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THE IMPACT OF KNOWLEDGE
INTEGRATION ON ENTERPRISE
SYSTEM SUCCESS
NOR HIDAYATI ZAKARIA
Degree of Doctor of Philosophy
FACULTY OF SCIENCE AND TECHNOLOGY
QUEENSLAND UNIVERSITY OF TECHNOLOGY 2011
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SUPERVISORY PANEL
Principal Supervisor
Dr. Darshana Sedera
Faculty of Science and Technology
Queensland University of Technology
Associate Supervisor
Prof. Guy G. Gable
Faculty of Science and Technology
Queensland University of Technology
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STATEMENT OF ORIGINAL AUTHORSHIP
The work contained in this thesis has not been previously submitted to meet
requirements for an award at this or any other higher education institution. To
the best of my knowledge and belief, the thesis contains no material previously
published or written by another person except where due reference is made.
..........................................................
Signature
NOR HIDAYATI ZAKARIA
10 October 2011
.........................................................
Date
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DEDICATION
This thesis is dedicated to:
My husband, Dr. Nazir,
My children, Asyila, Atef and Eishah,
My mother and parents-in-law
and
My supervisors, Dr. Darshana and Prof. Guy
and
All my friends
for your support, guidance, patience, joyfulness to make this experience complete.
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ABSTRACT
Despite promising benefits and advantages, there are reports of failures and low realisation of
benefits in Enterprise System (ES) initiatives. Among the research on the factors that influence
ES success, there is a dearth of studies on the knowledge implications of multiple end-user
groups using the same ES application. An ES facilitates the work of several user groups, ranging
from strategic management, management, to operational staff, all using the same system for
multiple objectives. Given the fundamental characteristics of ES – integration of modules,
business process views, and aspects of information transparency – it is necessary that all
frequent end-users share a reasonable amount of common knowledge and integrate their
knowledge to yield new knowledge. Recent literature on ES implementation highlights the
importance of Knowledge Integration (KI) for implementation success. Unfortunately, the
importance of KI is often overlooked and little about the role of KI in ES success is known.
Many organisations do not achieve the potential benefits from their ES investment because they
do not consider the need or their ability to integrate their employees‟ knowledge. This study is designed to improve our understanding of the influence of KI among ES end-users
on operational ES success. The three objectives of the study are: (I) to identify and validate the
antecedents of KI effectiveness, (II) to investigate the impact of KI effectiveness on the
goodness of individuals‟ ES-knowledge base, and (III) to examine the impact of the goodness of
individuals‟ ES-knowledge base on the operational ES success. For this purpose, we employ the
KI factors identified by Grant (1996) and an IS-impact measurement model from the work of
Gable et al. (2008) to examine ES success.
The study derives its findings from data gathered from six Malaysian companies in order to
obtain the three-fold goal of this thesis as outlined above. The relationships between the
antecedents of KI effectiveness and its consequences are tested using 188 responses to a
survey representing the views of management and operational employment cohorts.
Using statistical methods, we confirm three antecedents of KI effectiveness and the
consequences of the antecedents on ES success are validated. The findings demonstrate a
statistically positive impact of KI effectiveness of ES success, with KI effectiveness contributing
to almost one-third of ES success. This research makes a number of contributions to the
understanding of the influence of KI on ES success. First, based on the empirical work using a
complete nomological net model, the role of KI effectiveness on ES success is evidenced.
Second, the model provides a theoretical lens for a more comprehensive understanding of the
impact of KI on the level of ES success. Third, restructuring the dimensions of the knowledge-
based theory to fit the context of ES extends its applicability and generalisability to
contemporary Information Systems. Fourth, the study develops and validates measures for the
antecedents of KI effectiveness. Fifth, the study demonstrates the statistically significant positive
influence of the goodness of KI on ES success.
From a practical viewpoint, this study emphasises the importance of KI effectiveness as a direct
antecedent of ES success. Practical lessons can be drawn from the work done in this study to
empirically identify the critical factors among the antecedents of KI effectiveness that should be
given attention.
