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CHAPTER 4
DATA ANALYSIS AND RESULTS
This chapter presents the results of the data analysis. First, the
characteristics of the demographic profile of the respondent and the
descriptive profile of the investigated variables are presented. Since the data
analysis is done in AMOS, the results are presented in three stages. First the
measurement model of the latent variables are analysed by a confirmative
factor analysis, and the constructs are verified for the reliability and validity.
The second part presents the results of the inferential statistics of the basic
structural model. In the third part test for the preconditions of mediation
analysis and the effect of multiple mediation is done in the AMOS using the
direct, indirect and total effect. A bootstrapping test is done to test the
significance of indirect effect. The significant factors are identified and the
subsequent validation of the model is done. Further, the hypotheses are
verified.
4.1 DATA DESCRIPTION
Descriptive statistical measures are used to depict the data and in
addition to test the normality. The mean, standard deviation (SD), kurtosis
and skewness are used as preliminary tools for this purpose. It is important to
check the normality of the quantitative outcome variable as to not only to
present the appropriate descriptive statistics but also to apply the correct
statistical tests. A popular and useful measure of spread is the standard
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deviation, which tells us how much the scores in a dataset, cluster around the
mean. A large SD is indicative of a more varied data scores.
Skewness is a measure of symmetry, or more precisely, the lack
of symmetry. A distribution, or data set, is symmetric, if it looks the same to
the left and the right of the centre point. There are three types of skewness
(right: skew > 0, normal: skew ~ 0 and left: skew < 0). Skewness ranges
from -3 to 3. Acceptable range for normality is skewness lying between -1 to
1. Normality should not be based on skewness alone; Kurtosis is a measure
of whether the data are peaked or flat relative to a normal distribution. That
is, data sets with high kurtosis tend to have a distinct peak near the mean,
decline rather rapidly, and have heavy tails. Data sets with low kurtosis tend
to have a flat top near the mean rather than a sharp peak. A uniform
distribution would be the extreme case. Like skewness, acceptable range for
normality is kurtosis lying between -1 to 1 (Joanes and Gill 1998). For the
variables with the nominal scale, the frequency table is presented. For the
interval scales, the mean, standard deviation, kurtosis and skewness are
presented.
4.1.1 Demography of Responding Companies
The demography of the responding companies show that most of
them (38.9%) had number of employees between 51 and 250 and suppliers
(40.7%), customers (38.9%) in the range of 11 to 20. The type of ownership
of a larger portion (55.3%) of the respondents is proprietorship. 72.12% had
an international market and 15.5% had a distributed office or unit. 91.6 % of
the respondents are small and medium size enterprises (Table 4.1).
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Table 4.1 Demographics of the responding companies
Variable Scale Frequency (N)
Total = 226 Percentage (%)
Number of Employees
1 - 10 22 9.7 11 - 50 83 36.7
51 - 250 88 38.9 251 - 500 20 8.8
501 - 1000 9 4.0 >1000 4 1.8
Organisation Size Small 104 46.0 Medium 103 45.6 Large 19 8.4
Market Area International 163 72.12 Domestic 53 23.45 Both 10 4.43
Distributed Office/Branch/Unit 35 15.5
Number of Product Line
1 - 2 84 37.2 3 - 4 71 31.4 5 - 6 44 19.5
7 - 10 18 8.0 >10 9 4.0
Type of Ownership
Proprietor 125 55.3 Partnership 81 35.8 Private Ltd 18 8.0 Public Ltd 1 0.4 Joint Venture 1 0.4
No. of Suppliers
<10 45 19.9 11 - 20 92 40.7 21 - 50 68 30.1
51 - 100 11 4.9 >100 10 4.4
No. of Customers
<10 87 38.5 11 - 20 88 38.9 21 - 50 43 19.0
51 - 100 3 1.3 >100 5 2.2
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4.1.2 Functional Characteristics of the Respondent Firms
To examine the business and organisational characteristics of
responding firms Table 4.2, presents the various activities carried out within
the firm and the level of integration.
Table 4.2 Functional characteristics of the respondent firms
Variable Functions / Process N %
Companies
activities
carried out
internally
Inbound Logistics 26/226 11.5
Outbound Logistics 28/226 12.4
Production 225/226 99.6
Marketing 87/226 38.5
Sales 40//226 17.7
R & D 26/226 11.5
HR Management 40/226 17.7
IS Management 37/226 16.4
Administration/Finance/Quality Mgmt 78/226 34.5
Type of
manufacturing
that are
integrated
Spinning 11/226 4.9
Weaving 8/226 3.5
Knitting 28/226 12.4
Processing 19/226 8.4
Printing 56/226 24.8
Embroidery 45/226 19.9
Garmenting 221/226 97.8
Made-ups 22/226 9.7
Most of the companies are knitwear garment manufacturing
having an in-house production facility (99.65%); around 38% of the
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respondent firms have marketing activity that focus on acquiring new
buyers. Only 34.5% of the companies have Administration / Finance /
Quality Management systems. This shows that inspite of the size of the firm,
these activities are performed by the owner-manager or some are outsourced.
Other activities like inbound logistics, outbound logistics, R&D, HR
management, sales and IS management are spread around only to an extent
of 11 to 18 percent. On type of integration the companies have, printing
(24.8%) and embroidery (19.9%) are the main processes integrated with the
garmenting. Some companies (12.4%) have knitting being integrated.
Weaving (3.5%), Spinning (4.5%) and Made-ups (9.7%) are found only with
a very few companies. Table 4.3 presents the ERP adoption status of the
responding firms. Nearly 68.1% of the firms have not adopted ERP. Only
13.3% of the firms have adopted already and are using it. Around 10.6% of
the firms are in the process of completion whereas, rest of the firms (8%) are
in the process of implementation.
Table 4.3 Status of ERP adoption
ERP Adoption Status N % Cumulative %
Already in use for more than 2 Years 30 13.30 13.30
Completed Implementation 24 10.60 23.90
Configuration and implementing 18 8.00 31.90
Planning first project 31 13.70 45.60
Considering some modules 28 12.40 58.00
Consider in future 61 27.00 85.00
No intention to adopt 34 15.00 100.00
Total 226 100.00
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4.1.3 Case Summary
Tables 4.4 to 4.6 present the case summaries of the constructs
institutional isomorphic pressures, perceived benefits and perceived
challenges.
4.1.3.1 Case summaries for institutional isomorphic pressures
variables
On a scale of five the mid value being three, the mean values of
the variables except Firm's Customer Require ERP , Firm's Customer
Adopted ERP and Follow Recent Trend can be observed to be lesser than
three (Table 4.4). Government requires (2.093) and Firm's Supplier
Require ERP (2.310) have the least mean value. Follow Recent Trend
(3.146) and Firm's Customer Require ERP (3.089) have the highest mean
score. Influenced by media (0.946) has the highest standard deviation
followed by Industry perceives favourably (0.913) and Trade Association
Encourages (0.913). This shows a varying degree of perception on the
influence by the media and a favourable recognition of the industry.
However, Follow recent trend (0.589) and
supplier (0.499) have the least standard deviation. This shows that the
perceptions of these variables were commonly felt at a similar degree.
Skewness of all the variables is between -1 and +1 indicating a symmetrical
shape of distribution. Kurtosis is also between -1 to +1 for all variables
except Follow recent trend and .
However, they are less than 2. Some authors accept skewness and kurtosis
values between -2 and +2 (George and Mallery 2009). Therefore, the
variables can be included for further analysis. The results show that the
variables of the institutional isomorphic pressures are normally distributed.
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Table 4.4 Case summaries for institutional isomorphic pressures
variables
Items N = 226
Mean SD Variance Kurtosis Skewness
Required for Competitiveness 2.686 .768 .590 -.335 .189
Industry perceive favourably 2.566 .913 .833 -.321 -.057
Main Competitors Benefited 2.664 .779 .606 -.300 .496
Customers 2.717 .771 .595 -.685 .419
Government Requires 2.093 .677 .458 -.808 -.114
Government Promotion 2.425 .697 .485 -.576 -.802
Influenced by Media 2.566 .946 .896 -.452 .299
Influence by Consultants and Experts 2.575 .852 .725 -.295 .242
Trade Association Encourages 2.615 .913 .833 -.317 .203
Firm's Customer Require ERP 3.089 .778 .605 .815 -.270
Firm's Customer Adopted ERP 3.040 .906 .821 -.359 .138
Follow Recent Trend 3.146 .589 .347 1.629 .351
Perception of Competitor's Supplier 2.801 .499 .249 1.227 -.784
Firm's Supplier Require ERP 2.310 .668 .446 .916 .995
Firm's Supplier Adopted ERP 2.420 .683 .467 .106 .671
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4.1.3.2 Case summaries for perceived benefits variables
Table 4.5 presents the case summary of items measuring the
perceived benefits. The variables were measured on a scale of 1 to 5 with a
mid value of three. The mean value of all the variables is above three
indicating a positive perception on the benefits of ERP by the respondents.
Reduces wastages (3.78), Improves customer service (3.77) and
Eliminate redundant data (3.67) are the highly perceived benefits.
Builds common vision (3.14), Empower process owners (3.14) and
nhance business alliance (3.15) are benefits that are perceived to be less
important for this industry. The standard deviation and the variance are
below one for all variables except Better control of resources . To check the
normality of the measured data, the skewness and kurtosis were analysed.
Both values for each of the measures were between -1 and +1 indicating a
normal distribution.
