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The Relationship between the Enterprise Resource Planning (ERP)
Systems Internal Control Procedures
Hani Shaiti
University of Bedfordshire & King Faisal University
Yanqing Duan Syamarlah asaratnam
University of Bedfordshire University of Bedfordshire
Abstract
There is a general perception that the effective implementation of an adequate internal control
system (ICS) will normally lead to improved operations and performance. IC is a process which is
designed to enhance operational efficiency, promote the reliability of financial information, and
ensure compliance with the applicable laws and regulations. In recent years, IC has become the
focus of attention every time a notable scandal has arisen in the corporate world. An effective ICS
can prevent an organisation from committing fraud and errors, and also provide it with assurance
and competitive advantages. Some researchers argue that, in order to have a robust IC system, an
integrated system, such as an ERP system, is needed.
This research aims to develop a contingency framework for an IC system by operationalising the
support of a successful ERP system. In order to achieve the research aim, three hypotheses have
been identified, which were derived from the existing literature and tested in a quantitative study.
A questionnaire has been developed (containing four parts) and sent to 213 companies that have
implemented ERP in Saudi Arabia. A hundred valid responses were received. Partial Least Squares
Structural Equation Modelling (PLS-SEM) was adopted for the data analysis and hypotheses
testing. The results suggest that the organisational structure, prospectors’ strategy, organisational
culture and management support can help to explain the effectiveness of ICPs.
Introduction There is a general perception that the enforcement and application of a proper internal control
system (ICS) will normally improve an entity’s operations and performance. Internal control is a
crucial feature of an organisation’s governance system and is essential for supporting the
achievement of an organisation’s objectives.
In recent years, Internal Control (IC) has become the focus of attention every time there is a
notable scandal in the corporate world. For instance, Enron, WorldCom and Tyco in the US,
Parmalat, and Ahold in Europe have faced a breakdown in their ICS. It is an obligatory task for an
entity’s management to improve its ICS. An effective ICS can prevention an organisation from
committing fraud and errors, and provide it with assurance and a competitive advantage.
Some researchers argue that, in order to have a robust IC system, an integrated system, such as an
ERP system, is needed (Huang et al., 2008). ERP systems have the ability to control user access
and facilitate the separation of duties, which is one of the most common IC mechanisms used in
order to deter fraud within financial systems. Moreover, there are other factors that can provide
support to ensure that an IC system is effective, although the research in this area remains scarce
(Morris, 2011).
Therefore, to address this gap, it is important to investigate the factors that can improve an ICS.
For this purpose, this study will be guided by “the Contingency Theory”, which is built on the
argument that there is no one best way to organise an organisation, so the optimal course of action
depends on external or internal variables. A better organizational performance depends on a better
match between the (internal) control system and contingent factors (Fisher, 1998). The remainder
of this paper presents the literature review, theoretical framework and hypothesis development,
research method, results and discussion as well as the conclusion to the study.
Literature Review There has been a little empirical and archival research on the area of ERP systems. Kumar et al.
(2008) investigated the challenges facing an organization regarding the compliance of their ERP
systems to the IC requirements, particularly those imposed by the SOX Act. They found that the
companies faced technical, process-related, and cultural barriers regarding these requirements.
Maurizio et al. (2007) indicated the need for fully integrated systems like ERP systems to prevent
interruption to the data flow.
ERP vendors (e.g. SAP, Oracle) have taken advantage of the improvement in IC regulations by
updating the system’s reports and features. ERP systems can produce control and exception reports
which help to improve the monitoring and the segregation of duties (Turner and Owhoso, 2009).
ERP systems can provide ICPs with the tools for gathering, analyzing and reporting information
(Kumar et al., 2008). Researchers have found that companies using IT systems reported fewer IC
weaknesses than those that have not adopted IT systems (Klamm & Watson, 2009; Morris,
2009). Morris (2011) argued that the “built-in control” features and other features that ERP
systems have can help an organization to improve its ICPs and reduce its IC weaknesses.
ERP systems are able to support other frameworks such as ERM and COSO frameworks (Brown
and Caylor, 2006). ERM is a framework that can manage and reduce the different types of risk.