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TABLE OF CONTENTS
Supervisory Panel ii
Statement of Original Authorship iii
Dedication iv
Abstract v
Table of Contents vi
List of Figures xii
List of Tables xiii
Abbreviations xiv
CHAPTER 1 INTRODUCTION 1
1.1 RESEARCH BACKGROUND
1.1.1 A Critical Success Factor for the ES Lifecycle
1.1.2 The Types of ES Knowledge and Its Holders
1.2 MOTIVATION
1.3 THE POSITIVE IMPACT OF KNOWLEDGE INTEGRATION
ON ES SUCCESS
1.3.1 Integration of Individuals‟ ES-related Knowledge
1.3.2 The Benefit of KI on Individuals‟ ES-Knowledge Base
1.4 RESEARCH SCOPE
1.5 THEORETICAL OVERVIEW
1.6 RESEARCH OBJECTIVES AND QUESTIONS
1.7 SIGNIFICANCE OF RESEARCH
1.8 RESEARCH DESIGN
1.9 SUMMARY
1.10 OVERVIEW OF THESIS
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CHAPTER 2 KNOWLEDGE INTEGRATION FOR ES SUCCESS 27
2.1 INTRODUCTION
2.2 ENTERPRISE SYSTEMS: THE RESEARCH CONTEXT
2.2.1 ES Failures and Challenges
2.2.2 ES Post-Implementation: Why is it Significant?
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2.3 KNOWLEDGE MANAGEMENT
2.3.1 Knowledge: Is it so Important?
2.3.2 Knowledge Management Process
2.3.3 Knowledge Management Focus
2.3.4 Knowledge Management and Knowledge Integration
2.4 KNOWLEDGE INTEGRATION: A THEORETICAL
VIEWPOINT
2.4.1 The History of Knowledge Integration
2.4.2 The Recognition of KBT
2.5 KNOWLEDGE-BASED THEORY OF THE FIRM
2.5.1 Knowledge Integration Mechanisms
2.5.2 Factors of Knowledge Integration Effectiveness
2.6 KNOWLEDGE INTEGRATION IN ENTERPRISE SYSTEMS
2.6.1 Passive Integration in an Organisation
2.6.2 Active Integration in an Organisation
2.6.3 Active Integration by the Individual
2.6.4 Restructuring the Antecedents of KI Effectiveness
2.7 ES-KNOWLEDGE BASE
2.7.1 Software Knowledge
2.7.2 Business Process Knowledge
2.7.3 Organisational Knowledge
2.7.4 Types of ES Knowledge and Employment Cohorts
2.7.5 Significance of the Knowledge Base
2.8 KNOWLEDGE INTEGRATION AND ES-KNOWLEDGE
BASE
2.9 ENTERPRISE SYSTEM SUCCESS
2.10 SUMMARY
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CHAPTER 3 RESEARCH MODEL AND HYPOTHESES 72
3.1 INTRODUCTION
3.2 RESEARCH MODEL
3.3 SIGNIFICANCE OF RESTRUCTURING THE
ANTECEDENTS OF KI EFFECTIVENESS
72
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3.3.1 Individual and Organisational Perspectives
3.3.2 Passive and Active Perspectives
3.4 THE ANTECEDENTS OF KI EFFECTIVENESS
3.4.1 Passive Integration of an Organisation
3.4.2 Active Integration of an Organisation
3.4.3 Active Integration of the Individual
3.5 KI EFFECTIVENESS
3.6 THE CONSEQUENCES OF KI EFFECTIVENESS
3.6.1 The Goodness of Individual‟s ES-Knowledge Base
3.6.2 The ES Success
3.7 HYPOTHESES DEVELOPMENT
3.8 SUMMARY
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CHAPTER 4 SURVEY DEVELOPMENT 94
4.1 INTRODUCTION
4.2 THE UNIT OF ANALYSIS
4.3 DATA COLLECTION OBJECTIVES
4.4 MINIMISING THE COMMON METHOD VARIANCE
4.5 SURVEY DESIGN
4.5.1 Survey Instrument
4.5.2 The Antecedents of Knowledge Integration
Effectiveness
4.5.3 The Construct of KI Effectiveness
4.5.4 The Consequences of KI Effectiveness
4.5.5 Survey Translation
4.5.6 Research Sample Selection
4.5.7 Sample Overview
4.5.8 Content Validation
4.5.9 The Survey Instrument Revision
4.5.10 The Survey Deployment
4.6 RESPONDENT ANONYMITY AND CONFIDENTIALITY
4.7 SUMMARY
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CHAPTER 5 DATA ANALYSIS, RESULTS AND DISCUSSION 122
5.1 DATA ANALYSIS DESIGN
5.2 DATA COLLECTION OVERVIEW
5.3 DATA PREPARATION
5.4 DESCRIPTIVE STATISTICS
5.4.1 Responses by Employment Cohort
5.4.2 Responses by ES Solution Type
5.4.3 Responses by Length of ES Usage
5.4.4 Responses by Length of Working Experience
5.4.5 Mean and Standard Deviation
5.4.6 Data Distribution
5.4.7 Statistical Analysis Overview
5.5 RESEARCH MODEL MEASUREMENT
5.5.1 Constructs-Measurement Relationships
5.5.2 Formative Constructs
5.5.3 Reflective Constructs
5.5.4 Construct Validation
5.5.5 Construct Reliability
5.5.6 Model Assessment Overview
5.5.7 Content Validity
5.5.8 Multicollinearity Estimation for Formative
Constructs Assessment
5.5.9 Reliability Test
5.5.10 Construct Validities and Reliabilities for Reflective
Constructs Assessment
5.5.11 Factor Analysis
5.5.12 Cronbach‟s Alpha
5.5.13 Composite Reliability
5.5.14 Average Variance Extracted
5.6 GRANT‟S KBT MODEL EVALUATION
5.6.1 Multicollinearity Estimation for Formative
Constructs
5.6.2 Reflective Constructs Assessment
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5.6.3 Factor Analysis
5.6.4 Cronbach‟s Alpha
5.6.5 Composite Reliability
5.6.6 Average Variance Extracted
5.7 HYPOTHESES TESTING
5.7.1 Structural Research Model Assessment
5.7.2 Bootstrapping Procedure
5.7.3 Research Hypotheses Examination
5.7.4 Direct Impact of KI Effectiveness on ES Success
5.7.5 Choice of the Best Model
5.7.6 Original Structural Model Assessment
5.7.7 Relationships of the Constructs
5.7.8 Conclusion
5.8 ADDITIONAL FINDINGS
5.8.1 Managerial Group
5.8.2 Operational Group
5.8.3 A Comparison Between Managerial and Operational
Groups
5.8.4 SAP Users
5.8.5 ES Standard for Government State and Agencies
5.8.6 A Comparison Between Users of SAP and Standard
ES for Government
5.8.7 Length of Working Experience
5.8.8 Five Years and Below
5.8.9 Six Years and Above
5.8.10 A Comparison Between Groups Based on Length of
Experience
5.9 DISCUSSION OF THE RESEARCH FINDINGS
5.9.1 Discussion 1: Overall Research Model Findings
5.9.2 Discussion 2: Antecedents Only Represent 50% of
KI Effectiveness
5.9.3 Discussion 3: Managerial versus Operational Users
5.9.4 Discussion 4: SAP Product versus non-SAP System
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5.9.5 Discussion 5: Length of Working Experience
5.9.6 Discussion 6: Multi-Industry Sample
5.9.7 Multiple Cohorts‟ Sample
5.9.8 Experienced Sample
5.10 SUMMARY
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CHAPTER 6 RELATED WORKS, CONTRIBUTIONS, LIMITATIONS
AND FUTURE WORKS
181
6.1 RELATED WORKS
6.2 CONTRIBUTIONS
6.2.1 Contributions to Theory
6.2.2 Contributions to Practice
6.3 LIMITATIONS
6.3.1 Limitations in the Questionnaire Deployment
6.3.2 Limitations in the Research Findings
6.4 FUTURE STUDIES
6.5 CONCLUSION
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REFERENCES 203
APPENDIX 1: Example of ES Modules in Respondent‟s Organisation (from Interview
Session) 224
APPENDIX 2: Survey Instrument 225
APPENDIX 3: The Pool of 27 IS-Impact Measures 230
APPENDIX 4: The Letter of Participation 231
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LIST OF FIGURES
No. Figure name Page
Figure 1.1 Relationship between employment cohorts and ES knowledge types 7
Figure 1.2 Research model outline 22
Figure 1.3 The research design 23
Figure 2.1 Literature review design 28
Figure 2.2 Publications on ES challenges and failures (adapted from Momoh et al.