Table 4.5 Case summaries for perceived benefit variables
Items N = 226
Mean SD Variance Kurtosis Skewness
Reduces operational cost 3.429 .974 .948 -.505 -.396
Improves productivity 3.314 .963 .928 .096 -.455
Reduces business cycle 3.438 .863 .745 .170 -.560
Improves quality 3.208 .946 .894 -.037 -.586
Improves customer services 3.770 .933 .871 -.273 -.485
Reduces wastages 3.778 .931 .866 -.374 -.510
Integrates operations 3.589 .774 .599 .050 -.299 Enables process re-engineering 3.283 .777 .604 .145 -.197
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Table 4.5 (Continued)
Items Mean SD Variance Kurtosis Skewness Improves process efficiency 3.369 .828 .686 .348 -.358
Reduce inventory 3.770 .854 .729 -.475 -.317 Provides continuously improved plan 3.521 .833 .694 .198 -.578
Helps trace rejection 3.562 .868 .754 .247 -.501
Better control of resources 3.575 1.077 1.161 -.527 -.358
Enhance decision making 3.257 .912 .832 -.248 -.460 Increase organisational performance 3.385 .932 .869 -.042 -.308
Reduces time to market 3.270 .850 .722 -.317 -.329 Accommodate business growth 3.186 .828 .685 .415 -.359
Better coordination with partners 3.451 .859 .738 .265 -.464
Acquire best practices 3.496 .850 .722 .028 -.183 Expansion of market 3.319 .819 .671 .164 -.209 Enable business alliance 3.150 .835 .697 .069 -.289 Helps cost leadership 3.297 .825 .681 -.177 -.216 Increased revenue 3.307 .832 .693 .139 -.349 Eliminate redundant data 3.677 .878 .771 -.773 -.035 Increase business flexibility 3.305 .772 .595 .020 -.411
Allows data integration 3.584 .877 .768 -.692 -.040 Enables information transparency 3.637 .817 .668 -.407 -.227
Provides organisational flexibility 3.319 .846 .716 -.331 -.305
Empower process owners 3.137 .808 .652 -.627 -.102 Builds common vision 3.137 .791 .626 -.495 -.086
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Table 4.5 (Continued)
Items Mean SD Variance Kurtosis Skewness Improves customer satisfaction 3.527 .972 .944 -.447 -.310
Automate business process 3.298 .846 .716 -.425 -.125
4.1.3.3 Case summaries for perceived challenges variables
The case summaries of the measures of the perceived challenges
are presented in Table 4.6. Observing the mean value, it can be understood
that Lack of qualified staff (4.089), Difficulty to retain people (3.894),
ifficult change management (3.860), Difficult to customise (3.779) and
User resistance (3.788) are some of the highly perceived challenges.
Application not available (2.889), Top management support (2.987), No
business condition (3.053), Distance of CEO and IT head (3.124) and
Poor attitude of leader are less perceived challenges. The standard
deviation and variance were above one for Difficult to manage large
projects and Complex BPR . All the other measures had a value less than
one. The kurtosis and skewness of all the measures show a good normal
distribution.
Table 4.6 Case summaries for perceived challenges variables
Items N = 226
Mean SD Variance Kurtosis Skewness
Require large capital 3.668 .971 .943 -.744 -.292 Complete with planned time 3.403 .915 .837 .198 -.499
Lack of qualified staff 4.089 .849 .721 .105 -.699
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Table 4.6 (Continued)
Items Mean SD Variance Kurtosis Skewness Complex resource allocation 3.376 .941 .885 .024 -.558
Budget increases 3.381 .903 .815 -.227 -.132
Complex integration 3.454 .884 .781 .429 -.560
Difficult to customise 3.779 .945 .893 -.249 -.437
Deal with many players 3.343 .876 .767 .089 -.610
Lack of vendor support 3.465 .895 .801 .354 -.606 Implementation partner not available 3.288 .801 .641 .230 -.462
Unclear application linkages 3.350 .852 .726 .506 -.652
Require good IT infrastructure 3.686 .963 .928 -.435 -.328
Difficult change management 3.860 .756 .572 -.130 -.313
Top management support 2.987 .797 .635 .190 -.189
Difficult training support 3.721 .837 .700 .070 -.540
Difficult to retain people 3.894 .918 .842 .165 -.728
Poor attitude of leader 3.195 .831 .691 .703 -.472 Distance of CEO and IT head 3.124 .732 .536 .701 -.609
User resistance 3.788 .864 .746 .477 -.619 Difficult to manage large project 3.478 1.034 1.068 -.413 -.379
Difficult to align with business 3.323 .965 .931 -.129 -.267
Complex BPR 3.633 1.017 1.033 -.803 -.289
Require strong vision 3.496 .958 .918 -.158 -.370
Application not available 2.889 .806 .650 .052 -.155
No business condition 3.053 .852 .726 -.235 -.232
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4.2 TESTING THE MEASUREMENT MODEL
The constructs measured by the multiple items are tested for
unidimensionality, convergent validity, internal consistency and discriminant
validity. For this purpose, the measurement models of latent constructs are
first developed in the AMOS and the Confirmative Factor Analysis (CFA) is
performed. CFA is a multivariate statistical procedure that is used to test
how well the measured variables consistently represent the constructs that
are understood by researcher. The primary objective of a CFA is to
determine the ability of a predefined factor model to an observed set of data.
The most commonly used test of model adequacy is the Chi-square goodness
of fit test. The null hypothesis for this test is that the model adequately
accounts for the data, while the alternative is that there is a significant
amount of discrepancy. This technique is found appropriate for smaller
sample because this test is highly sensitive to the size of the sample, such
tests involving large samples will generally lead to a rejection of the null
hypothesis.
The following acceptable values of goodness-of-fit indices are
used for assessing the degree of fit between the model and the sample:
normed or relative Chi-square (CMIN/DF, 2:1 or 3:1 is acceptable; Kline
2005), Tucker Lewis Index (TLI; > 0.90 acceptable, > 0.95 excellent; Tucker
and Lewis 1973), the Comparative Fit Index (CFI: > 0.90 acceptable, > 0.95
excellent; Bentler 1990; Bentler and Bonett 1980), and Root Mean Square
error of approximation (RMSEA; < 0.08 acceptable,
< 0.05 excellent; Browne and Cudeck 1993).
A critically important assumption in the conduct of SEM analyses
in general, and in the use of AMOS in particular (Arbuckle 2007), is that the
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data are multivariate normal. Thus, before any analyses of data are
undertaken, it is important to check that this criterion has been met.
Statistical research has shown that skewness tends to impact tests of means
whereas kurtosis severely affects tests of variances and covariances
(DeCarlo 1997). Given that SEM is based on the analysis of covariance
structures, evidence of kurtosis is always of concern and, in particular,
evidence of multivariate kurtosis, as it is known to be exceptionally
detrimental in SEM analyses. The univariate kurtosis value and its critical
ratio (i.e., z-value) produced by the AMOS results are analysed for each of
the measured items.
There appears to be no clear consensus on how large the
non-zero values should be before conclusions of extreme kurtosis are drawn
(Kline 2005). West et al. (1995) considered rescaled 2 values equal to or
greater than 7 to be indicative of early departure from normality. Using this
value of 7 as a guide, a review of the kurtosis values reported in the
Table A 2.1 of Appendix 2 reveals no item to be substantially kurtotic.
The index of multivariate kurtosis and its critical ratio, both of which appear
at the bottom of the kurtosis and critical ratio (C.R.) columns, respectively
are analysed. The most important here is the C.R. value, which in essence
represents (1970, 1974) normalized estimate of multivariate
kurtosis, although it is not explicitly labelled as such (Byrne 2010, p. 104).
When the sample size is very large and multivariate
normalized estimate is distributed as a unit normal variate so that large
values reflect significant positive kurtosis and large negative values reflect
significant negative kurtosis. Bentler (2005) has suggested that, in practice,
values > 5.00 are indicative of the data that are non-normally distributed. In
this application, the z-statistic of 14.495 is highly suggestive of nonnormality
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in the sample (Byrne 2010, p. 102). This nonnormality of multivariate
measures is dealt by bootstrapping the estimates.
Outliers represent cases whose scores are substantially different
from all the others in a particular set of data. Univariate outlier has an
extreme score on a single variable, whereas a multivariate outlier has
extreme scores on two or more variables (Kline 2005). A common approach
to the detection of multivariate outliers is the computation of the squared
mahalanobis distance (d2) for each case. This statistics measures the distance
in standard deviation units between a set of scores for one case and the
sample means for all variables (centroids). Typically, an outlying case will
have a d2 value that stands distinctively apart from all the other d2
values
(Byrne 2010, p. 105). A review of these values reported in
Table A 2.2 of the Appendix 2, shows that the observation numbers 76, 111,
110, and 94 have p1 or p2 as 0.000 and is distinctively different from the
next value, showing multivariate outliers. These observations are removed
from further analysis leaving a tally of 222 observations for the structural
analysis.
4.2.1 Testing the Measurement Model of Institutional
Isomorphic Pressures
First, the measurement model of institutional isomorphic
pressures theorised as three constructs; mimetic pressure, coercive pressure
and normative pressure were tested. The estimates of the items such as
follow recent trend, perception of competitor's supplier, government requires
and government promotion were found to be negative. In addition, the items
such as follow recent trend, firm's supplier require ERP, government
promotion and firm's suppliers adopted ERP were insignificant. Hair et al.
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(2007) suggested that standardised factor loading should be 0.5 or higher,
ideally 0.7 or higher for establishing the convergent validity.
The results in Table 4.7 show a poor convergent validity.
Table 4.7 Results of the confirmative factor analysis of institutional
isomorphic pressures constructs
Dimension Indicators B S.E. C.R. P Beta
Mimetic
Industry perceive favourably
1.000 0.721
Main competitors benefited
0.967 0.085 11.382 *** 0.818
Perception of
1.014 0.086 11.853 *** 0.866
Influenced by media 0.543 0.102 5.319 *** 0.378 Influence by consultants and experts
0.469 0.092 5.108 *** 0.363
Follow recent trend -0.106 0.064 -1.662 0.096 -0.118 Perception of competitor's supplier
-0.170 0.054 -3.151 0.002 -0.224
Normative
Firm's customer adopted ERP
1.000 1.990
Government promotion -0.049 0.106 -0.459 0.647 -0.126 Firm's suppliers adopted ERP
0.006 0.017 0.336 0.737 0.015
Coercive
Firm's customer require ERP
1.000 0.553
Required for competitiveness
1.075 0.151 7.130 *** 0.603
Government requires -0.239 0.111 -2.148 0.032 -0.152 Firm's supplier require ERP
0.036 0.108 0.335 0.737 0.023
Trade association encourages
1.025 0.169 6.078 *** 0.483
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Table 4.7 (Continued)
Summary of second order loading of Institutional isomorphic pressures
Institutional isomorphic pressures
Indicators B S.E. C.R. P Beta
Mimetic pressure 1.000 0.659
Coercive pressure 1.388 0.295 4.700 *** 1.399
Normative pressure 0.927 0.152 6.100 *** 0.223
*** Significant at < 0.001 level
Table 4.8 Model fit summary of institutional isomorphic pressures
CMIN CMIN DF P CMIN/DF
1005.584 87 0.000 11.558
Baseline comparison TLI CFI
0.265 0.391
RMSEA RMSEA LO 90 HI 90 PCLOSE
0.217 0.205 0.229 0.000
Discriminant validity is assessed on the fit indices of the model.