Ramamoorti and Weidenmier (2006) stated that the technology is associated with all of the COSO
ERM frameworks’ components. Chang and Jan (2010) designed an ERP IC framework using
COSO components and other items. They stated that the framework can help the shareholders,
managers, and auditors to assess the effectiveness of ICPs.
Authors The Model dimensions contributions limitations
DeLone
&
McLean
(1992)
Informatio
n System
Success
Model
- System Quality
- Information Quality
- Use
- User satisfaction
- Individual Impact
-Organisational
Impact
-Measure the IS
success in different
levels
-Classification for
multitude of IS
evaluation.
-Different
stakeholders
evaluating the IS
success
- Did too much
-Inappropriateness
of the use as a
dimension.
-Overlapped of user
satisfactions
Myers,
Kappelm
-an &
Prybutok
(1997)
Comprehe-
nsive
Model for
Assessing
the ISF
-DeLone & McLean
(1992) 6components
-Service Quality
- Work Group
Impact,
-Consider the overlap
-Describes how
measures can be
determine.
-Contengency factors
-Carried some
DeLone & McLean
model problems
Gable,
Sedera &
Chan
(2003)
ERP
Success
Measurem-
ent Model
-System Quality
-Information Quality
-Individual Impact
-Organisation
Impact,
-First assessed the
ERP success and
tested.
- Eliminated two of
DeLone and
McLean’s
dimensions
-Complete set of
tested ERP measures.
-Tested data only
from Australian
public
organisations, only
SAP.
-Ignoring the
Service Quality
dimension.
Although the academic literature covers ERP systems and IC, it has paid too little attention to
studying the impact of ERP systems’ success on IC (Huang et al., 2008). According to Al-Mashari
(2003), many companies that have implemented ERP have failed to achieve the system’s estimated
benefits (e.g. cost reduction). It has been argued that companies should assess the value of the
system (Heo and Han, 2003). Basically, assessing ERP success refers to evaluating the
performance of an ERP system post-implementation. Gable et al. (2003) defined an ERP system’s
success as the utilisation of the system to achieve the organisational goals. The literature includes
several IS (ERP) success models, such as that of DeLone and McLean (1992); Myers et al. (1997)
and Gable et al. (2003) (see Table 1).
Table 1: the IS and ERP success models
Theoretical Development and Hypotheses Formulation The theoretical framework helps to determine the study’s key variables, the type of the
relationships that link the key variables and the theoretical assumptions of the related theory. It
also provides a basis for the study’s hypotheses and the selection of appropriate research method(s)
in order to address the research question(s). The theoretical framework is based on the contingency
theory. The contingency theory of an organisation is a significant theoretical lens that can be used
to view the organisation (Donaldson, 2001). It has been structured by theorists such as Burns and
Stolker (1961), Perrow (1970) and Galtung (1967) within the period of organisational theory in the
early 1960s. Basically, the term contingency means that something is true only under specific
conditions.
There has been a history of more than three decades of published research about contingency
theory and the different aspects of management accounting practices as well as information
systems (Chenhall, 2007; Otley, 1980; Hong and Kim, 2002). Otley (1980) proposes the
application of the contingency approach to management accounting practices, as “there is no
universal appropriate control system which applies to all organisations in all circumstance”
(pp413).
The core of the structural contingency theory model is that organisational effectiveness (i.e. ICPs’
effectiveness, in this study) results from fitting the organisational characteristics, such as
organisational systems, to contingencies that reflect the situation of the organisation (Donaldson,
2001). In other words, contingencies influence the effect of the organisational characteristics on the
organisational effectiveness. Thus, contingency theory can be used to examine the impact of the fit
between ERP systems and the contingency variables, such as the organisational structure, strategy
or size, on the effectiveness of ICPs.
There are several forms of theoretical fit, according to Chenhall (2007), that have been utilised to
categorise the contingency-based research in the field of management accounting systems,
including the selection approach, system approach, fit (congruence and interaction) approach,
intervening variable approach and, more recently, the structure equation models (SEM) approach
(such as PLS-SEM). This study uses the SEM approach, which can predict the relationships
between contingencies, the success of an ERP system and the effectiveness of ICPs, and analyse
the effects into direct and indirect ones through the success of ERP systems (Smith and Lagfield-
Smith, 2004; Hall and Smith, 2009; Elbashir et al., 2011).