(2010))
31
Figure 2.3 ES phases (adapted from Ross et al. (2003)) 33
Figure 2.4 Evolution of KM research 38
Figure 2.5 The theory used in KM articles 41
Figure 2.6 Different collective levels of knowledge (adapted from Tiwana (2001)) 43
Figure 2.7 ES-knowledge base development 67
Figure 2.8 IS-impact measurement model 69
Figure 3.1 Brief outline of the research model 74
Figure 3.2 KI management framework for ES (adapted from Wunram et al. (2003)) 78
Figure 3.3 Restructuring the antecedents of KI effectiveness 79
Figure 3.4 The research model 80
Figure 4.1 The survey design 97
Figure 5.1 Data analysis design 123
Figure 5.2 Response rate by ES types 126
Figure 5.3 Length of ES usage 127
Figure 5.4 Length of working experience 128
Figure 5.5 Assessment of research model 147
Figure 5.6 Model without ES-knowledge base 151
Figure 5.7 Original model 153
Figure 5.8 Estimated paths for the managerial group with PLS 156
Figure 5.9 Estimated paths for the operational group with PLS 158
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LIST OF TABLES
No. Table name Page
Table 1.1 Key concepts of the research 16
Table 1.2 Research objectives 19
Table 2.1 Knowledge integration definitions 42
Table 2.2 Restructuring the antecedents of KI effectiveness 57
Table 2.3 Summary of knowledge base discussion 58
Table 2.4 Significance of the knowledge base 64
Table 2.5 IS-impact measures 70
Table 3.1 Summary of hypotheses tests 92
Table 4.1 Rating scale of agreement 98
Table 4.2 Rating scale of frequency 98
Table 4.3 Frequency questions 104
Table 4.4 Dimensions of ES success 109
Table 4.5 Summary of research constructs and measures 111
Table 4.6 Summary of research sample 116
Table 5.1 Response rate by employment cohort 125
Table 5.2 Response rate by ES solution types 126
Table 5.3 Length of ES usage 127
Table 5.4 Working experience 128
Table 5.5 Suitability of the measures 129
Table 5.6 Validity test for formative constructs 136
Table 5.7 Factor loadings, Cronbach‟s alpha, composite reliability and AVE 138
Table 5.8 Validity test for formative constructs 142
Table 5.9 Factor loadings, Cronbach‟s alpha, composite reliability and AVE 143
Table 5.10 Summary of measures and path coefficients 148
Table 5.11 Summary of hypotheses test 153
Table 5.12 Estimated value of paths for SAP users 160
Table 5.13 Estimated value of paths for government users (SPEKS and SAGA) 160
Table 5.14 Estimated value of paths for respondents with work experience of 5
years and less
163
Table 5.15 Estimated value of paths for respondents with work experience of 6
years and above
164
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ABBREVIATIONS
Acronym Full title
AII Active Integration of the Individual
AIO Active Integration of an Organisation
AVE Average Variance Extracted
ERP Enterprise Resource Planning
ES Enterprise System
KBT Knowledge-based Theory of the Firm
KI Knowledge Integration
KM Knowledge Management
PCA Principal Component Analysis
PIO Passive Integration of an Organisation
PLS Partial Least Square
RBV Resource-based View
SAGA Standard Accounting for Government Agencies
SAP Systems Applications and Products
SEM Structural Equation Model
SPEKS Standard Accounting for State Government
SPSS Statistical Package for the Social Science
VIF Variance of Inflation Factor
Y2K Year 2000
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CHAPTER 1:
INTRODUCTION
In an era of rapidly developing industries and a globalised world economy, the
Enterprise System (ES), a large-scale of an integrated software packages has become an
important tool for organisations to optimise their productivity and efficiency, and to
provide the functionality for employees to work more effectively. While some
organisations gain significant benefits, others fail to achieve the potential advantages of
the ES (Liang et al. 2007; Soh et al. 2000). A number of ES failures that occurred in the
late 1990s and in the early part of the new millennium have been reported (Caya 2008).
Most of the organisations affected by poorly implemented ES had tried to use an ES to
quickly address the „Y2K‟ problem (Ross et al. 2003). Since ES implementation is costly,
with costs as high as 500 million US dollars, failure to correctly operate the ES can lead
to bankruptcy (Seddon et al. 2010). As many organisations have learned from past ES
failures, ES vendors have begun to offer more affordable prices, are more accessible to
diverse sectors and tend to provide shorter return-on-investment periods. To date,
however, the issues surrounding ES failure remain the subject of debate among
organisations and researchers.
Understanding the factors that cause ES failure is crucial in order to determine the
success of an ES. One of the common factors is the lack of appropriate training in the
ES usage. Prior studies have shown that poor education and training is a primary reason
for the failure of an ES to meet expectations (Scott 2005). Since an ES is very complex
and heavily integrated, it is difficult to utilise. Thus, it must have the support from the
entire organisation. Considering the complexity of the ES, it is important to ensure
employees fully understand it, and the chances for ES success are enhanced if the
employees in an organisation can use the system properly. Once the ES is implemented,
many employees still do not understand its proper use and struggle to perform basic
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transactions in the system (Klaus and Blanton 2010; Klaus et al. 2010). Thus, it is very
important to ensure all employees accept and fully understand the system.
Organisations should also identify the people who will be responsible for ensuring that
the operations of the ES follow the intended purposes and are fully utilised by their
employees.
Another factor in ES failure is the failure to share the objectives of the ES
implementation with all the employees (Worley et al. 2005). If information about the
benefits that the organisation seeks to achieve through the ES is not disseminated
throughout the entire organisation, employees will not have a clear understanding
about what they need to obtain from the new system. They may also not know what
actions have to be taken to achieve the system‟s benefits or even how to judge
whether the system is successful or not. Lack of knowledge of the new system makes it
difficult to convince employees about the advantages offered by the ES compared to
their legacy system (Klaus and Blanton 2010), which increases the possibility of
employees‟ rejection of the system.