The results of the goodness of fit of the model (Table 4.8) also showed a
poor fit with a CMIN/DF = 11.558, CFI = 0.391 and RMSEA = 0.217.
This shows a poor divergent validity of the construct (Table 4.7).
The results presented on the path diagram in Figure 4.1.
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Figure 4.1 Hypothesised second-order CFA model of institutional
isomorphic pressures
Therefore, an exploratory factor analysis to investigate what each
measured items really indicate was performed. The items loaded on to five
components. While observing the grouping of the items, the factors are
agents mechanisms of the
rs are labelled as
associate pressure, customer pressure, competitor pressure, supplier pressure
and government pressure. However, the construct of government pressure
had negative variance and the supplier pressure was insignificant. Therefore,
these two constructs were removed for further analysis.
Benders et al. (2006) quoted DiMaggio and Powell (1983) that the
* Values Shown are Unstandardised estimates
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isomorphic forces are distinguishable analytically but not necessarily
empirical. They state that the forces often act in conjunction. Government
forces are translated into norms through legislation and legal enforcement
and may reflect in normative forces on which decision makers try to comply
with. This justifies the modification that was done to the constructs. Table
4.9 shows the outcome of the modified measurement model.
Table 4.9 Measurement model of institutional isomorphic pressures
Dimension Indicators B S.E. C.R. P Beta
Associate Pressure
Influence by Consultants and Experts
1.000 0.870
Influenced by Media 1.151 0.069 16.758 *** 0.901 Trade Association Encourages 0.986 0.068 14.424 *** 0.800
Customer Pressure
Firm's Customer Require ERP 1.000 0.763
Firm's Customer Adopted ERP 1.045 0.135 7.721 *** 0.684
Competitor Pressure
Perception of
Customers
1.000
0.895
Main Competitors Benefited 0.975 0.061 16.114 *** 0.860
Industry Perceive Favourably 0.953 0.077 12.309 *** 0.715
Required for Competitiveness 0.827 0.064 12.859 *** 0.736
Summary of second order loading of Institutional isomorphic pressures
Institutional isomorphic pressures
Associate Pressure 1.000 0.752
Competitor Pressure 0.493 0.110 4.466 *** 0.405
Customer Pressure 1.013 0.229 4.433 *** 0.957 *** Significant at < 0.001 level
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The goodness of fit of the explorative model shows a
CMIN/DF = 2.384 (Table 4.10). This value is above the generally acceptable
value of 2. However, Arbuckle (2007, p. 587) quote
. (1977) suggest that the researcher also compute a relative chi-square ( ).... They suggest a ratio of appro . In our experience, however, to degrees of freedom ratios in the range of 2 to 1 or 3 to 1 are indicative of an acceptable fit between the hypothetical model and the sample data.
(Carmines and McIver 1981, p. 80)
...different researchers have recommended using ratios as low as 2 or as high as 5 to indicate a reasonable fit.
(Marsh and Hocevar 1985).
Table 4.10 Model fit summary of institutional isomorphic pressures
CMIN CMIN DF P CMIN/DF
57.21 24 0.000 2.384
Baseline comparison TLI CFI
0.953 0.969
RMSEA RMSEA LO 90 HI 90 PCLOSE
0.079 0.053 0.106 .035
Therefore, the CMIN/DF value of this model, which is close to 2,
but less than 5 is accepted and the model is considered, fit. In addition the
CFI = 0.969, TFI = 0.953 are in the acceptable range of above 0.9.
The RMSEA is 0.079, which is within the general acceptance level of 0.08.
Therefore, the RMSEA value for this model is less than 0.08 and it indicates
143
a reasonable error of approximation. Considering the various goodness of fit
values, the model is considered to have an adequate fit and considered for
further structural analysis. The validated measurement model is shown in
Figure 4.2.
Figure 4.2 Modified measurement model of institutional
isomorphic pressures
The correlation and the covariance results of the sub-construct of
the institutional isomorphic pressures obtained by the explorative factor
analysis are presented in Table 4.11. The relationship between associate
pressure, customer pressure and competitor pressure were significant.
* Values Shown are Unstandardised estimates
144
Table 4.11 Covariance and correlation of sub-constructs of
institutional isomorphic pressures
Relationship Covariance
Correlation Estimate S.E. C.R. P
Trade <--> Customer 0.316 0.047 6.692 *** 0.719
Trade <--> Competitor 0.154 0.04 3.869 *** 0.304
Customer <--> Competitor 0.156 0.037 4.221 *** 0.387
*** Significant at < 0.001 level
4.2.2 Testing the Measurement Model of Perceived Benefits
The measurement model of perceived benefits has five constructs:
operational benefits, managerial benefits, strategic benefits, IT infrastructure
benefits and organisational benefits. Table 4.12 provides the unstandardised
estimate (B), Standard Error (S.E.), Critical Ratio (C.R.), significance value
(P) and the standardised estimate (Beta). The standardised estimates of all
the variables on the first order constructs are above 0.7. The critical ratio
(C.R.) of all the items are above 7.00 and are significant at P < 0.001
indicating that each variable reflect the latent content to a greater extent,
showing a high discriminant and convergent validity.
Table 4.12 Measurement model of perceived benefits
Dimension Indicators B S.E. C.R. P Beta
Operational Benefits
Reduces Operational Cost 1.000 .796
Provides Continuously Improved Plan
.872 .063 13.955 *** .817
145
Table 4.12 (Continued)
Dimension Indicators B S.E. C.R. P Beta
Operational Benefits
Improves Productivity 1.028 .072 14.317 *** .832
Reduces Business Cycle .864 .065 13.205 *** .785
Improves Quality .948 .072 13.138 *** .782
Improves Customer Services .962 .071 13.507 *** .798
Reduces Wastages .948 .070 13.450 *** .795
Integrates Operations .812 .059 13.854 *** .813
Enables Process Re-engineering .791 .059 13.435 *** .795
Improves Process Efficiency .843 .062 13.511 *** .798
Reduce Inventory .877 .064 13.669 *** .805 Helps Trace Rejection .908 .066 13.791 *** .810
Managerial Benefits
Better Control of Resources 1.000 .878
Enhance Decision Making .871 .048 18.255 *** .899
Increase Organisational Performance
.849 .050 17.023 *** .862
Organisati-onal Benefits
Empower Process Owners 1.000 .832
Automate Business process .994 .074 13.520 *** .790
Builds Common Vision .963 .068 14.200 *** .818
146
Table 4.12 (Continued)
Dimension Indicators B S.E. C.R. P Beta
Organisati-onal Benefits
Improves Customer Satisfaction 1.207 .083 14.458 *** .828
Strategic Benefits
Reduces Time to Market 1.000 .807
Increased Revenue 1.014 .068 14.831 *** .845 Helps Cost Leadership .942 .070 13.536 *** .792
Better Coordination with Partners .947 .074 12.730 *** .757
Accommodate Business Growth .997 .070 14.311 *** .824
Acquire Best Practices .979 .072 13.532 *** .792
Expansion of Market 1.018 .067 15.182 *** .858
Enable Business Alliance 1.028 .068 15.109 *** .855
IT Infrastructure Benefits
Eliminate Redundant Data 1.000 .799
Provides Organisational flexibility
.906 .066 13.670 *** .824
Allows Data Integration 1.019 .076 13.381 *** .810
Enables Information Transparency .898 .072 12.426 *** .765
Increase Business flexibility 1.012 .071 14.155 *** .846
147
Table 4.12 (Continued)
Summary of second order loading of perceived benefits
Dimension Indicators B S.E. C.R. P Beta
Perceived Benefits
Operational Benefits 1.000 .636
Managerial Benefits 1.521 .191 7.964 *** .797
Strategic Benefits 1.132 .145 7.834 *** .817
IT Infrastructure Benefits 1.189 .152 7.849 *** .841
Organisational Benefits 1.224 .148 8.245 *** .904
*** Significant at < 0.001 level
The model is found to have high goodness of fit. The Chi-Square
(CMIN) to degree of freedom (CMIN/DF) is found to be 1.881, which is less
than two indicating a good fit of the model to the sample. The TLI and CFI
are 0.926 and 0.931 respectively and are higher than 0.9 and close to one
indicating good fitness indices. RMSEA (0.063) is also below 0.08 with
PClose less than 0.05 indicating an acceptable residual error (Table 4.13).
The validated measurement model is presented in Figure 4.3.
Table 4.13 Model fit summary of perceived benefits
CMIN CMIN DF P CMIN/DF
863.343 459 0.000 1.881
Baseline comparison TLI CFI
.926 .931
RMSEA RMSEA LO 90 HI 90 PCLOSE
0.063 0.057 0.070 0.001
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Figure 4.3 Hypothesised second-order CFA model of perceived benefit
* Values Shown are Unstandardised estimates
149
The correlation and the covariance results are presented in
Table 4.14. The results show a positive and significant relationship between
the sub-constructs. This relationship proves that the variables adequately
capture the proposed construct of perceived benefits. This shows that the
variables have a good criterion-related validity making it useful in predicting
the outcome and qualify the sub-constructs to be included for a regression
analysis.
Table 4.14 Covariance and correlation of sub-constructs of
perceived benefits
Relationship Covariance Correlation
Estimate S.E. C.R. P
Operational <--> Managerial .340 .056 6.093 *** .518
Managerial <--> Strategic .416 .059 7.096 *** .647
Strategic <--> IT .325 .045 7.194 *** .692
Organisation <--> IT .360 .048 7.470 *** .789
Operational <--> Strategic .261 .010 6.322 *** .551
Operational <--> IT .219 .040 5.475 *** .459
Organisation <--> Operational .275 .043 6.441 *** .596
Managerial <--> IT .422 .060 7.009 *** .651
Organisation <--> Managerial .450 .062 7.312 *** .719
Organisation <--> Strategic .314 .044 7.086 *** .695
*** Significant at < 0.001 level
The results of perceived benefits tested using the framework of
Shang and Seddon (2004) showed a valid model with a high goodness of fit.