Figure 1 the Theoretical Framework
-Contingency Variables
The literature suggests that a number of contingency variables may affect the relationship between
the ERP system and the effectiveness of ICPs, including the external environment, organisational
structure, size, organisational strategy, management support and organisational culture (Otley,
1980; Gable et al., 2003; Rae and Subramaniam, 2008).
This study found, from the literature, sufficient clues that suggest that there exist a number of
important contingency variables when considering the relationship between an ERP system and the
effectiveness of ICPs, including the strategy, structure, size, organisational culture and
management support. Appendix 1 shows the measures of the study’s constructs.
-Hypotheses
In order to investigate the relationship between ERP systems and IC effectiveness and to
understand the relationships between the variables and the study constructs, a theoretical
framework was developed (Figure 1). The theoretical framework is based on three main
relationships, which are illustrated by the three main hypotheses as follows:
Structure,
strategy, size,
support,
culture
ERP Maturity,
Brand, Year
Implementati
on
Effectivene
ss of ICPs
Success of
ERPs
Organisational determinants
ERPs determinants
1. Contingency Variables and ERP systems’ success
Myers et al. (1997) indicate that considering contingency theory will improve the quality and
productivity of the IS functions in order better to meet the need of an entity. A review of the
literature reveals that the organisational structure, strategy, size, management support,
organisational culture, and maturity of an ERP system affect its success. Within this group, there
are twelve hypotheses, including two mediation hypotheses. They are illustrated as follows:
H1: Organisational structure is positively associated with ERP system success
H2: There is a positive association between prospector strategy and ERP success.
H3: There is a positive relationship between organisational size and ERP success.
H4: Organisational culture positively influences the ERP success.
H5: There is a positive correlation between top management support and ERP success.
H6: There is a relationship between the ERP brand and ERP success.
H6a: There is indirect relationship between ERP brand and ERP success.
H7: The years of ERP implementation are positively associated with ERP success.
H7a: The years of ERP implementation are indirectly associated with ERP success.
H8: There is a positive correlation between the maturity of ERP functions and ERP success.
H9: The ERP brand is negatively associated with the maturity of the ERP functions.
H10: There is a positive relationship between the years of ERP implementation and the maturity of the
ERP functions.
2. Contingency Variables and the effectiveness of ICP
Several studies have investigated the relationship between the number of contingency variables and
the management control systems (Otley, 1999; Chenhall, 2007; Abdel-Kader and Luther, 2008). As
a result of reviewing the literature, the researcher came up with five important contingency factors,
including: organisational structure, strategy, size, management support, and organisational culture,
which may be related to the effectiveness of ICPs. Within this group, there are five hypotheses,
which are structured as follows:
H11: Organisational structure is associated with ICP effectiveness.
H12: There is a relationship between the strategy prospectors and ICP effectiveness.
H13: A large organisation is positively associated with ICP effectiveness.
H14: There is a positive relationship between organisational culture and ICP effectiveness.
H15: There is a positive relationship between management support and ICP effectiveness.
3. ERP system success and ICP effectiveness
This group of hypotheses contain the main hypothesis of this research, which is the relationship
between the success of ERP systems and ICP effectiveness. Little empirical research related to the
area of ERP systems and IC has been published (Huang et al., 2008). For instance, Gupta and
Kohli (2006) investigated the benefits of ERP systems and found that SAP R/3 integrates the
processes, data, and firm elements and units within a single software package. This tight
integration feature can defend the system’s source code. Morris (2011) argues that the “built-in
control” features and other features of ERP systems can help an organization to improve its
internal control procedure. The study finds that companies that have adopted ERP systems are
reporting less internal control material weaknesses than companies that have not adopted them.
Additionally, an ERP system can play a mediation role in influencing the effectiveness of
ICPs. Therefore, there is a direct and indirect relationship between the success of ERP systems,
the contingency variables and ICP effectiveness. Accordingly, it can be hypothesised that:
H16: ERP success is positively associated with ICP effectiveness.
H16a: There is an indirect relationship between the contingency variables and ICP effectiveness.