Some organisations overlook the advantage of clear organisational structures, not
realising the importance of a strong structure for employees. The ES implementation
will generate changes in some organisational processes and in the structure of the
organisation as it relates to employees‟ roles (Adam and O‟Doherty 2003). The
changes to their roles brought about by the new system may be met with levels of
resistance.
These brief examples of the factors in ES failure highlight the importance of effective
knowledge integration (KI), which we define it as a combination of expertise and skills
among the employees in an organisation. KI deals with how well employees use all the
available knowledge resources. Communication between employees who have diverse
skills, backgrounds and status is more likely to produce fresh and novel ideas and
approaches to problem solving (Huggins and Izushi 2007). After the go-live date, it is
incumbent on organisations to work out how to continuously leverage the ES
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investment and improve processes long after the consultants walk out the door. Thus,
promoting the involvement of employees and engendering a sense of ownership among
them is vitally important during post-implementation. Employees in organisations
should proactively integrate the required ES knowledge and minimise their reliance on
the consultancy team. This has the concomitant benefit of reducing consultancy costs
(Garg 2010).
The integration of ES knowledge among employees can generate promising advantages
to an organisation. These benefits include greater and more efficient ES productivity,
improved performance throughout the organisation, quicker ES adoption among
employees, maintenance of the company‟s business processes, while enabling
employees to perform daily activities better and faster with the new system‟s
functionality. In addition, effective KI helps employees gain the knowledge and skills
needed to leverage ES value. It enables them to maximise the enhanced functionality of
the ES and quickly understand how to use and customise the business processes
supported by the system. Employees can gain optimal insight into the ES and realise its
benefits through better understanding of the business processes, issues and
improvement opportunities.
KI does not necessarily only occur through events such as the delivery of software
training by experts to employees. In fact, the integration process may happen via
informal interactions among employees. Having defined KI effectiveness as “the ability
to successfully combine and synthesise the ES values from others‟ expertise”, we
employ the concept of KI from Grant‟s (1996) knowledge-based theory of the firm
(KBT), taking the range of factors that facilitate KI as articulated in the theory and
restructuring these factors to fit the ES context. Using the factors of efficiency, scope
and flexibility from KBT, we articulate these factors as three antecedents of KI
effectiveness which we refer to as: passive elements of integration that exist in an
organisation (PIO); active integration practices by employees within groups, teams or
departments in an organisation (AIO); and active integration practices from individuals‟
perspectives (AII) (see Chapter 3). By determining the importance of horizontal
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integration among employees within department, we seek to examine the influence of
KI effectiveness on ES success through empirical analyses of ES implementation and use
in six large organisations in Malaysia.
ES are often very difficult to manage due to their scope which is very process-oriented
and cross-functional. In fact, many organisations simply do not have skills to carry out
the ES successfully (Vaman 2007). Hence, we try to understand ES success from the
operational side, which is always an important aspect for organisations to be aware of.
From our perspective, operational ES success means the interaction of staff with the ES
to put the ES into operation in accordance with the intent of the system. For instance,
operational ES success involves the integration of ES knowledge among staff to use,
maintain and upgrade the ES, whereby staff possess the necessary skills to run the ES
and adopt the ES comfortably and effectively.
1.1 RESEARCH BACKGROUND
An Enterprise System, also referred to as Enterprise Resource Planning (ERP), is a large
integrated system designed to meet most needs of organisations including those in the
fields of accounting, manufacturing, sales, human resources and management reporting
(Strong and Volkoff 2010). An ES centralises all the organisation‟s key functional
systems and business processes (Davenport et al. 2004), and is expected to speed up
communications, improve decision-making and lead to lower support costs (Ross and
Vitale 2000). Over the last decade, many organisations have invested enormous
amounts into such systems. Investment in an ES consistently remains the top IT
spending priority in organisations, as reported in the Forrester survey data (Wang and
Hamerman 2008). Importantly, the report predicts the ES market to constantly grow at
a steady rate of 6.9% reaching $50 billion by 2012 (Sedera and Gable 2010).
Organisational benefits from ES continue to be difficult to achieve and unpredictable
(Strong and Volkoff 2010). Many studies report ES failures (Scott and Vessey 2002;
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Zabjek et al. 2009) whereby businesses continuously lose billions of dollars annually
(Zhang et al. 2005) and are dissatisfied with benefits obtained from their ES investments
(Sedera and Gable 2010). For example, a Standish Group study reported that fewer
than 10% of ES implementations succeed with full functionality within forecasted cost
and timeframes (Momoh et al. 2010). The ES remains problematic as experienced in
many prominent organisations (Sathish 2006), such as Dell Computer, Boeing, Allied
Waste Industries, Waste Management Inc., Hershey Food Corporation, Dow Chemical,
Mobil Europe and Kellogg‟s (Dey et al. 2010).
1.1.1 A Critical Success Factor for the ES Lifecycle
Further investigation of factors which influence ES success is imperative as prior
research indicates a 90% failure rate (Zabjek et al. 2009). The high failure rate of the ES
is a major concern for organisations (Dey et al. 2010), given that large investments have
been made for the ES. The failure risk of ES can be decreased if organisations know
exactly what critical factors affect the success of an ES. If the critical factors are not
established within the ES, its success could be jeopardised. A number of potential
explanations for ES implementation failures have been offered, which recently have
tended to place greater emphasis on the importance of human factors rather than
technical and economic aspects for ES success (Wang and Chen 2005; Yeh and OuYang
2010). In addition, many researchers suggest critical success factors for ES lifecycle-
wide success (Dey et al. 2010, Mandal and Gunasekaran 2003; Umble et al. 2003) in
which the importance of knowledge management (KM) in ES has been realised (Al-
Mashari et al. 2003; Ko et al. 2005; Lee and Lee 2000; O‟Leary 2002; Sedera and Gable
2010).