The reliability and the confirmative factor analysis of the constructs show
that the theoretical classifications of the ERP benefits are justified.
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4.2.3 Testing the Measurement Model of Perceived Challenges
The measurement model of perceived challenges has four
constructs: resource challenges, technology challenges, people challenges
and organisational challenges. To check the discriminant and convergent
validity, the standardised estimates on the first order constructs are analysed
and found that all the variables are above 10.394. The critical ratios are
above 8.776 and are significant at P < 0.001 (Table 4.15). This indicates that
each variable reflect the latent content to a greater extent.
Table 4.15 Measurement model of perceived challenges
Dimension Indicators B S.E. C.R. P Beta
Resource Challenges
Budget increases 1.000 .879
Complex resource allocation 1.098 .052 21.145 *** .925
Lack of qualified staff .934 .051 18.166 *** .864
Complete with planned time 1.085 .050 21.682 *** .935
Require large capital 1.101 .056 19.677 *** .897
Technology Challenges
Require good IT infrastructure 1.000 .765
Unclear application linkages .948 .072 13.220 *** .820
Implementation partner not available .922 .066 13.991 *** .859
Lack of vendor support 1.065 .074 14.364 *** .877
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Table 4.15 (Continued)
Dimension Indicators B S.E. C.R. P Beta
Deal with many players 1.036 .072 14.385 *** .878
Difficult to customise 1.031 .079 13.042 *** .811
Complex integration 1.030 .073 14.107 *** .864
Organisational Challenges
No business condition 1.000 .715
Application not available .951 .091 10.394 *** .721
Require strong vision 1.233 .110 11.261 *** .781
Complex BPR 1.448 .117 12.379 *** .858
Difficult to align with business 1.271 .109 11.622 *** .806
Difficult to manage large project 1.455 .119 12.244 *** .849
People Challenges
User resistance 1.000 .848
Distance of CEO and IT head .754 .056 13.456 *** .757
Poor attitude of leader .868 .064 13.598 *** .762
Difficult to retain people 1.039 .066 15.746 *** .835
Difficult training support .938 .062 15.244 *** .819
Top management support .934 .057 16.520 *** .859
Difficult change management .866 .055 15.840 *** .838
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Table 4.15 (Continued)
Summary of second order loading of perceived challenges
Dimension Indicators B S.E. C.R. P Beta
Perceived Challenges
Resource challenges 1.000 .699
Technology challenges 1.189 .130 9.133 *** .889
People challenges 1.166 .120 9.694 *** .880
Organisational challenges 1.004 .114 8.776 *** .916
*** Significant at < 0.001 level
The measurement model of the perceived challenges is also found
to have high goodness of fit. The Chi-Square (CMIN) to degree of freedom
(CMIN/DF) is found to be 1.948, indicating a good fit of the model to the
sample. The TLI and CFI are 0.944 and 0.950 respectively indicating good
fitness indices. RMSEA is 0.065 with a PClose<0.005 indicating an
acceptable residual error that is significant in the measured sample (Table
4.16). The validated measurement model is presented in Figure 4.4.
Table 4.16 Model fit summary of perceived challenges
CMIN CMIN DF P CMIN/DF
527.804 271 .000 1.948
Baseline comparison TLI CFI
.944 .950
RMSEA RMSEA LO 90 HI 90 PCLOSE
.065 .057 .074 .001
153
Figure 4.4 Hypothesised second-order CFA model of perceived challenges
* Values Shown are Unstandardised estimates
154
The correlation and the covariance results of the sub-construct of
the perceived challenges are presented in Table 4.17. The results show a
positive and significant relationship between the sub-constructs.
This relationship proves that the variables adequately capture the proposed
construct of perceived challenges. This shows that the variables have a good
criterion-related validity making it useful in predicting the outcome and
qualifies the sub-constructs to be included for a regression analysis.
Table 4.17 Covariance and correlation of sub-constructs of
perceived challenges
Relationships Covariance
Correlation Estimate S.E. C.R. P
Resource <--> Technology .388 .054 7.129 *** .673
Technology <--> People .408 .054 7.501 *** .769
People <--> Organisational .359 .048 7.423 *** .824
Resource <--> People .336 .050 6.781 *** .587
Resource <--> Organisational .293 .045 6.547 *** .619
Technology <--> Organisational .355 .050 7.093 *** .808
*** Significant at < 0.001 level
The conceptual categories of the perceived challenges are
verified. The model has a high goodness of fit and a good convergent and
discriminant validity.
4.3 RELIABILITY STATISTICS
The reliability of the constructs was also done by examining the
Cronbach's alpha (Table 4.18). The alpha values for the constructs are all
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above the acceptable level of 0.7. For the constructs with only two items,
their correlation is tested. The correlation value (r) for customer pressure is
0.680 and is significant at P < 0.001 level.
Table 4.18 Reliability statistics
Dimension/Construct No. of Items Alpha
Benefits - Operational 12 .954
Benefits - Managerial 3 .905
Benefits - Strategic 8 .939
Benefits - IT Infrastructure 5 .900
Benefits - Organisational 4 .882
Challenges - Resources 5 .953
Challenges - Technology 7 .940
Challenges - People 7 .930
Challenges - Organisational 6 .905
Institutional isomorphic pressures - Associate 3 .890
Institutional isomorphic pressures - Customer * 2 .680***
Institutional isomorphic pressures - Competitive 4 .874
***significant at < 0.001 level.
4.4 TEST FOR COMMON METHOD VARIANCE
The data collection method used was self administered, cross
sectional research design. The measures were collected from the same
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-factor
test was done to verify the bias (Podsakoff and Organ 1986). The un-rotated
principal component factor analysis (Table 4.19) revealed the presence of 13
factors explaining a total variance of 74.9% and the largest factor did not
account for a majority of the variance (31.769%). This shows that there is no
apparent general factor. Therefore, the presence of common method bias is
rejected.
Table 4.19 Unrotated principal component factor analysis
for Harma one factor test
S.No. Extraction Sums of Squared Loadings
Total Eigen values % of Variance Cumulative %
1 23.191 31.769 31.769
2 8.436 11.557 43.326
3 4.523 6.195 49.521
4 3.160 4.328 53.849
5 2.492 3.414 57.263
6 2.398 3.285 60.548
7 2.193 3.004 63.552
8 1.944 2.664 66.215
9 1.655 2.267 68.482
10 1.286 1.762 70.243
11 1.219 1.670 71.913
12 1.165 1.596 73.509
13 1.013 1.387 74.897
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To cross validate the results one more method was used to find
common method bias. Marker variable technique, which is a partial
correlation procedure, is used to find the method bias. Lindell & Brandt
(2000) explained the use of this procedure as a post hoc test without the
marker variable being identified prior to the test. Lindell and Whitney (2001)
stated that the smallest correlation among the manifest variables provides a
reasonable proxy for CMV.
From the correlation table of the manifest variables including the
dependent and predictors, the smallest positive correlation (rL1)in this study
was found to be 0.001.Though this may be used as a conservative estimate of
CMV, to remove potential chance factors according to Lindell and Whitney
(2001), the second lowest positive correlation (rL2) was identified as 0.002.
As in marker-variable analysis, a method factor is assumed to have a
constant correlation with all of the measured items. The rL2 can be used to
adjust the correlation between the variables under investigation. Considering
the magnitude of the rL2, the adjustment of a value 0.002 will not cause any
significant variation to the correlation matrix. Therefore, it can be concluded
that the method bias in this study is not significant.
4.5 Assumptions of SEM
Since SEM is fundamentally using the regression analysis, it is
necessary to satisfy certain assumptions before analysing the structural
equation. Similar to the requirement of a regression analysis, the normality
and outliers have been tested and reported earlier. In addition, the following
characteristics of the data are verified before the path analysis is done.
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4.5.1 Reliability
The analysis of reliability is examined by Cronbach's alpha and is
reported in Table 4.18. The reliability for all the measures varies between
0.874 and 0.954, well above the acceptable limit of 0.70 (Nunnally 1978).
The construct that was indicated by just two variables was tested by the
correlation and the value was found to be 0.680 showing that the two
variables measured the intended parameter with a good reliability.
4.5.2 Content Validity
The items used in the measure are supported by a thorough
literature review. The measure was also subjected to rigorous refining
processes including interviews, peer review, focus group meetings, and
quantitative tests. These checks add to the confidence placed on the content
validity that the measures reflect the reality of the measured domain.
4.5.3 Convergent Validity
The t-statistics given by C.R. value of each factor loading is used
to examine convergent validity of each construct's measure (Chin 1998b). As
a guideline, items with loading of 0.70 or above should be retained (Hulland
1999). T also indicates the convergent validity of the
4.5.4 Discriminant Validity
The confirmative factor analysis is used for the analysis of the
measurement model and the cross loading of the factors are not analysed.
However, the high critical ratio (C.R.) indicates that the measures load
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highly on their respective constructs (Chin 1998a, 1998b; Gefen et al. 2000).
In addition, their significant covariances indicate a discriminant function.
4.6 TESTING THE STRUCTURAL MODEL
After the measurement model is tested and the assumptions for
the SEM analysis are satisfied, the complete structural model is developed in
the AMOS. In the model, the institutional isomorphic pressures were treated
as Independent Variable (IV). Perceived benefits (M1), perceived
challenges (M2) and organisational complexity (M3) were treated as the
Mediating Variables (MV). The adoption of ERP was treated as the
Dependent Variable (DV). The paths are created in such a way that an arrow
leads from the independent variable to the mediating variables and from
mediating variables to the dependent variable. A direct relationship path is
also created by drawing an arrow from the independent variable to the
dependent variable (Figure 4.5). First the results of the hypothesised model
is analysed, then the nested models are tested and compared to the
hypothesised model to find the best fitting model of the sample.