Research Method The aim of this section is to illustrate how to achieve the study objectives. The pathway of the
research should be supported by a research philosophy. No one research philosophy is better than
another. The question of which is ‘better’ depends on how the researcher seeks to answer the study
question(s) (Saunders et al., 2009). This study will adopt the positivist paradigm that will involve
collecting quantitative data in order to address the research questions. This paradigm depends on
the assumption that the social reality is more objective and includes unbiased decisions. Because
the positivist study measurement is an essential element of the research process, the data are highly
specific and precise, and so the findings tend to be more reliable (Collis and Hussey, 2009).
Under the positivist paradigm, research is deductive. Therefore, the research was started by
developing the theoretical structure including the three main hypotheses after reviewing the
literature. The survey approach was used to collect the main data and test the study hypotheses.
-Sample and Data Collection
All Saudi companies that were found to have implemented an ERP system were included in the
study sample. However, since there was no existing database of the ERP system population relative
to the field of study, Saudi Arabia, various sources of data were used in the current study. These
include some experimental studies on Saudi Arabian companies (such as Al-Muharfi, 2010; Al-
Turki, 2011). The research contacted some of the ERP system vendors in Saudi Arabia (e.g. SAP
and Oracale) to gain the names of the companies that have implemented an ERP system in Saudi
Arabia. Additionally, some websites were used; for example, those of the top international ERP
systems’ vendors, the Minister of Commerce and Industry, and certain Saudi’s companies. Finally,
a list of the 213 companies that have implemented an ERP system in Saudi Arabia was created.
For the data collection, an initial questionnaire was developed, based on the literature review. The
questionnaire contains four sections. The first section measures the effectiveness of the internal
control procedures (EICPs). The second section measures ERP success. The third section
investigates the impact of CVs. The last section includes demographic questions. The questionnaire
was distributed to the study sample, and 110 valid responds were received (with a response rate of
52%). After collecting the empirical data, they were analysed using two types of software: SPSS
and Partial Least Squares (PLS).
-Partial Least Squares
PLS is an approach to SEM that has been utilised for many years, especially in psychology and
social sciences researches, including many business researches (e.g. Fornell and Larcker, 1981)
and information systems researches (e.g. Vinzi et al., 2010). Regardless of the increased use of
PLS path modelling in other business disciplines (e.g. marketing), the popularity of PLS-SEM (and
other SEM modelling techniques) in the accounting discipline has slowly increased (Lee et al.,
2011). There are a few empirical studies in the field of accounting that have used PLS-SEM (e.g.
Hall 2008; Hall and Smith, 2009; Elbashir et al., 2011). According to Lee et al. (2011), the
unwillingness to use PLS-SEM in accounting research may be due to a lack of understanding about
its benefits and how to use it. PLS-SEM is a latent variable modelling technique that provides a
good opportunity for path modelling to move forward without being limited to restricted
assumptions, such as normality and a large sample size (Hall, 2008).
Results
-The Measurement Model
Before assessing the significance of the study’s variables’ relationships, it is an important to assess
the measurement model. First, the research removes all of the cases and indicators that have over
10% of missing data. Thus two cases and six indicators (two related to internal control, three to
ERP success and one to the maturity of the ERP system) were removed. Second, the factor loading
for each indicator was examined. All items load onto their respective constructs; however, seven
items (CA1, Serv.Q, Strategy6, OC4, OC5, MS2 and Maturity1) have factor loadings of less than
0.5 (Hulland, 1999), which ranks them between 0.469 for Maturity1 to 0.44 for MS2. A low factor
loading adds a few values to the model explanation, so these were removed (Hulland, 1999; Hair et
al., 2010). Table 2 shows the factor loading of the indicators.
