.0907 3.8697*** Supported 0.1939 0.4889
(Source: by author)
252
Appendix 7.1
Practical implications for ERP adoption organizatons’ stakeholders
7.3.2.1 For ERP vendors:
The difference on the relationship between perceived accounting benefit and satisfaction by
the ERP vendors recorded significantly indicates that at originations employing popular ERP
packages such as SAP and Oracle, satisfaction of accounting professionals is increasingly
higher when they perceive accounting-benefits that the ERP system brings for their work.
This finding is meaningful for popular ERP vendors (SAP and Oracle). They understand their
products better; determine their tools’ impacts when implementing them in practice; then
provide valuable experiences for their customers in duration of ERP implementation and post-
implementation. The more advantaged their customers receive from their products, the higher
their reputation is.
7.3.2.2 For ERP-adopting organizations
The ESMAP allows maximize the ERP’s impacts on accounting professionals, which in turn
increasingly enhance the ERP’s impacts on organizations. To put it differently, organizations
will obtain beneficial outcome when individuals (particular, accountant professional) use
successfully the ERP system to add more value to organization. Therefore, the ERP-adopting
organizations receive indirect benefits from the ESMAP.
7.3.2.3 For accounting professionals
The ERP system is still company’s backbone for the planning, control, and execution of all
business processes20, even in revolution of industry 4.0. However, the role of accounting
professions, especially CFOs and senior controllers, has dramatically changed. They are no
longer seen as passive manager, but partners actively involved in creating business strategies.
The ESMAP guides them how to be come productive under ERP settings. In short, the
ESMAP supports them to adjust their ERP adopting behavior in order to improve their work
performance, and via this, enhance organizational performance.
20 According to Deloitte’s report “Industry 4.0: is your ERP system ready for the digital era?” quoted from
website:
https://www2.deloitte.com/content/dam/Deloitte/de/Documents/technology/Deloitte_ERP_Industrie-
4-0_Whitepaper.pdf
253
7.3.2.4 For organization management
The results of this study support organization management to increasingly acknowledge the
power of the ERP system for its usage of senior accounting experts. In other words, the study
provides guidance on how to guide accountant professionals to effectively adopt the ERP
system in organization management’s efforts to improve organizational performance. Besides,
it allows management to better manage, control accounting experts and their work in ERP
adoption and ongoing context. For example, organizational management should recognize
that a high level of system quality and perceived accounting benefit are likely to result in
improved organizational performance via increased effective use, satisfaction and accountant
performance.
More specifically, given discussions on findings in Chapter 6, which is modeled in Figure 7.1,
the author identifies practical implications, summarizes and categories them into four
instruction sets for organization management, namely:
Black arrow line: direct effect
Red arrow line: indirect effect
Blue line: the relationship has difference by length of ERP system or by
firm size or by ERP vendor
Figure 7.1: The supported relationships useful for organization management
A set of instructions on advancing organizational performance
The following solutions are ordered in descend manner of impact extent of each construct on
organizational performance.
First, organization management should focus on finding how to improve accountant
performance, since it is not only a direct driver, also a mediator controlling the impacts of use
and satisfaction on organizational performance, and also a mediator managing the impacts of
perceived accounting benefit on organizational performance. More specifically, management
should frequently organize seminars for accounting department, or thoroughly communicate
254
to accounting-professionals on ERP system’s advantages for their work such as “they can
learn much through the presence of the ERP system” (AP1), “the ERP system helps them to
more enhance their awareness and recall of job-related information” (AP2), “the ERP system
also helps them to more enhance their effectiveness in their job” (AP3), and “the ERP system
allows them more increasing their productivity” (AP4). Noting that, large-sized companies’
management need to more focus on raising these perceptions of accounting professionals.
Analogous to small-and-medium-sized firms, the higher accounting department perceives the
ERP impacts on them; the more massive their organization performance is enhanced.
Second, management should pay more attention on accounting department’s satisfaction of
the ERP system, as it offers both direct and indirect impacts on organizational performance.
In a little more detail, management needs to find out how to make accountant professional
more satisfy of system quality (SAT1), information quality (SAT2), perceived accounting
benefit (SAT3) and overall ERP system (SAT4).