The ES lifecycle process is a process of knowledge creation, retention, transfer and
application (Alavi 2001) from the KM viewpoint (O‟Leary 2002). For instance, the
process of ES selection, alignment of the organisation‟s business processes with the
functionality offered by the ES, knowledge transfer for ES adoption in the organisation
show that KM is intrinsically linked with the entire ES lifecycle.
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However, managing knowledge for an ES is a complex task, as the system is often found
to be huge, overly complicated to run and challenging (Farhoomand 2007), and only a
few organisations are reported to have been fully successful (Davenport 2004). An ES
would certainly fail if the key employees lack the relevant skills and knowledge (Zabjek
et al. 2009). A number of issues can arise such as erroneous data input, poor use of the
ES and employee resistance (Momoh et al. 2010) if the employees do not understand
the ES completely (Liang et al. 2007). One common problem is poor ES-related
knowledge among end-users, who, when the system is up and running, do not know
how to use and maintain it continually. Employees are often obliged to employ the ES
without a solid understanding of the ES goals, benefits and weaknesses. This usually
happens in the context of packaged ES software whereby users have not been involved
in designing the software which is relatively fixed by the package (Wagner and Newell
2007).
1.1.2 The Types of ES Knowledge and Its Holders
An ES requires vast types of knowledge in order to reap the system‟s benefits. The
system involves internal and external stakeholders including the client organisation, ES
vendor and consultants (Sedera and Gable 2010). Typically, there are three types of ES
knowledge, namely, business process knowledge, organisation knowledge and software
knowledge (Davenport 1998). These types of knowledge are contributed by the ES key
players. During implementation, consultants and vendors bring together their prior
work experience, work values, norms, philosophies and problem-solving approaches
(Ko et al. 2005).
The ES also involves users from various levels, departments and divisions of the
organisation (Lin and Rohm 2009), from senior executives to middle managers and
operational staff. There are three levels of employment cohorts in an organisation:
strategic, management and operational levels (Anthony 1965). The strategic level
involves complex, irregular decision-making and focuses on providing policies to govern
the organisation. These ES users have multiple ES perspectives, views and intentions.
They require diverse types of knowledge, expertise and specialised skills of the ES
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(Hoegl and Gemuenden 2001; Sathish 2006). However, the knowledge that is required
by the management level is different from the knowledge needed by the strategic level.
The management level focuses on ensuring that the organisation‟s resources are used
effectively and efficiently to accomplish the goals identified by the strategic level (Sedera
2007). In contrast, the operational level is involved in highly structured and specific
tasks that are routine and transactional. With reference to Sedera (2007), Sedera et al.
(2004) and Sedera et al. (2007), we conceptualise the correlation between the
employment cohorts in organisations and the ES knowledge types as set out in Figure
1.1.
Software Business
processes
Organisation
Strategic Low Medium High
Management Medium High Medium/Low
Operational High Medium Low
Figure 1.1: Relationship between employment cohorts and ES knowledge types
Figure 1.1 shows the three hierarchical levels of employment cohorts and describes the
significance of ES knowledge types for each cohort. Organisational knowledge is highly
significant to the strategic employees, and is less essential for the management and
operational employees. At the operational level, software knowledge is crucial for the
employees. In contrast, it is a necessity for management staff to have a deep knowledge
of business processes to achieve greater efficiency and better quality of ES usage.
Therefore, if the current business practices and procedures need to change,
management staff can review and make innovations to the processes, services or
business functions which fit the ES. The diversity of ES knowledge and ES users within
the organisation needs to be managed carefully, as well as the ES complexity and the
various ES problems that are reported. Thus, knowledge management for ES is found
to be a central factor for an organisation (Francoise et al. 2009).
Strategic
Management
Operational
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1.2 MOTIVATION
High ES failure rates, unrealised benefits, cost overruns and low return-of-investment
to organisations despite heavy investment are reported in the literature (Davenport
and Harris 2002; Davenport et al. 2004). The high failure rate of ES can be largely
understood by reference to the complexity of such a system (Liang et al. 2007). Indeed,
the integrated database and complex structure of an ES may make it difficult for
companies to adapt their processes (Davenport 2000). Besides multiple functions, the
ES involves numerous stakeholders and user cohorts, diverse knowledge, expertise and
skills, and every ES user has a unique viewpoint about the support activities that should
be carried out under the ES (Chang et al. 2008).
For some years now, research in ES has attracted greater attention in various papers
published in academic journals and conferences. Yet, research in the ES context has not
yet reached maturity and several ES areas need further investigation. For instance, ES
post-implementation is neglected (Soh et al. 2000; Wagner and Newell 2007), as most
extant research focuses on the stage of ES implementation. It is apparent that much
work has been focused on the pre-implementation of ES while very little has been
concerned with post-implementation even though this phase is crucial for ES success
(Santhanam et al. 2007; Scott 2005).
The ES post-implementation phase engages many parties from within and outside an
organisation, and that makes managing knowledge in an ES complicated. Even though
the ES post-implementation phase is crucial for ES success, very little has been
concerned with this important phase (Scott 2005). Since ES end-users are from varied
user cohorts, experiences and skills, systems are reported to be ineffective with low ES
performance or return-of-investment even though the ES implementation phase was
carried out successfully (Wagner and Newell 2007). We believe that a gap of
knowledge among ES users in ES post-implementation plays an important practical role
in the high ES failure rates. This suggestion draws upon the recognition over the past
few decades of knowledge as a highly important resource for an organisation (Nonaka
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1994). In the ES context, knowledge increases from interaction among ES users where
the interaction facilitates the flow of people‟s knowledge and expertise, and contributes
by obtaining individuals‟ ES knowledge from different backgrounds and experience
(Leonard and Sensiper 1998). If poor communication occurs, it may prevent
organisations from achieving long-term ES success, cause severe problems in ES post-
implementation and put the performance of daily business activities at risk (Peng and
Nunes 2009; Worley et al. 2005). Any communication difficulties can hamper efforts to
share ES knowledge and understand differing ES views, and ultimately, hamper the
operation of the ES. As a result, this creates a knowledge gap between ES users which
is a main cause of unsuccessful adoption of an ES (Soh et al. 2000; Pan et al. 2007).