Table 2 the Correlation Matrix of the Constructs and Indications
Brand Culture Dec. EICPs ERP Form. MS Maturity Size Strategy Team-based
Year Imp
Brand 1 -0.175 0.022 -0.179 -0.105 -0.011 -0.074 -0.186 -0.404 -0.057 0.004 0.014
Culture1 -0.233 0.838 0.269 0.486 0.29 0.435 0.441 0.15 0.097 0.339 0.357 -0.012
Culture2 -0.228 0.793 0.272 0.496 0.194 0.394 0.418 0.245 0.06 0.419 0.394 -0.038
Culture3 -0.114 0.788 0.501 0.497 0.266 0.396 0.527 0.15 -0.005 0.486 0.491 0.110
Culture6 0.051 0.636 0.418 0.461 0.222 0.299 0.623 0.166 -0.136 0.485 0.422 0.136
Structure3 0.022 0.477 1 0.511 0.238 0.549 0.609 0.201 -0.082 0.447 0.568 0.106
IE -0.119 0.483 0.468 0.739 0.221 0.492 0.588 0.327 -0.03 0.586 0.385 -0.020
RA -0.06 0.486 0.41 0.726 0.237 0.496 0.433 0.144 -0.019 0.516 0.327 0.096
CA -0.192 0.552 0.481 0.873 0.32 0.624 0.58 0.312 0.092 0.659 0.407 -0.001
InfCo -0.167 0.483 0.237 0.775 0.495 0.372 0.374 0.358 0.009 0.427 0.267 0.045
M -0.154 0.467 0.376 0.788 0.519 0.338 0.539 0.419 0.088 0.47 0.323 -0.006
SQ -0.147 0.281 0.232 0.485 0.893 0.358 0.259 0.564 0.154 0.314 0.308 0.144
IQ -0.071 0.211 0.089 0.354 0.870 0.298 0.111 0.515 0.174 0.256 0.193 0.083
IndIm -0.051 0.29 0.24 0.37 0.863 0.286 0.282 0.4 0.096 0.269 0.216 -0.074
OIm -0.077 0.316 0.257 0.329 0.804 0.279 0.178 0.486 0.081 0.266 0.182 0.164
Structure1 -0.014 0.375 0.402 0.423 0.185 0.807 0.313 0.198 0.081 0.521 0.345 0.045
Structure2 -0.007 0.481 0.535 0.596 0.399 0.926 0.489 0.154 0.026 0.577 0.425 0.041
MS1 -0.059 0.489 0.485 0.551 0.225 0.361 0.862 0.314 0.05 0.565 0.439 0.091
MS3 -0.183 0.553 0.494 0.537 0.187 0.381 0.848 0.291 0.056 0.512 0.460 0.038
MS4 -0.029 0.628 0.596 0.584 0.226 0.405 0.890 0.189 -0.078 0.473 0.525 -0.048
MS5 0.001 0.6 0.539 0.588 0.202 0.513 0.880 0.232 -0.098 0.537 0.537 0.020
Maturity2 -0.235 0.237 0.173 0.313 0.567 0.222 0.273 0.861 0.208 0.216 0.208 0.233
Maturity3 -0.154 0.184 0.091 0.288 0.499 0.011 0.203 0.860 0.138 0.117 0.137 0.257
Maturity4 -0.094 0.147 0.135 0.379 0.413 0.157 0.257 0.794 0.097 0.307 0.267 0.180
Maturity6 -0.102 0.182 0.276 0.364 0.403 0.261 0.235 0.781 0.053 0.234 0.233 0.279
Size -0.404 0.009 -0.082 0.039 0.15 0.054 -0.023 0.158 1 0.11 -0.006 0.288
Strategy1 -0.059 0.409 0.317 0.425 0.193 0.43 0.461 0.255 0.209 0.744 0.405 0.118
Strategy2 -0.077 0.416 0.321 0.595 0.291 0.525 0.448 0.187 0.173 0.891 0.341 0.042
Strategy3 -0.067 0.46 0.375 0.581 0.226 0.486 0.528 0.151 0.108 0.903 0.508 0.081
Strategy4 0.033 0.511 0.399 0.488 0.26 0.459 0.444 0.163 0.029 0.810 0.561 0.072
Strategy5 -0.062 0.559 0.452 0.73 0.357 0.694 0.615 0.31 -0.005 0.861 0.520 0.007
Structure4 0.004 0.543 0.568 0.441 0.267 0.448 0.565 0.25 -0.006 0.553 1 0.104
Year Imp 0.014 0.063 0.106 0.026 0.1 0.048 0.027 0.288 0.288 0.068 0.104 1
Third, the Cronbach’s alpha, Composite reliability and convergent validity were assessed. As
shown in Table 3, on the value of the Cronbach’s alpha, the composite reliability for all of the
constructs is above 0.7 (Hulland, 1999; Hair et al., 2010). Additionally, the Average Variance
Extracted (AVE) values for all constructs are more than 0.5 degrees (Fornell and Larcker, 1981).