Third, the most different and dominant contribution of the ESMAP, management needs to
find out how to improve accounting department’s perceptions of accounting-related benefits
received from the ERP system as these perceptions influence directly and indirectly
organizational performance. These perceptions include “the ERP system makes reduction of
time for closure of monthly account” (PAB6), “the ERP system makes reduction of time for
closure of quarterly account” (PAB7), “the ERP system makes reduction of time for closure
of annual accounts” (PAB8), “the ERP system makes reduction of time for issuing of
financial statements” (PAB9), “the ERP system increases flexibility in information
generation” (PAB10), “the ERP system increase integration of accounting applications”
(PAB11), “the ERP system improves decisions based on timely and reliable information”
(PAB12, “the ERP system improve quality of reports- statements of account” (PAB13), “the
ERP system improves internal audit function” (PAB14), “the ERP system improve working
capital control” (PAB15), “the ERP system increase use of financial ratio analysis” (PAB16),
“the ERP system make reduction of time for issuing payroll” (PAB17), “the ERP system
makes reduction of personnel of accounting department” (PAB18). Overall, the more
clarified the accountant professional perceive accounting-benefits obtained from the ERP
system, the higher the organizational performance is. Simultaneously, the more clarified the
accountant professional perceive accounting-related benefits received from the ERP system,
the more effective they use the ERP system (when they are master of it), and in the
meantime, the more satisfied they feel for the ERP system, then the more productive they are,
and finally, the more gainful their organizations offer.
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Fourth, management should concern about how to improve effective use because it has nearly
full indirect impact on organizational performance via accountant performance. Based on the
measures of effective use, the author suggests that making accounting professionals clarify
how effectively the ERP system used for their decision support and work integration is
appropriate solution to improve effective use. For example, effectiveness of using the ERP
system means that using it enables them to improve the efficiency of the decision process
(USE1), manage their work (USE3), make explicit the reasons for their decision (USE4),
make sense out of data (USE7), communicate with people in other work groups (USE5),
monitor their own performance (USE6), communicate with people in other departments
(USE8), and keep people in other department informed (USE11).
A set of instructions on enhancing accountant performance
The following solutions are ordered in descend manner of impact extent of each construct on
accountant performance.
First, management should find out how to improve the satisfaction of accounting
professionals, which is more necessary in large-sized companies. The more satisfied
accounting professionals are with ERP system, the more strongly they agree that the ERP
system supports them to perform well their routine work.
Second, a different way to enhance accountant performance relates to increasing how
effectively when using the ERP, especially in large-sized organizations. To put it differently,
this study provides a strong proof for management to convince their inexperience and
unconfident accounting professionals that the more extensively the ERP system is used for
decision support and work integration, the more likely these experts will perceive that ERP
system has considerably positive impacts on their work.
Third, PAB has not direct impact on accountant performance, however PAB has indirect
impact on accountant performance via use and satisfaction (parallel mediation model).
Therefore, to advance accountant performance, management not only focuses on making use
and satisfaction increased, also concentrate on making PAB improved.
An instruction on improving effective use
According to the ESMAP, to make accountant professionals use the EPR system more
effectively, the only way that the management should conduct is to attempt to find how to
increase PAB, as the more increasingly they perceive accounting-related benefits from the
ERP system, the more effectively they use the ERP system.
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A set of instructions on increasing accounting professionals’ satisfaction
The following solutions are ordered in descend manner of impact extent of each construct on
satisfaction
First, organization management should find how to improve system quality, which in turn,
absolutely, enhance satisfaction. For instance, information such as “the ERP system is easy to
use” (SQ1), “the ERP system is easy to learn” (SQ2), “the ERP user interface can be easily
adapted” (SQ7), “the ERP system meets users requirements” (SQ5), “the ERP system
includes necessary features and functions for users job” (SQ6), and “the ERP can be easily
modified or improved according to users needs” (SQ8) should be communicated commonly to
accounting department. The earlier the accountant professionals realize system quality, the
more satisfied they feel about the ERP system.
Second, another solution of increasing satisfaction is making accountant professionals
perceive accounting-related benefits, which the ERP system offers, more earlier and clarified.
This is more meaningful in large-sized organizations or in firms employing popular ERP
packages such as SAP and Oracle.