Employees are often obliged to employ the ES without a solid understanding of the ES
goals, benefits and weaknesses. This usually happens in the context of packaged
software as ES users were not involved in designing the software (Wagner and Newell
2007). As a result, ES users may not understand the ES completely (Liang et al. 2007).
Therefore, it is very important for an organisation to take advantage of the integration
of employees‟ knowledge (Caya 2008). By integrating employees‟ specialised
knowledge, the organisation utilises knowledge held by individuals in an appropriate
way for specific task requirements, increases their performance and minimises the
waste of others‟ time.
We define effective knowledge integration as the ability to successfully combine and
synthesise the ES value from others‟ expertise. From the overview of the knowledge-
based theory of the firm, KI effectiveness is required in order to make strategic moves
which focus on the three dimensions of scope, efficiency and flexibility (Grant 1996;
Awazu 2004). By focusing on this, we submit that KI is one of the critical success
factors for ES use. This is explored further in Chapter 2, where the definitions and
literature relating to KI are reviewed.
Stemming from a focus on ES post-implementation, we therefore investigate the impact
of KI effectiveness on the ES success by drawing upon the knowledge-based theory of
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the firm view (Grant 1996). Grant identified knowledge as an important factor for an
organisation in order to maintain its competitive advantage. By highlighting the dynamic
capability of knowledge as proposed in the KBT view, we argue that the success of an
ES is positively related to the ES users‟ ability to successfully combine others‟ ES
knowledge in operationalising the ES to perform their tasks. However, KBT is purely
theoretical and no studies report the operationalisation of the theory in the ES
context, except from case study explanations in very few research studies (Huang and
Newell 2003; Newell et al. 2004). Even though Alavi and Tiwana (2002), for example,
rely upon Grant‟s KBT as a conceptual underpinning, the important roles of passive and
active elements of KI such as common knowledge, organisational structure or flexibility
of integration still remain unexplored. Thus, our approach of proposing a quantitative
measurement of the antecedents of KI effectiveness, including the elements of passive
and active integration, contributes to the extension of the perspective of KBT from a
pure theoretical level to a more operationally oriented and empirically testable ground.
The importance and relevance of KI has been emphasised by KBT, with three factors
identified to facilitate the KI effectiveness. However, the factors are not specifically
focused on the ES context. To bring the contributors of KI effectiveness into our
specific research domain, we restructure these factors into three antecedents to fit the
ES post-implementation context. These antecedents are the Passive Integration of an
Organisation (PIO), the Active Integration of an Organisation (AIO), and the Active
Integration of an Individual (AII), as discussed further in Chapter 2 and Chapter 3.
Although the importance of KI is well-known in various contexts including science,
management, medicine (Caya 2008) and the success of projects and teams (Newell et
al. 2004; Tiwana and McLean 2005), no studies have empirically investigated KI for ES
success. More precisely, no study has explored the relationship between the
antecedents of KI effectiveness, its consequences for the goodness of individuals‟ ES-
knowledge base, and ES success in a complete nomological net.
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1.3 THE POSITIVE IMPACT OF KI ON ES SUCCESS
From the knowledge management viewpoint, KI is a part of KM that comprises all
activities concerned with transforming and manipulating knowledge (Jetter et al. 2006).
KI extends the scope of knowledge sharing, where the knowledge is not only
distributed, but effectively used to perform a task to generate new knowledge
(Herrmann et al. 2007). Therefore, KI provides mechanisms for the knowledge
application phase (Alavi and Tiwana 2002; Tiwana and McLean 2005).
The positive impact of KM and ES success has been recently established by Sedera and
Gable (2010). Extensive research on ES and KM has also been conducted in a number
of other studies (Devadoss and Pan 2007; Jones et al. 2006; Lee and Lee 2000; Pan et
al. 2001; Pan et al. 2007). In particular, research on how KM can effectively facilitate the
health and longevity of the ES lifecycle has commenced in the last ten years (Davenport
1998; Gable et al. 1998; Klaus and Gable 2000; Sumner 1999). This focus is due to the
perception that KM benefits can lead to business success (Li and Kettinger 2006). In
more current research, Gable et al. (2008) examined the relationship between KM and
ES success, with ineffective ES lifecycle KM, poor management of in-house expertise,
and inadequate employee retention strategies identified as key contributors to
disappointing ES benefits.
Many research streams on KM and ES have been investigated, including knowledge
sharing, transfer and creation. Nevertheless, there still appears to be a significant gap
regarding the connection that would make KM actionable by organisations as ES
investors (Ergazakis et al. 2002). Unlike typical information systems, the complexity of
an ES requires diverse types of system knowledge among all key stakeholders in the
organisation to be shared and integrated. Thus, to fill the research gap, we build on the
suggestion of KI as a critical ES success factor (Newell and Huang 2004; Pan et al. 2007)
and we focus on the KI aspect.
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1.3.1 Integration of Individuals’ ES-related Knowledge
The integration of knowledge is an essential key point in the success of an ES, and yet
many organisations underestimate the importance of it. The literature suggests that the
knowledge gap between individuals is the main cause of unsuccessful adoption of an ES
(Soh et al. 2000; Pan et al. 2007). One of the issues is that the ES is being embedded in
complex social contexts with many different stakeholders and different employment
cohorts influencing the ES implementation and use (Ko et al. 2005; Sedera and Gable
2010). ES knowledge requirements vary for each level of employment. Although all staff
are connected to the knowledge sources of an ES, not all levels of staff need to know
all the ES knowledge. For example, organisational knowledge is required for ES end-
users, and employees need to know how their tasks fit into the overall process and
how the process contributes to the achievement of organisational goals (Vandaie 2008),
including strategic planning, management control and operational control. More
importantly, they need to be able to recognise and obtain valuable ES knowledge from
other employee groups and subsequently integrate that knowledge with their existing
ES knowledge. We believe that fostering KI among ES users is an appropriate way to
benefit from the complexity of the ES context as previously discussed. Once knowledge
is sufficiently integrated, people may contribute to innovation without the need for
explicit communication to extract the value of others‟ expertise (Grant 1996).