Table 3 Summary of Cronbach’s alpha, Composite reliability and convergent validity
Construct AVE Composite Reliability
R Square
Cronbachs Alpha
Communality Redundancy
Brand 1 1 1 1
Year Imp 1 1 1 1
Size 1 1 1 1
MS 0.757 0.926 0.893 0.757
Culture (Collaboration) 0.866 0.928 0.846 0.866
Culture (Coordination) 0.722 0.839 0.616 0.722
Strategy 0.708 0.923 0.896 0.708
Structure (Decentralisation) 1 1 1 1
Structure(Formalisation) 0.758 0.862 0.693 0.758
Structure(Team-based) 1 1 1 1
Maturity 0.684 0.896 0.122 0.847 0.684 0.022
ERP 0.744 0.921 0.454 0.885 0.744 -0.007
EICPs 0.612 0.887 0.668 0.840 0.612 0.097
Fourth, the discriminant validity was assessed by using the Fornell-Larcker technique (Fornell
and Larcker, 1981). Table 4 shows that all of the square roots of AVE for the study’s constructs are
higher than the correlation between each construct and another (in the same row or column).
Therefore, all of the constructs have an agreeable level of discriminant validity.
Table 4 Square Root of AVE and Correlation Matrix of Study Constructs
Brand Culture Dec. EICPs ERP Form. MS Maturity Size Strategy Team based
Year Imp
Brand 1
Culture -0.175 0.767
Dec. 0.022 0.476 1
EICPs -0.179 0.633 0.5108 0.782
ERP -0.105 0.319 0.2375 0.454 0.858
Form. -0.011 0.499 0.5489 0.601 0.359 0.869
MS -0.074 0.653 0.6086 0.650 0.241 0.478 0.870
Maturity -0.186 0.231 0.2011 0.400 0.579 0.195 0.292 0.825
Size -0.404 0.009 -0.0819 0.039 0.150 0.054 -0.023 0.158 1
Strategy -0.057 0.563 0.4466 0.687 0.323 0.630 0.599 0.257 0.110 0.844
Team-based 0.004 0.543 0.5675 0.441 0.267 0.448 0.565 0.250 -0.006 0.553 1
Year Imp 0.014 0.063 0.1058 0.026 0.100 0.048 0.027 0.288 0.288 0.068 0.104 1
-Testing the Hypotheses
The structural model was estimated using SmartPLS software in order to test the hypotheses. The
software makes no distributional assumptions, so bootstrapping (500 samples with replacement) is
applied to test the statistical significance of each path coefficient (Hair et al., 2013). Table 5 as well
as Figure 2 illustrate the results of the significance of each path coefficient.
Table 5 the Significance of Path Coefficient
Path from Path To
Predicted sign Maturity ERP EICPs
Brand -,+ 0.190** 0.035
Year Imp +,- 0.297*** 0.113
Culture (Collaboration) +,+ 0.081 0.183*
Culture(Coordination) +,+ 0.249* 0.003
MS -,+ 0.223 0.287**
Size +,- 0.098 0.023
Strategy +,+ 0.094 0.299***
Structure (Formalisation) +,+ 0.235* 0.102
Structure(Decentralisation) -,+ 0.087 0.139
Structure(Team-based) +,- 0.033 0.157*
Maturity + 0.551***
ERP + 0.225**
R² 0.122 0.454 0.668 ***, **, and * Significant at 0.01, 0.05, and 0.1 levels respectively (two-tailed).
Figure 2: Path Diagram with Path Coefficients-Whole Sample
Among the contingency variables, organisational structure (Team-based), management support,
organisational strategy and organisational culture (collaboration) were found to be significantly
associated with EICPs. Additionally, it was found that ERPs’ success was significantly related to
the EICPs. However, size was not significantly associated with the EICPs.
It was found that organisational culture (coordination), the maturity of the ERP system and the
organisational structure (formalisation) were significantly related to the success of ERP systems,
whereas organisational size, strategy and management support were not significantly associated
with ERP success. The results of this chapter provided evidence of the mediation effect of ERP
success on the relationship between organisational structure (formalisation), organisational culture
(coordination) and EICPs.