4.6.1 Testing the Proposed Model and Fit Indices
The results of the hypothesised model are presented in
Table 4.20. The regression weight of the paths between institutional
isomorphic pressures and ERP adoption (B = 0.857), between institutional
isomorphic pressures and perceived benefits (B = 0.377), between
institutional isomorphic pressures and organisational complexity
(B = 5.696), between perceived benefits and ERP adoption (B = 1.321), and
between organisational complexity and ERP adoption (B = 0.045) were all
positive and statistically significant.
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Figure 4.5 Hypothesised structural model developed in AMOS
161
The regression weight of paths between institutional isomorphic
pressures and perceived challenges (B = -0.327), between perceived
challenges and ERP adoption (B = -1.105), between organisational
complexity and perceived challenges (B = -0.017) and between perceived
challenges and perceived benefits (B = -0.249) were all statistically
significant and negative. The validity of the hypothesised model is verified
by the goodness of fit indices. The CMIN/DF value is 1.667, clearly within
the acceptable limit. TLI = 0.885 and CFI = 0.89 are close to an acceptable
fit of the model (value above 0.9 is considered as acceptable fit and values
around 0.89 reflect a reasonable fit of a model). RMSEA value 0.055 at
P < 0.01 is below 0.080, showing an acceptable error values. Considering the
various fit measures, the model can be said to be valid and perfectly fit the
data i.e., the model is proved empirically true. The results are presented as
the AMOS output with unstandardised regression weight is shown in Figure
4.6 and the path diagram in Figure 4.7. To clarify the practical value of the
model, the R Square value was analysed from the text output.
The results show that the independent and mediating variables together can
predict ERP adoption to an extent of 67.1%.
Table 4.20 The results of the analysis of the hypothesised
structural model
AMOS Results
Direct Effect B S.E C.R P Beta
ERP Adoption
Institutional isomorphic pressures 0.857 0.274 3.132 0.002 0.263
Perceived Benefits
Institutional isomorphic pressures 0.377 0.082 4.616 *** 0.467
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Table 4.20 (Continued)
Direct Effect B S.E C.R P Beta
Perceived Challenges
Institutional isomorphic pressures -0.327 0.096 -3.415 *** -0.365
Organisational Complexity
Institutional isomorphic pressures 5.696 0.799 7.126 *** 0.578
ERP Adoption
Perceived Benefits 1.321 0.286 4.617 *** 0.327
ERP Adoption
Perceived Challenges -1.105 0.217 -5.099 *** -0.303
ERP Adoption
Organisational Complexity 0.045 0.018 2.420 0.016 0.135
Perceived Challenges
Organisational Complexity -0.017 0.008 -2.179 0.029 -0.190
Perceived Benefits
Perceived Challenges -0.249 0.075 -3.299 *** -0.276
Selected Fit Measures
CMIN DF CMIN/DF P
3651.986 2191 1.667 0.000
TLI CFI RMSEA PCLOSE
R Square = 0.671 0.885 0.89 0.055 0.005
*** Significant at < 0.001 level
163
Figure 4.6 Structural model in AMOS with unstandardised results
* Values shown are unstandardised estimates
164
4.6.2 Model Comparison and Summary of the Model Fit
In order to identify whether the hypothesised model represents the
sample data better than alternate models, first the Modification Indices (MI)
are analysed. Secondly, the nested models are compared. During the
analysis, the MI with a threshold limit of four were listed and analysed.
Large MIs argue for the presence of factor cross-loadings and error
covariance. on of the
AMOS output, all the values were only related to the error covariance and in
addition freeing those parameters were not substantively meaningful.
Similarly, the parameters in output did not
show any meaningful cross loading. This shows that there is no substantial
evidence of model misfit. To find the evidence for re-specification of the
model to best fit the sample data, alternate models nested in the hypothesised
model are compared. Significant change in the Chi-square value to one
degree of freedom is verified for a significantly better model.
For testing the invariance between nested models, AMOS
provides different options. One of the methods is to use the option of
models manually and to
compare them for the best fit. It is possible to specify hundreds or thousands
of candidate models in this way, but to do so would be time consuming and
would inevitably lead to mistakes. AMOS also provides a second method for
specifying candidate models. In this alternative approach, some single- and
double-headed arrows in a path diagram are designated as optional. When
optional arrows are present, AMOS fits the model both with and without
each optional arrow, using every possible subset of them. If only one arrow
is optional then an exploratory analysis consists of fitting the model with and
without the optional arrow. If there are, say, three optional arrows, the
165
program fits the model eight (that is, 23) times, using every possible subset
of the optional arrows. An approach is chosen on the complexity of the
model and the level of exploration, depending on how many arrows are
optional. There is a practical limit to the number of optional arrows, since
each optional arrow doubles the number of models that need to be fitted. In
this study, the candidate models are created by adding a constraint by freeing
one parameter at a time using the manage model option. The goodness of
fit indices of all the models is presented in Table 4.21. The comparison of
the model fits show that the hypothesised model has the best fit than the
other models.
Table 4.21 Goodness of fit indices of model comparison
Model CMIN DF P CMIN/DF TLI CFI
Hypothesised Model 3651.99 2191 0 1.667 0.885 0.890
OC PC Removed 4656.42 2192 0 1.668 0.885 0.890
OC EA Removed 3657.54 2192 0 1.669 0.885 0.889
IIP OC Removed 3715.54 2192 0 1.695 0.881 0.885
IIP PC Removed 3665.91 2192 0 1.672 0.884 0.889
IIP PB Removed 3683.02 2192 0 1.680 0.883 0.888
IIP EA Removed 3663.07 2192 0 1.671 0.885 0.889
PC PB Removed 3663.19 2192 0 1.671 0.885 0.889
PC EA Removed 3679.67 2192 0 1.679 0.883 0.888
PB EA Removed 3674.36 2192 0 1.676 0.884 0.888
Independence model 14425.6 2278 0 6.333 0 0
IIP = Institutional isomorphic pressures, PB = Perceived Benefits, EA = ERP adoption, PC = Perceived Challenges, OC = Organisational complexity
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To test the significance of the difference between the models,
AMOS examines every pair of models in which one model of the pair is
obtained by constraining the parameters of the other. For every such pair of
nested models, several statistics to compare the two models are displayed
in Table 4.21 and the significant difference with the hypothesised model is
presented in Table 4.22.
Table 4.22 Nested model comparisons assuming hypothesised
model to be correct
Model DF CMIN P TLI
OC PC Removed 1 4.429 0.035 0
OC EA Removed 1 5.551 0.018 0
IIP OC Removed 1 63.549 0 0.005
IIP PC Removed 1 13.925 0 0.001
IIP PB Removed 1 31.038 0 0.002
IIP EA Removed 1 11.082 0.001 0.001
PC PB Removed 1 11.204 0.001 0.001
PC EA Removed 1 27.687 0 0.002
PB EA Removed 1 22.368 0 0.002
IIP = Institutional isomorphic pressures, PB = Perceived Benefits, EA = ERP adoption, PC = Perceived Challenges, OC = Organisational complexity
The output shows the comparison of the alternate model obtained
by constraining the original hypothesised model. Considering that the
original hypothesised model is correct, a test of the additional constraints of
the alternate models based on the Chi-square statistic, which has 1 degree of
freedom, is done. The probability of a Chi-square statistic with 1 degree of
freedom exceeding Chi-square value (CMIN) is indistinguishable below
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0.05. Therefore, the alternate models can be rejected at any conventional
significance level. The results also show the level of increase in the TLI
when constraint is reduced. The results show that the hypothesised model
has the best fit to the sample data. Therefore, the model is accepted without
any modification and further analysis is carried out. The unstandardised
regression coefficients of the paths of the hypothesised model are shown in
Figure 4.7.
Figure 4.7 Results of the hypothesised model
ERP adoption R2 = 0.671
Perceived Challenges
Institutional Isomorphic Pressures
B= -0.249, C.R = -3.299, P<0.001
B=5.696, C.R =7.126
P<0.001
Perceived Benefits B=0.377,
C.R =4.616 P < 0.001
Organisational Complexity
B=0.857, C.R =3.132, P=0.002
B=1.312, C.R = 4.617,
P < 0.001
B= -0.017, C.R = -2.179,
P=0.029
B=0.045, C.R =2.42,
P=0.016
B= -0.327, C.R =-3.415
P<0.001
B= - 1.105, C.R =-5.099
P<0.001
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4.6.3 Hypotheses Testing for the Basic Structural Model
The relationship between the institutional isomorphic pressures
(IV) and the ERP adoption (DV) was first verified in Table 4.20.
The regression coefficient (B) was found to be 0.857 and significant
(CR = 3.132, P < 0.005). The findings do not support the null hypothesis
(H10) of proposition 1, which proposes that institutional isomorphic
pressures will not influence ERP adoption. The alternate hypothesis that
proposes that institutional isomorphic pressures will influence the ERP
adoption is therefore accepted.
H1
H10 Institutional isomorphic pressures will not
influence the ERP adoption Rejected
H1a Institutional isomorphic pressures will influence
the ERP adoption Accepted
The relationship between the institutional isomorphic pressures
(IV), with the three mediating variables, is next verified in Table 4.20.
The relation between the institutional isomorphic pressures (IV) and the
perceived benefits (M1) is first checked. The coefficient value (0.377)
suggests that the institutional isomorphic pressures positively influences the
perceived benefits and is significant at P < 0.001 with CR = 4.616.
The findings do not support the null hypothesis (H20) of proposition 2, which
proposes that institutional isomorphic pressures will not influence the
perceived benefits. The alternate hypothesis that proposes that institutional
isomorphic pressures will influence the perceived benefits is therefore
accepted.
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H2
H20 Institutional isomorphic pressures will not
influence the perceived benefits Rejected
H2a Institutional isomorphic pressures will influence
the perceived benefits Accepted
Next, the relation between the institutional isomorphic pressures
(IV) and the perceived challenges (M2) are verified. Institutional isomorphic
pressures negatively influences the perceived challenges
(B = -0.327). The relationship is found to be significant at P < 0.001 with CR
= -3.415. The findings do not support the null hypothesis (H30) of
proposition 3, which proposes that institutional isomorphic pressures will not
influence the perceived challenges. The alternate hypothesis that proposes
that institutional isomorphic pressures will influence the perceived
challenges is therefore accepted.