1.3.2 The Benefit of KI on Individuals’ ES-knowledge base
The outcome of the KI process is that organisations are able to pool together a wide
range of expertise from various employees or departments to accomplish complex
tasks and apply ES best practice that they are comfortable with (Alavi and Tiwana
2002). This eventually leads to a goodness of collective knowledge (Leonard and
Sensiper 1998) where the integration of ES knowledge among individuals will form new
ideas or solutions that did not previously exist in the organisation. Such integration
generates consensus through collective input, which is essential to produce initiatives,
such as implementing ES solutions, and process innovations (Huang and Newell 2003).
The KI, which is guided by the KM activities, in turn, fosters knowledge base
Page | 13
capabilities, and consequently, product development performance (Revilla and Curry
2008). Henceforth, we refer to this collective ES knowledge as an ES-knowledge base.
We define our ES-knowledge base as “the collection of all the individuals‟ ES
knowledge, including tacit and explicit”. This research argues that the goodness of the
ES-knowledge base depends on the quality of the individual‟s ES knowledge integration
in an organisation, and that this plays an important role in the success of an ES. In other
words, the better the ES-knowledge base gained by individuals, the better the ES
performance. For example, integration helps individuals develop a better ES-knowledge
base to better align the ES with the organisation‟s business processes (e.g. system bugs
or misalignment between system design and actual practice) (Santhanam et al. 2007).
Thus, effective integration of ES-related knowledge benefits the organisation by
influencing a better level of individuals‟ ES-knowledge base. Accordingly, even if some
ES experts leave the organisation, a high level of ES performance remains. Thus, our
major aim is also to empirically demonstrate the relationship between the ES-
knowledge base and the success of the ES.
1.4 RESEARCH SCOPE
This research investigates the impact of KI effectiveness on ES success. We focus on ES
post-implementation in terms of operational ES by ES end-users. We are in agreement
with Willcocks and Sykes (2003) that the challenge of an ES is how best to use the new
system‟s capabilities. As they report in their study, the operation of an ES is where the
failure to deliver begins to be real in many organisations.
We frame our research questions within the perspective of ES operationalisation in the
post-implementation phase at the individual level wherein staff at management and
operational levels are key players who constantly interact and use the ES knowledge.
We limit our research to ES end- users from the cohorts of managerial and operational
personnel as these groups are direct ES users in organisations. The strategic group is
assumed to infrequently use the ES. As the research focus is on the ES
operationalisation, it is believed to be appropriate to gather the research data from the
Page | 14
managerial and operational groups only. It was also necessary to recruit respondents
with satisfactory levels of ES knowledge. This further justified the decision not to
collect data from ES end-users in the strategic group.
Only ES knowledge that is highly significant among two groups of employment cohorts
(managerial and operational) was included in our data analysis. Therefore, the analysis
concentrates on the contribution of two types of ES knowledge, namely, software
knowledge and business process knowledge. The integration of ES knowledge is specific
to a type of horizontal integration, so the data is gathered to examine the integration
practices among employees who work in the same department.
Horizontal KI is crucial for internal networks or departments in an organisation
(Pettigrew and Fenton 2000). However, little research has focused on the integration
of knowledge to address departmental performance problems (Patnayakuni et al. 2007).
Furthermore, rigid and hierarchical organisational structures may limit the interaction
among different functional areas in departments and organisations (Mohamed et al.
2004). Thus, to understand the integration of ES knowledge among staff, we focus on
formal and informal KI practices in departments with the guidance of KBT. Formal
practices are specifically focused on job rotation practices, meetings, trainings and
decision-making in the department, while informal practices emphasise interaction and
communication among employees in the department.
As we seek to understand the integration of ES knowledge for ES end-users who
frequently use the ES, we are only concerned with peer-to-peer KI within the targeted
department that intensely uses the ES. Haddad and Bozdogan (2009) identify this as
horizontal integration. In addition, Grant (1996) defines knowledge dependencies in an
organisation along two dimensions: horizontal (across different specialties) and vertical
(across different hierarchies). By definition, vertical integration in an organisation
involves all departments with various business processes. Thus, we do not consider the
vertical integration. Moreover, horizontal KI is found to be crucial for departments in
an organisation (Pettigrew and Fenton 2000).
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We scope our research to the perspective of ES end-users who work in private and
public organisations in Malaysia. The literature shows that ES implementation faces
additional challenges in developing countries in Asia and Latin America compared to
developed countries such as the US, Canada and the UK (Huang and Palvia 2001). Due
to a lack of ES experience and low IT maturity among other factors, organisations in
developing countries are less likely to succeed in ES implementation (Lin and Rohm
2009). A massive trend of ES implementation is evident in developing countries (Molla
and Bhalla 2006), yet many ES failures have been reported in these settings (Rajapakse
and Seddon 2005). Thus, we narrow our respondents to ES end-users in a developing
country, Malaysia.
The mode of data collection was a survey conducted by distributing questionnaires
directly to the targeted respondents. We identified relevant representatives in all the
respondents‟ companies in order to collect the completed questionnaires. Even though
this method led to promising responses, the number of returned forms was less than
expected. This limitation is acknowledged and was managed in the data collection. Even
though we are aware that there are other methods such as web-based surveys that can
facilitate multiple responses quickly and cost-effectively, the overall response rate to
the distributed survey was considered sufficient for this research.
1.5 THEORETICAL OVERVIEW
Our research model proposes the antecedents and consequences of KI effectiveness in
three sequential phases: 1) the three antecedents of KI effectiveness; 2) KI effectiveness
as the research focus; and 3) two consequences of KI effectiveness that point to the
impact on the goodness of ES-knowledge base and the impact on ES success
chronologically. We discuss the details of the research model in Chapter 3.
Although a number of prior studies have investigated KI, this model of the role of KI
effectiveness on ES success has not been proposed or empirically tested in previous
Page | 16
research. More precisely, this study is one of the first attempts to test the entire causal
model in a nomological net in the ES context. Table 1.1 summarises the key concepts
that are used in our research.