Discussion and Conclusion The objective of this research was to propose and validate a contingency model that explains the
role of ERP success in supporting the effectiveness of ECPs. The results from testing the
hypotheses are summarised below in Table 6:
Table 6 Summary of the study hypotheses Result
Contingencies and ERPs success H1:Organisational structure is positively associated with the ERP system
success
Partially
Accepted
H2: There is a positive association between prospector strategy and ERPs
success.
Rejected
H3: There is a positive relationship between organisational size and
success of ERP system.
Rejected
H4: Organisational culture positively influences the success of ERP
systems.
Partially
Accepted
H5: There is a positive correlation between top management support and
ERPs success.
Rejected
H6:There is a relationship between the ERP brand and success of ERP
system
Rejected
H6a:There is indirect relationship between the ERP brand and success of
ERP system
Rejected
H7:The year of ERP implementation positively associated with the
success of ERP systems
Rejected
H7a:The year of ERP implementation indirectly associated with the
success of ERP systems
Accepted
H8:There is a positive correlation between the maturity of ERP functions
and success of ERP systems
Accepted
H9:The ERP brand is negatively associated with maturity of ERP
functions
Accepted
H10: There is a positive relationship between the year of ERP
implementation and the maturity of ERP functions.
Accepted
Contingencies and EICPs H11: Organisational structure is associated the effectiveness of ICPs. Partially
Accepted
H12:There is a relationship between the strategy prospectors and the
effectiveness of ICPs
Accepted
H13:A large size organisation is positively associated with the
effectiveness of ICPs
Rejected
H14:There is appositive relationship between the organisational culture
and the effectiveness of ICPs
Partially
Accepted
H15: There is a positive relationship between management support and the
effectiveness of ICPs.
Accepted
ERPs success and EICPs H16: Success of ERP systems is positively associated the effectiveness of
ICPs.
Accepted
H11a: There are indirect relationships between the structure
(formalisation) and the effectiveness of ICPs.
Accepted
H14a: There are indirect relationships between the culture (coordination) and the effectiveness of ICPs.
Accepted
- Contingencies and ERP success
The results indicate that organisational structure (formalisation), organisational culture
(coordination) and the maturity of the ERPs’ functions significantly predict the success of ERP
systems. Researchers, such as Gable et al. (2003), DeLone and McLean (2003) and Ifinedo (2006)
discuss the importance of contingency factors on the success of ERP systems. Contingency factors
like the structure, strategy size and organisational culture have been studied in relation to ERPs’
implementation, but few researchers have investigated these variables with regard to the success of
ERP systems (post-implementation). Ifinedo and Nahar (2009) find that organisational structure
and size influence the success of ERP systems. They point out that centralisation, specialisation
and formalisation are adequate for assessing ERP success, which is similar to the current study’s
finding, except with regard to size. Chou and Chang (2008) observe that an organisational culture
that embraces coordination influences the benefit of ERP systems.
Additionally, Saunders and Jones (1992) speculate about the importance of the maturity of IS’s
functions, but very few studies examine the effect of the maturity of ERPs’ functions. Voordijk et
al. (2003), show that the maturity of the IT infrastructure is an important factor for a successful
ERP system implementation. Dias and Souza’s (2004) study’s results point to a relationship
between the level of maturity of the ERP systems and the potential for perceiving a competitive
advantage, so this study adds more evidence about the importance of the structure (formalisation
and centralisation), organisational culture (coordination) and maturity of ERPs’ functions in
predicting the success of ERP systems.
- Contingencies and EICPs
The results illustrated in Table 6 show that the structure (team-based), prospectors’ strategy,
management support, and an organisational culture that embraces collaboration significantly
predict the EICPs. Waterhouse (1975) observes a relationship between the organisational structure
and the effect and use of the budgets. The study’s findings, in general, agree with those of many
other researchers. For instance, Waterhouse (1975) observes a relationship between organisational
structure and the effect and use of the budgets. Rajaratnam and Chonko (1995) find empirical
evidence of the relation between organisational strategy and organisational performance. Bititci et
al. (2006) suggest the existence of a relationship between organisational culture, management style
and the performance measurement system. Moynihan and Ingraham (2004) find that the top
management matter with regard to the use of performance information in decision making and also
to the organisational effectiveness.