H3
H30 Institutional isomorphic pressures will not
influence the perceived challenges Rejected
H3a Institutional isomorphic pressures will influence
the perceived challenges Accepted
The relationship between institution isomorphic pressures (IV)
and the organisational complexity (M3) is next verified. Institutional
isomorphic pressures positively (B = 5.696) and significantly (CR = 7.126, P
< 0.001) influence the organisational complexity. The findings do not
support the null hypothesis (H40) of proposition 4, which proposes that
institutional isomorphic pressures will not influence the organisational
complexity. The alternate hypothesis that proposes that institutional
170
isomorphic pressures will influence the organisational complexity is
therefore accepted.
H4
H40 Institutional isomorphic pressures will not
influence the organisational complexity Rejected
H4a Institutional isomorphic pressures will influence
the organisational complexity Accepted
The relationship between the three mediating variables and the
ERP adoption variables is next verified from Table 4.20. First the
relationship between perceived benefits (M1) on ERP adoption (DV) show
that there is a positive influence (B = 1.312) of perceived benefits on ERP
adoption. The result is significant at P < 0.001 with CR = 4.617.
The findings do not support the null hypothesis (H50) of proposition 5,
which proposes that perceived benefits will not influence the ERP adoption.
The alternate hypothesis that proposes that perceived benefits will influence
the ERP adoption is therefore accepted.
P5 H50
Perceived benefits will not influence the ERP
adoption Rejected
H5a Perceived benefits will influence the ERP adoption Accepted
The influence of perceived challenges (M2) on the ERP adoption
(DV) is negative (B = -1.105) on the ERP adoption and is highly significant
(CR = -5.099, P < 0.001). The findings do not support the null hypothesis
(H60) of proposition 6, which proposes that perceived challenges would not
influence the ERP adoption. The alternate hypothesis that proposes that
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perceived challenges would influence the ERP adoption is therefore
accepted.
P6 H60
Perceived challenges will not influence ERP
adoption Rejected
H6a Perceived challenges will influence ERP adoption Accepted
The relationship between the organisational complexity (M3) and
the ERP adoption show a positive influence (B = 0.045) and is significant
(CR = 2.42, P < 0.05).The findings do not support the null hypothesis (H70)
of proposition 7, which proposes that organisational complexity will not
influence the ERP adoption. The alternate hypothesis that proposes that
organisational complexity will influence the ERP adoption is therefore
accepted.
P7
H70 Organisational complexity will not influence ERP
adoption Rejected
H7a Organisational complexity will influence the ERP
adoption Accepted
Esteves (2006) proposed a model on business complexity and its
impact on the strategic alignment of the ERP package and the system
benefits. A similar relationship is also tested additionally in this study.
The relationship between the mediating variables was also tested to
investigate a change in effect between the mediating variables.
The influence of organisational complexity (M3) on perceived challenges
(M2) was found to be significantly negative (B = -0.017, CR = -2.179,
P < 0.05). The findings do not support the null hypothesis (H80) of
172
proposition 8, which proposes that organisational complexity will not
influence the perceived challenges. The alternate hypothesis that proposes
that organisational complexity will influence the perceived challenges is
therefore accepted.
P8
H80 Organisational complexity will not influence
Perceived challenges Rejected
H8a Organisational complexity will influence the
Perceived challenges Accepted
The influence of perceived challenges (M2) on perceived benefits
(M1) was verified. The relationship between them was found to be
significantly negative (B = -0.249, CR = -3.299, P < 0.001). The findings do
not support the null hypothesis (H90) of proposition 9, which proposes that
perceived challenges will not influence the perceived benefits.
The alternate hypothesis that proposes that perceived challenges will
influence the perceived benefits is therefore accepted.
P9
H90 Perceived challenges will not influence perceived
benefits Rejected
H9a Perceived challenges will influence perceived
benefits Accepted
4.7 TESTING THE MEDIATION EFFECT
After the model is properly identified and the first sets of
hypotheses are verified, the test for mediation and the verification of the
related hypotheses are done. Though this study uses SEM technique with
AMOS software, the Baron-Kenny (1986) steps are followed to investigate
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the mediation test step-by-step. Finally, a complete multiple mediator model
was analysed for simultaneous indirect effect using AMOS.
(1986) analysis is to test
first that there is a significant zero-order effect between; (i) the independent
variable and the dependent variable, (2) independent variable and
moderating variable, (3) moderating variable and the dependent variable.
Zero-order correlation is the relationship between two variables, while
ignoring the influence of other variables in prediction. Since latent variables
are used in the model, to calculate the zero order relationship, the
standardized regression coefficients are used as effect size measures for
individual paths that are equivalent to the correlation value as suggested by
MacKinnon et al. (2007). The regression coefficient between each set of
factors is calculated. Table 4.23 shows the consolidated results of the zero
order relationship i.e. the relationship between the two variables when all
other factors are controlled. The relationships are all significant at
P < 0.001. These results verify the Baron- suggest
that the factors included for the study can be tested for mediation.
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4.7.1 Test for Simple Mediation Effect
To investigate the independent effect of each mediator on the
independent and dependent variable, the individual mediator model within
the multiple mediator model is initially analysed for mediation effect.
The results are presented in Tables 4.24 to 4.33 and the significance of the
indirect effect was verified by bootstrapping the sample 5000 times and
considering a 95% confidence interval.
4.7.1.1 Test for simple mediating effect of perceived benefits
First, the independent mediator model of perceived benefits is
presented in Table 4.24. The regression coefficient of the direct effect of
institutional isomorphic pressures on ERP adoption (B = 1.258) has been
reduced in the presence of the perceived benefits, indicating a mediation.
The total effect on ERP adoption was found to be B = 2.097, and the weight
of the indirect effect was B = 0.839. The goodness of fit of the model is
analysed for the validity of the theorised model to the data.
he CMIN/DF = 1.84 at P < 0.001, TFI = 0.902, CFI = 0.908, and
RMSEA = 0.062 at P < 0.001 are compared with the rule of thumb and
found adequate for good model fit. The regression weights of IV to MV and
MV to DV are also found to be positive and significant. However, to test
the significance of the indirect effect, the results of bootstrapping presented
in Table 4.25 are analysed. Since the results of individual regression
weights tend to be normally distributed, results of bootstrapping are
presented for indirect effects because they are known to be non-normal.
Both percentile method and bias corrected confidence interval at 95% are
calculated for the regression weights of the model paths and the direct,
indirect and total estimates. The results of direct, indirect and the total
176
effects are found to be non-zero and significant, so the possibility of the
values becoming zero in the population parameters is ruled out.
Table 4.24 Mediating effect of perceived benefits between
institutional isomorphic pressures and ERP adoption
Model
AMOS Results
Direct Effect B S.E C.R P Beta ERP Adoption
Perceived Benefits 1.887 0.333 5.669 *** 0.466
Perceived Benefits Institutional isomorphic pressures 0.445 0.081 5.492 *** 0.571
ERP Adoption Institutional isomorphic pressures 1.258 0.258 4.872 *** 0.398
Indirect Effect B Beta
ERP Adoption Institutional isomorphic pressures 0.839 0.266
Total Effect B Beta
ERP Adoption Institutional isomorphic pressures 2.097 0.664
Selected Fit Measures
CMIN DF CMIN/DF P 1488.828 809 1.84 0.000
TLI CFI RMSEA PCLOSE 0.902 0.908 0.062 0.000
*** Significant at < 0.001 level
ERP Adoption
Perceived Benefits
Institutional Isomorphic Pressures
B=0.445, C.R =5.492 P < 0.001
B=1.887, C.R = 5.669, P < 0.001
R2 = 0.587
B=1.258, C.R = 4.872, P < 0.001 (Zero Effect: B = 2.072, C.R = 7.737, P < 0.001)
178
The findings do not support the null hypothesis (H100) of
proposition 10, which proposes that perceived benefits will not mediate the
institutional isomorphic pressures towards ERP adoption. The alternate
hypothesis that proposes that perceived benefits will mediate the
institutional isomorphic pressures towards ERP adoption is therefore
accepted. The results strongly propose that the presence of perceived
benefits mediate the influence of institutional isomorphic pressures and
positively affect the ERP adoption. To examine the type of mediation
effect, the recommendations of Zhao et al. (2010) are applied. Since the
direct effect of institutional isomorphic pressures on ERP adoption was
found to be non-zero in the presence of the mediating factor, it can be
understood that the perceived benefits do not completely mediate, but
partially mediate the effect. This can be termed as that the mediator
complements the effect of IV on DV.
H10
H100
Perceived benefits will not mediate the
institutional isomorphic pressures towards ERP
adoption
Rejected
H10a Perceived benefits will mediate the institutional
isomorphic pressures towards ERP adoption Accepted
4.7.1.2 Test for simple mediating effect of perceived challenges
The independent mediator model, with the perceived challenges
as the mediator between institutional isomorphic pressures and ERP
adoption was tested and the results are presented in Table 4.26.
The regression coefficient of the direct effect of the institutional isomorphic
pressures on ERP adoption is B = 1.46. The total effect on ERP adoption
was found to be B = 2.084 and the weight of the indirect effect was
179
B = 0.624. The CMIN/DF = 1.729 at P < 0.001, TFI = 0.933, CFI = 0.938,
and RMSEA = 0.057 at P < 0.001 shows a goodness of fit of the model to
be within the acceptable standards. The model reflects the data to a high
degree of validity. One specific feature of this model is that the regression
weights of IV to MV and MV to DV were found to be statistically
significant and negative. The significance of the indirect effect was tested
by bootstrapping. The results are presented in Table 4.27. The regression
coefficients of all the relationship paths are found to be significant.
The perceived challenges are found to mediate the institutional isomorphic
pressures towards ERP adoption. The results of direct, indirect and the total
effects are found to be non-zero and significant. Therefore, the possibilities
of the values becoming zero in the population parameters are ruled out.
This reveals a complementary mediating effect of perceived challenges.
