chapter 4 data analysis and...

124

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

125

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).

126

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

127

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

128

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

129

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.

130

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

131

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


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

132

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

133


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


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

134


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

135

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

136

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

137

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.

138

(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

139


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.

140

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

141

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


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

142

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


57.21 24 0.000 2.384


0.953 0.969


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.


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


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


Operational Benefits

Reduces Operational Cost 1.000 .796

Provides Continuously Improved Plan

.872 .063 13.955 *** .817

145



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



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


Summary of second order loading of perceived benefits


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


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


863.343 459 0.000 1.881


.926 .931


0.063 0.057 0.070 0.001

148

Figure 4.3 Hypothesised second-order CFA model of perceived benefit


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.


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


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.

150

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


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

151



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

152


Summary of second order loading of perceived challenges


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


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


527.804 271 .000 1.948


.944 .950


.065 .057 .074 .001

153

Figure 4.4 Hypothesised second-order CFA model of perceived challenges


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.


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


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

155

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

156

-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

157

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.

158

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

159

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.

160

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

162




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


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


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

166

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

167

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


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


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

168

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.

169

H2


influence the perceived benefits Rejected


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


influence the perceived challenges Rejected


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


influence the organisational complexity Rejected


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

171

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

173

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.

175

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 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



CMIN DF CMIN/DF P 1488.828 809 1.84 0.000

TLI CFI RMSEA PCLOSE 0.902 0.908 0.062 0.000


ERP Adoption

Perceived Benefits


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


Model

AMOS Results


Perceived Challenge -1.555 0.255 -6.108 *** -0.426

Perceived Challenges Institutional isomorphic pressures -0.401 0.080 -5.028 *** -0.466




Total Effect B Beta



CMIN DF CMIN/DF P

952.883 551 1.729 0.000


0.933 0.938 0.057 0.024


ERP Adoption



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


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


Model

AMOS Results


Organisational Complexity 0.086 0.023 3.683 *** 0.258

Organisational Complexity Institutional isomorphic pressures 5.338 0.791 6.748 *** 0.555




Total Effect B Beta



CMIN DF CMIN/DF P

89.691 40 2.242 0.000


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


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


adoption

Rejected

H12a

Organisational complexity will mediate the


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


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


CMIN DF CMIN/DF P

647.488 319 2.030 0.000


0.933 0.939 0.068 0.000


ERP Adoption


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



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


CMIN DF CMIN/DF P 2679.394 1584 1.692 0.000

TLI CFI RMSEA PCLOSE 0.902 0.906 0.056 0.004


ERP Adoption

Perceived Benefits


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




Perceived Benefits


1.321**

-0.017 *

0.045 *

-0.327*** - 1.105 **



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


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


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.

chapter 4 data analysis and...

Documents