- Success of ERP systems and EICPs
This study confirms that the success of ERP systems significantly predicts the EICPs. The field
study findings (for the relationship between ERP systems and ICPs) are similar to those of
Maurizio et al. (2007)and Morris (2011). Maurizio et al. (2007) indicate the need for fully-
integrated systems like ERP systems to prevent interruption to the data flow. Morris (2011) argues
that the “built-in control” features and other features of ERP systems can help an organization to
improve its internal control procedure. The study finds that companies that have adopted ERP
systems are reporting less internal control material weaknesses than those that have not adopted
them.
- Conclusion
The current study’s main aim is to develop a structure model of the contingency variables and ERP
system success that can predict the effectiveness of ICPs for Saudi Arabian companies. The
structure mode was developed at the beginning of this paper, following by an assessment of the
variables that predicate the effectiveness of ICPs. The results indicate a positive role for the
success of ERP systems in predicting the EICPs. Additionally, the results show the mediation
effect of the success of ERP systems on predicting the EICPs.
Appendix
Appendix 1: The variables measures
N
o
Variables References
1
ER
P
syst
e
m
succ
e
ss
dim
e
nsi
on
s
System
quality:
refer to the
Data accuracy, Data currency, Database
contents, Ease of use, Ease of learning, Access,
User requirements, System features, System
(DeLone and McLean,
1992; Myers et al.,
1997; Gable et al.,
enviable
characteristic
of the system
accuracy, Flexibility, Reliability, Efficiency,
Sophistication, Integration, Customization
2003)
2 Information
quality:
refer to
enviable
characteristic
of the system
output
Importance, Availability, Usability,
Understandability, Relevance, Format, Content
Accuracy, Conciseness, Timeliness,
Uniqueness
DeLone & McLean
(1992); Myers et
al.(1997); The Gable et
al. (2003)
3 Services
quality: refer to the
quality of the
support from
the IT or IS
dep.
Responsiveness, reliability, assurance,
accuracy, technical competence.
Myers et
al.(1997);(DeLone and
McLean, 2003),
(Seddon, 1997)
4 Individual
impact: refer
to the extent
of the system
contribution
to the
individual
Learning, Awareness / Recall, Decision
effectiveness, Individual productivity
DeLone & McLean
(1992); Myers et
al.(1997); The Gable et
al. (2003)
5 Organisatio
n impact : refer to the
extent of the
system
contribution
to the
organisation
Organizational costs, Staff requirements, Cost
reduction, Overall productivity, improved
outcomes/outputs, increased capacity, e-
government, Business Process Change
DeLone & McLean
(1992); Myers et
al.(1997); The Gable et
al. (2003)
6
Co
nti
ng
ency
var
iab
les
Organisation
structure
- Diversified occupational speciality
- Descriptive of the jobs.
- Participation of employees in decisions
- Relationship b/w manger and staffs
Pugh et al. (1968)
Donaldson (2001)
7 Organisation
strategy
-Supporting new products/services
- Leading to innovation
-Responds quickly to opportunity
-Competitive activities.
-Promotes long range planning/decisions
-Involving in high-risk projects
(Croteau and
Bergeron, 2001;
Chenhall, 2007)
8 Organisation
size
Total of assets Mabert et al 2003,
(Abdel-Kader and
Luther, 2008)
9 Organisation
al culture
- Employees work in project teams
- Employees willing to collaborate
- Coordination
- Controlling
- Meeting deadlines
-New ideas welcomes
(Detert et al., 2000;
Jones et al., 2006)
10 Management
support
- Supporting research and innovation
- Willing to take risks
- Helps to provide necessary resources
- Involves employees in strategic plan.
- Providing direction and motivation.
- Delegating tasks to others
11 ERP brand The vendor of ERP systems (Wang et al., 2011)
12 Period of
ERP imp.
Number of year that ERP systems have been
implemented
(Wang et al., 2011)
13 Maturity of
the system
growth stages of the system
Nolan (1979)
14 dep.
var
Efficiency of
ICP
internal environment, objective setting, event
identification, risk assessment, risk response,
control activities, information and
communication, and monitoring
(COSO, 2004)
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