180
Table 4.26 Mediating effect of perceived challenges between
institutional isomorphic pressures and ERP adoption
Model
AMOS Results
Direct Effect B S.E C.R P Beta ERP Adoption
Perceived Challenge -1.555 0.255 -6.108 *** -0.426
Perceived Challenges Institutional isomorphic pressures -0.401 0.080 -5.028 *** -0.466
ERP Adoption Institutional isomorphic pressures 1.460 0.247 5.918 *** 0.465
Indirect Effect B Beta
ERP Adoption Institutional isomorphic pressures 0.624 0.199
Total Effect B Beta
ERP Adoption Institutional isomorphic pressures 2.084 0.663
Selected Fit Measures
CMIN DF CMIN/DF P
952.883 551 1.729 0.000
TLI CFI RMSEA PCLOSE
0.933 0.938 0.057 0.024
*** Significant at < 0.001 level
ERP Adoption
Perceived Challenges
Institutional Isomorphic Pressures
B= - 0.401, C.R = -5.028 P < 0.001
B= -1.555, C.R = -6.108, P < 0.001
R2 = 0.582 B=1.460, C.R = 5.918, P < 0.001
(Zero Effect: B = 2.072, C.R = 7.737, P < 0.001)
182
The findings do not support the null hypothesis (H110) of
proposition 11, which proposes that perceived challenges will not mediate
the institutional isomorphic pressures towards ERP adoption. The alternate
hypothesis that proposes that perceived challenges will mediate the
institutional isomorphic pressures towards ERP adoption is therefore
accepted. The type of mediation is again complementary because there is a
positive indirect effect and the direct effect is not zero.
H11
H110 Perceived Challenges will not mediate the institutional isomorphic pressures towards ERP adoption
Rejected
H11a Perceived Challenges will mediate the institutional isomorphic pressures towards ERP adoption
Accepted
4.7.1.3 Test for simple mediating effect of organisational complexity
The mediating effect of the organisational complexity between
the institutional isomorphic pressures and ERP adoption was individually
tested and the results are presented in Table 4.28. The results show that the
organisational complexity does mediate the effects of institutional
isomorphic pressures. The regression coefficient of the direct effect of
institutional isomorphic pressures on ERP adoption is B = 1.668 and the
indirect effect through the mediator variable is B = 0.457. The total effect
on ERP adoption is found to be B = 2.125. Analysing the goodness of fit of
the model, it is found out that the CMIN/DF = 2.242 at P < 0.001,
TFI = 0.946, CFI = 0.961 and RMSEA = 0.075 at P < 0.05. The value of
Chi square to the degree of freedom (CMIN/DF) is found to be slightly
above the normally acceptable standard. However, going by the argument
of Arbuckle (2007, pp. 587-590), the values are found to be within a
reasonable limit. Therefore, the model is accepted to represent the data and
183
deemed valid. The regression weights of IV to MV and MV to DV are
found to be statistically significant and positive.
Table 4.28 Mediating effect of organisational complexity between
institutional isomorphic pressures and ERP adoption
Model
AMOS Results
Direct Effect B S.E C.R P Beta ERP Adoption
Organisational Complexity 0.086 0.023 3.683 *** 0.258
Organisational Complexity Institutional isomorphic pressures 5.338 0.791 6.748 *** 0.555
ERP Adoption Institutional isomorphic pressures 1.668 0.293 5.689 *** 0.522
Indirect Effect B Beta
ERP Adoption Institutional isomorphic pressures 0.457 0.143
Total Effect B Beta
ERP Adoption Institutional isomorphic pressures 2.125 0.665
Selected Fit Measures
CMIN DF CMIN/DF P
89.691 40 2.242 0.000
TLI CFI RMSEA PCLOSE
0.946 0.961 0.075 0.026 *** Significant at < 0.001 level
ERP Adoption Institutional Isomorphic Pressures
B=1.668, C.R = 5.689, P < 0.001 (Zero effect: B = 2.072, C.R = 7.737, P < 0.001)
R2 = 0.489
Organisational Complexity
B=0.086, C.R = 3.683, P < 0.001
B=5.338, C.R =6.748 P < 0.001
185
The boot strapping results are analysed for the significance of the
indirect effect. Table 4.29 shows the bootstrapped results. The regression
coefficients of all the relationship paths are found to be significant.
The results of direct, indirect and the total effects are found to be non-zero
and significant. However a closer look at the unstandardised regression
weight of the organisational complexity to ERP adoption (B = 0.086) shows
that it is very close to zero. Even the lower bound value (0.013) of the
bootstrapped sample is very close to zero. There is a tendency for the value
to become zero and not to mediate the effect. However, the current study
suggests that there is a mediating effect of organisational complexity in
ERP adoption.
The findings do not support the null hypothesis (H120) of the
proposition 12, which proposes that the organisational complexity will not
mediate the institutional isomorphic pressures towards ERP adoption.
The alternate hypothesis that proposes that organisational complexity will
mediate the institutional isomorphic pressures towards ERP adoption is
therefore accepted. The type of mediation is again complementary because
there is a positive indirect effect and the direct effect is not zero.
H12
H120
Organisational complexity will not mediate the
institutional isomorphic pressures towards ERP
adoption
Rejected
H12a
Organisational complexity will mediate the
institutional isomorphic pressures towards ERP
adoption
Accepted
186
4.7.1.4 Test for simple mediating effect of between mediating
variables: Perceived challenges on organisational complexity
and ERP adoption
The conceptual model proposes a relationship between the
mediating variables. Their interaction can have an intervening effect on the
outcome variable in the model. The analysis may be of interest when
interpreting the specific indirect effect in the multiple mediator model.
Therefore, the intervening effect of one mediator on the other mediator
variable was analysed and the results are presented in Tables 4.30 to 4.33.
First, the influence of the organisational complexity on ERP
adoption mediated by the perceived challenges was tested (Table 4.30).
The regression coefficient of the direct effect of organisational complexity
on ERP adoption is B = 0.115 and the indirect effect through the mediator
variable is B = 0.067. The total effect on ERP adoption was found to be
B = 0.182. Their significance is also verified by results of bootstrapping and
found valid (Table 4.31). Analysing the goodness of fit of the model, it was
found that the CMIN/DF = 2.030 at P < 0.001, TFI = 0.933, CFI = 0.939
and RMSEA = 0.068 at P < 0.001. The results show that there is a
mediating effect of perceived challenges on ERP adoption. However, the
relationships between organisational complexity and the perceived
challenges and that of perceived challenges and adoption of ERP were
found to be negative and statistically significant. This is interpreted as the
organisational complexity reduces the perceived challenges and the
perceived challenges reduce the adoption of ERP. Therefore, the reduced
perceived challenges do not pose a barrier to ERP adoption and thus
supports the ERP adoption when the organisational processes are complex.
The goodness of fit of the model is also found valid with the data.
187
Table 4.30 Mediating effect of perceived challenges between
organisational complexity and ERP adoption
Model
AMOS Results
Direct Effect B S.E C.R P Beta
ERP Adoption Perceived Challenges -1.842 0.251 -7.351 *** -0.505
Perceived Challenges Organisational Complexity -0.036 0.007 -5.494 *** -0.399
ERP Adoption Organisational Complexity 0.115 0.018 6.534 *** 0.347
Indirect Effect B Beta ERP Adoption Organisational Complexity 0.067 0.201
Total Effect B Beta ERP Adoption Organisational Complexity 0.182 0.548
Selected Fit Measures
CMIN DF CMIN/DF P
647.488 319 2.030 0.000
TLI CFI RMSEA PCLOSE
0.933 0.939 0.068 0.000
*** Significant at < 0.001 level
ERP Adoption
Perceived Challenges
B = 0.115, C.R = 6.534, P < 0.001 (Zero effect B = 0.182, C.R = 9.735, P < 0.001)
B = -0.036, C.R = -5.494 P < 0.001
R2 = 0.514 Organisational
Complexity
B = -1.842, C.R = -7.351, P < 0.001
189
4.7.1.5 Test for simple mediating effect of between mediating
variables: Perceived benefits on perceived challenges and
ERP adoption
The mediation of perceived benefits to the relationship between
perceived challenges and ERP adoption is also tested within the nested
model to examine the specific indirect effect. The results (Table 4.32) show
a peculiar model in which the regression estimate for path between the
perceived challenges and the perceived benefits is significant and negative
(B = -0.446, CR = -5.396, P < 0.001). The regression weight for the path
between the perceived benefits and ERP adoption is significantly positive
(B = 2.015, CR = 6.602, P < 0.001). However, the direct effect (B = -1.446,
CR = -6.153, P < 0.001), indirect effect (B = -0.898) and the total effect
(B = -2.344) are negative. The significance of the indirect effect and total
effect that are verified from bootstrapping are also found to in the non-zero
range.
The results can be interpreted to state that the higher perceived
challenges reduce the perceived benefits and also the adoption of ERP,
whereas, higher perceived benefits will increase the adoption of ERP.
The direct effect is non-zero indicating a mediation effect, but is a
competitive one. This empirically clarifies the obvious relationship between
the perceived benefits and perceived challenges, which tend to be usually
opposite. The model shows that the negative effect of perceived challenges
can be modified by perceived benefits. This is indicated in the reduction of
the direct effect of perceived challenges on ERP adoption. The goodness of
fit of the model, CMIN/DF = 1.692 at P < 0.001, TLI = 0.902, CFI = 0.906
and RMSEA = 0.056 at P < 0.005 suggests a good fit of the model to the
190
data. The bootstrapped values also indicate the significance of the
parameters to the simulated population (Table 4.33).
Table 4.32 Mediating effect of perceived benefits between
perceived challenges and ERP adoption
Model
AMOS Results
Direct Effect B S.E C.R P Beta ERP Adoption
Perceived Benefits 2.015 0.305 6.602 *** 0.496
Perceived Benefits Perceived Challenges -0.446 0.083 -5.396 *** -0.497
ERP Adoption Perceived Challenges -1.446 0.235 -6.153 *** -0.397
Indirect Effect B Beta ERP Adoption Perceived Challenges -0.898 -0.246
Total Effect B Beta ERP Adoption Perceived Challenges -2.344 -0.643
Selected Fit Measures
CMIN DF CMIN/DF P 2679.394 1584 1.692 0.000
TLI CFI RMSEA PCLOSE 0.902 0.906 0.056 0.004
*** Significant at < 0.001 level
ERP Adoption
Perceived Benefits
Perceived Challenges
B=2.015, C.R = 6.602, P < 0.001
R2 = 0.599 B= -1.446, C.R = -6.153, P < 0.001
(Zero Effect: -2.345 C.R= -8.474, P<0.001)
B= -0.446, C.R =-5.396, P < 0.001
192
4.7.2 Test for Multiple Mediation Effect
Ultimately, the multiple mediator model was tested for the effect
of simultaneous mediation. The direct, indirect and total effect option in the
AMOS is used and the results are presented in the Table 4.34.
