Proceedings

To analyse the data, Structural Equation Modelling (SEM) technique has been
employed as the main tool, and it has been run by a computer programme called
Amos, version 17. SEM is a systematic approach that employed for test of models fit
by doing factor analysis and linear regression at the same time (Williams et al., 2009).
This technique could take the measures directly from questionnaire as indicators or
observed variables to estimate the relevant concepts or latent variables (Hoyle, 1995).
By using this technique combination of moderating and intervening models can also
be tested and some changes in initial model would be possible.
4. PRELIMINARY RESULTS
275 completed questionnaires were collected from the universities (72.8 percent
response rate), but only 262 of them were fully completed without any missing data,
as it is necessary for SEM, then the real response rate is 69.3 percent. Finally by
screening the data and in order to gain an acceptable level of normality of distribution
16 questionnaires were set aside as outliers, so final response rate is 65.1 percent. The
brief results of descriptive analysis have been shown in the table 1 as below.
Table 1 Descriptive statistic results of the data, using SPSS 17.
Variables Min* Max* Mean Std.
Dev.
Skew. Kurt.
No. of
Items
Cronbach’s
Alpha
Competitive position 2 6 4.21 0.74 -.200 -.415 3 .74
Financial pressure 2 6 4.58 0.64 -.302 -.464 4 .77
Decentralization 1 6 3.19 0.84 .094 -.852 7 .91
Improved
accounting system
2 6 4.26 0.75 .170 -.757 11 .87
Participative budgeting 1 6 3.01 0.94 .100 .291 6 .88
More emphasis on budget
control
2 6 4.45 0.83 -.508 .141 3 .85
Competitive advantage 1 5 2.25 0.96 .455 -.645 4 .95
Satisfaction with budgets 1 6 2.95 0.88 .146 -.093 4 .87
Comprehensive
performance measures
1 6 4.38 0.87 -.445 .208 7 .91
Appropriate
reward system
1 6 3.21 0.96 .477 .575 7 .88
Usage of accounting
information in PM
1 6 3.16 1.03 .264 -.336 4 .91
Departmental performance 1 6 3.45 0.72 .133 -.046 6 .76
* Theoretical Min and Max are 1 and 6 respectively.
To analyse the data by SEM, two phases should be performed , namely measurement
model and structural model (Williams et al., 2009). By running the first phase which
is actually a kind of confirmatory factor analysis the reliability of indicators has been
tested and confirmed, of course to gain a better model some of the indicators have
been dropped out of the model by the indication of the initial results (Shook et al.,
2004). After running the two proposed models by SEM, based on the outcomes of the
first phase, the indices of “Model Fit” showed that the collected data are fit to the
models.
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