Table 4.34 Mediating effect of perceived benefits and perceived
challenges on institutional isomorphic pressures
towards ERP adoption
ERP Adoption Institutional isomorphic pressures B Beta
Direct effect 0.857 0.263
Indirect Effect 1.363 0.418
Total Effect 2.220 0.680
The direct effect of institutional isomorphic pressures on ERP
adoption (B = 0.857) was positive and non-zero showing a partial
mediation effect. The indirect effect of institutional isomorphic pressures
through the mediators (Bc- = 1.363) was positive showing a complementary
mediation. The total effect of the institutional isomorphic pressures on ERP
adoption (Bc = 2.22) shows a positive effect that the ERP adoption will
increase when the institutional isomorphic pressures is supported by the
motivating factors (Mediating variables).
The significance of the indirect effect was verified by
bootstrapping the samples (Table 4.35). The results show that the direct,
indirect and total effects were positive and statistically significant because
the lower bound and upper bound values did not have the zero coordinate
between them. It can be interpreted that the chance of these variables
becoming zero is not possible.
194
The multiple mediator model with regression coefficients and the
mediation effect is shown in the Figure 4.8.
Significance levels : * P < 0.05, ** P<0.005, *** P<0.001
Figure 4.8 The multiple mediator model with unstandardised
regression coefficients
Since AMOS was not capable of producing the results for the
specific indirect effect and verify its statistical significance. The specific
indirect effect was calculated by the product of coefficient method.
Table 4.36 presents the specific indirect effect of each nested path. The sum
ERP adoption R2 = 0.671
Perceived Challenges
Institutional Isomorphic Pressures
Perceived Benefits
Organisational Complexity
1.321**
-0.017 *
0.045 *
-0.327*** - 1.105 **
ERP adoption R2 = 0.671
Institutional Isomorphic Pressures
Total 2.22*** Indirect 1.363***
0.857*
5.696***
0.377 ***
-0.249 **
195
of the indirect effects of all possible paths are summed up and found to be
almost equal to the value of indirect effect from the results of SEM.
To calculate the significance of the specific indirect effects the Selig and
is used (Figure A 3.1). The path
coefficients and the standard error values of the paths are fed in a web
based macro with a bootstrapping simulation of 20000 samples at 95% CI.
The lower bound values and the upper bound value are presented in
Table 4.36. The significance of the paths having more than one intervening
variables were not tested.
Table 4.36 Specific indirect effect of each mediator in a multiple
mediation model
The Path Product of Coefficients
LL 95%
UL 95%
Institutional isomorphic pressures Perceived Benefits ERP adoption 0.498 0.231 0.837
Institutional isomorphic pressures Perceived Challenges ERP adoption 0.361 0.134 0.645
Institutional isomorphic pressures Organisational Complexity ERP adoption 0.256 0.035 0.489
Institutional isomorphic pressures Perceived Challenges Perceived Benefits ERP adoption
0.108
Institutional isomorphic pressures Organisational Complexity Perceived Challenges ERP adoption
0.107
Institutional isomorphic pressures Organisational Complexity Perceived Challenges Perceived Benefits ERP adoption
0.032
Total indirect effect 1.362
196
The findings do not support the null hypothesis (H130) of
proposition 13, which states that the mediating factors will not influence the
ERP adoption, when present together. Therefore, the alternate hypothesis,
which proposes that the mediating factors will influence the ERP adoption
when present together is therefore accepted.
H13
H130
Perceived benefits, perceived challenges and organisational complexity together will not mediate institutional isomorphic pressures towards ERP adoption
Rejected
H13a
Perceived benefits, perceived challenges and organisational complexity together will mediate institutional isomorphic pressures towards ERP adoption
Accepted
4.7.3 Cross validation of results using Partial Least Square
method (PLS)
WarpPLS 3.0 utilises the PLS method for path analysis. This
software also allows users to test mediating effects directly through
inspection of coefficients generated for indirect and total effects, which
include P values. This allows for the direct test, without having to resort to
intermediate calculations (e.g., Baron and Kenny; Preacher and Hayes), of
mediation of various levels of complexity (e.g., multiple
mediation). WarpPLS also calculates total effects and respective P values,
in addition to indirect effects. All of these are calculated whether linear or
nonlinear analyses are conducted (Kock 2012). To cross validate the results
of AMOS, WarpPLS 3.0 was used to test the multiple mediation model.
The results are presented in tables 4.37 to 4.40. The analysis of the path
coefficients show a significant relationship (P<0.001) between all the
constructs. The coefficient values for relationships that involve perceived
197
challenges is found to be negative and all other relations are positive (Table
4.37).
Table 4.37 Path Coefficients found using PLS technique
The Path B S.E P ERP Adoption Institutional isomorphic pressures 0.230 0.067 ***
Perceived Benefits Institutional isomorphic pressures 0.337 0.069 ***
Perceived Challenges Institutional isomorphic pressures -0.302 0.067 ***
Organisational Complexity Institutional isomorphic pressures 0.554 0.055 ***
ERP Adoption Perceived Benefits 0.303 0.064 ***
ERP Adoption Perceived Challenges -0.320 0.065 ***
ERP Adoption Organisational Complexity 0.193 0.059 ***
Perceived Challenges Organisational Complexity -0.264 0.078 ***
Perceived Benefits Perceived Challenges -0.356 0.007 ***
The model statistics is presented in the table 4.38. The RSquare
value of the full model is found to be 0.703, which is slightly higher than
the value found in the covariance technique using AMOS. The RSquare for
influence on perceived benefits, challenges and organisation complexity are
also higher than 0.25 indicating a sizable impact of institutional isomorphic
pressure on these factors. The composite reliability of the constructs is
above 0.809 and the collinearity factors (VIF) are well below the threshold
limit.
198
4.38 Model statistics in PLS
R Square Composite reliability Full VIFs
ERP Adoption 0.703 - 2.723
Perceived Benefits 0.352 0.910 1.840
Perceived Challenges 0.252 0.925 1.613
Institutional Isomorphic Pressures - 0.812 1.595
Organisational Complexity 0.307 0.809 1.663
The table 4.39 reports the correlation of the latent constructs. All
values are found to be significant at P<0.001 level.
Table 4.39 Correlation matrix of latent Constructs
ERP Adoption 1 0.593 -0.611 0.562 0.655
Org. Complexity 0.593 0.610 -0.402 0.495 0.480
Perceived Challenges -0.611 -0.402 0.869 -0.396 -0.446
Institutional Isomorphic Pressure 0.562 0.495 -0.396 0.771 0.480
Perceived Benefits 0.655 0.480 -0.446 0.480 0.818
Note: Square roots of average variances extracted (AVE's) shown on
diagonal. All values are significant at P<0.001.
199
The results show a moderate relationship between the constructs.
The estimated correlations between the latent construct are important to
highlight the necessity of theory-driven structural analysis. High correlation
between latent constructs could indicate redundancy or shared sources of
variance. The results rule out the possibility of common method variance.
Table 4.40 presents the results of indirect and total effects using PLS. The
results show a significant indirect effect (P<0.001), which proves the
mediating effect in the model.
Table 4.40 Results of indirect and total effect using PLS
The Path Product of Coefficients P SE
Institutional isomorphic pressures Perceived Benefits ERP adoption
0.306 *** 0.042 Institutional isomorphic pressures Perceived Challenges ERP adoption
Institutional isomorphic pressures Organisational Complexity ERP adoption Institutional isomorphic pressures Perceived Challenges Perceived Benefits
ERP adoption 0.079 *** 0.018
Institutional isomorphic pressures Organisational Complexity Perceived Challenges ERP adoption Institutional isomorphic pressures Organisational Complexity Perceived Challenges Perceived Benefits ERP adoption
0.016 *** 0.007
Sum of indirect effect 0.401 *** 0.045
Total Effect ( Direct + Indirect effect) 0.631 *** 0.052
200
4.8 RESULTS OF HYPOTHESES TESTING
The summary of the results of the hypotheses testing is presented
in Table 4.41.
Table 4.41 The summary of results of the hypotheses testing
Hypothesis Results
H1 H0 Institutional isomorphic pressures will not influence the ERP adoption Rejected
H2 H0 Institutional isomorphic pressures will not influence the Perceived benefits Rejected
H3 H0 Institutional isomorphic pressures will not influence the Perceived challenges Rejected
H4 H0 Institutional isomorphic pressures will not influence the organisational complexity Rejected
H5 H0 Perceived benefits will not influence the ERP adoption Rejected
H6 H0 Perceived challenges will not influence ERP adoption Rejected
H7 H0 Organisational complexity will not influence ERP adoption Rejected
H8 H0 Organisational complexity will not influence Perceive challenges Rejected
H9 H0 Perceived challenges will not influence perceived benefits Rejected
H10 H0 Perceived benefits will not mediate the institutional isomorphic pressures towards ERP adoption
Rejected
201
Table 4.41 (Continued)
Hypothesis Results
H11 H0 Perceived challenges will not mediate the institutional isomorphic pressures towards ERP adoption
Rejected
H12 H0 Organisational complexity will not mediate the institutional isomorphic pressures towards ERP adoption
Rejected
H13 H0
Perceived benefits, perceived challenges and organisational complexity together will not mediate institutional isomorphic pressures towards ERP adoption
Rejected
4.9 SUMMARY
This chapter has presented the data analyses and results
of the test measurements. Confirmative factor analysis of the measurement
models were tested for discriminant and convergent validity. Then the
reliability of the constructs were assessed and found acceptable. In the next
stage, the structural model was analysed for regression weights. In the third
stage, the mediation effects were tested for independent mediators and with
all the mediators together. The hypotheses were verified.
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