Table 1.1: Key concepts of the research
Construct Description
Antecedents of KI effectiveness
Passive integration of an
organisation (PIO)
Elements in an organisation which are not actively reflected in
integrating knowledge activities. The elements are static and should
be exploited wisely to create an effective integration.
Active integration of an
organisation (AIO)
Elements that are directly involved in KI practices in an organisation.
The elements are dynamic and require interactions, knowledge
transfers and changes among employees within departments, teams
or similar work groups that are initiated or led by the organisation
to integrate knowledge effectively.
Active integration of the
individual (AII)
KI practices that are actively performed by an individual. Individuals
are free to actively gain their knowledge from any sources and any
ways that they prefer. This depends on individuals‟ initiatives
without organisational restriction.
KI effectiveness ES end-users‟ ability to successfully combine others‟ ES knowledge
in utilising the ES to achieve their tasks.
Consequences of KI effectiveness
ES-knowledge base The combined collection of ES knowledge including tacit and explicit
knowledge.
ES success The level of ES performance.
In general, KI is a combination of knowledge to synthesise others‟ knowledge (Grant
1996; Kogut and Zander 1992). It extends the scope of knowledge sharing being
effectively used to perform tasks to generate new knowledge (Herrmann et al. 2007).
KI aims to optimise the use of others‟ knowledge by reducing knowledge transfer and
sharing between individuals (Caya 2008; Grant 1996; Spender 1996). Understanding the
role of KI in the ES context would represent an important step forward in attempts to
understand how to strengthen ES performance among ES users.
Furthermore, an ES is complex, has multiple functions, various business functions,
modules, multiple user cohorts, and diverse types of knowledge (Sedera and Gable
2010), which are all reasons to research the impact of KI. An ES involves large groups
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of people, including users from various levels, departments and divisions of the
organisation (Lin and Rohm 2009). In addition, an ES creates processes incorporating
both individual mastery and collective action and knowledge (Lengnick-Hall and
Lengnick-Hall 2006). Each ES user brings potentially valuable ES knowledge as the users
are drawn towards their roles and the system from diverse experience, skills and
expertise. ES users in an organisation need to cooperatively share and effectively
integrate their various ES knowledge to ensure better performance of the ES in their
organisation.
Here, we seek to understand the KI in two stages. First, we aim to identify and to
validate the antecedents of KI effectiveness. In order to understand the antecedents in
the ES context, we restructure components of the KI measures that were proposed by
Grant (1996) in KBT. Thus, by specifically focusing on the relevant ES context
viewpoints, we re-organise the influence factors for KI effectiveness into organisational
aspects and categorise them as passive elements in organisations, active KI practices in
organisations, and active KI practices at the individual level. We describe the passive
elements as the „passive integration of an organisation‟, which refers to the inactive KI
elements in an organisation. The active aspects that are directly involved in KI practices
in an organisation are described as the „active integration of an organisation‟. For the
individual level, we create a term called the „active integration individual‟ which we use
to look for the KI practices actively performed by individuals. Further details of these
concepts are discussed in Chapter 3.
Second, we examine the consequences of KI effectiveness. For the KI consequences,
we hypothesise that the more effective the integration of an individual‟s knowledge, the
better is their ES-knowledge base. Each employee brings a different level of ES-
knowledge base, and the types of ES knowledge requirement vary for each level of
employment. Hence, organisations must encourage their employees to effectively
integrate their diverse types of ES knowledge to gain and enhance their ES knowledge.
As discussed earlier, we apply the types of ES knowledge from Davenport (1998),
namely, software knowledge, business process knowledge and organisation knowledge.
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Consequently, we propose that the goodness of an individual‟s ES-knowledge base plays
an important role in the success of the ES. The better ES-knowledge base they gain, the
better is the ES performance: we argue that KI leads to a strong ES-knowledge base,
which in turn will have a positive impact on ES success.
To measure the success of the ES, we employ the success measures from the IS-impact
measurement model (Gable et al. 2008). This model has four quadrants, namely,
individual impact, organisational impact, information quality and system quality. The
System Quality construct is used to measure the performance of the system from a
technical and design perspective. Information Quality is a measure of the system output
concerning the quality of the information. Individual Impact refers to the measure of
influence of an individual‟s capabilities and effectiveness, while Organisational Impact
measures the organisational results and capabilities. According to the extensive
evidence offered by Gable et al. (2008) regarding the validity of Enterprise System
success, this research uses all four quadrants.
1.6 RESEARCH OBJECTIVES AND QUESTIONS
In light of the important role of KI, as discussed above (Section 1.1), we propose that
effective KI among ES users will have a beneficial impact on ES success. Our main
objective is to gain a better understanding of the influence of KI effectiveness on the ES
success in organisations. To achieve the key goal, we subdivide our aim into three
interrelated objectives based on the research questions.
First, we aim to identify and to validate the antecedents of KI effectiveness. In the ES
context, we identify three salient antecedents of KI effectiveness that are categorised as
passive elements in an organisation, active practices in an organisation and active
knowledge integration of individuals. The passive elements of integration for an
organisation include a good organisational structure and scope of knowledge
integration. The active practices are found among the ES end-users‟ common
knowledge and their frequency of task performance in operationalising the ES. The
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active KI for individuals refers to flexibility of the integration in re-configuring and
extending their ES knowledge. All these components in the antecedents are derived
from the KBT framework, and we classify them in three groups of factors. Second, we
seek to investigate the impact of KI effectiveness on the goodness of individuals‟ ES-
knowledge base. To further examine the ES-knowledge base, we validate the types of
ES knowledge proposed by Davenport (1998). Thus, we look for a better definition of
the ES-knowledge base by understanding the ES operationalising learning process and
the types of ES knowledge. Third, we examine the impact of the goodness of
individuals‟ ES-knowledge base on the ES success as a result of KI effectiveness. We
argue that the full potential impact of the ES-knowledge base on the ES success
depends on the extent to which the ES end-users integrate their ES knowledge. To
assess the ES success, we employ constructs and measures from the IS-impact
measurement model (Gable et al. 2008). A summary of the research objectives is
provided in Table 1.2.
Table 1.2: Research objectives
No. Objective
1 To identify and to validate the antecedents of KI effectiveness.
1.1 To know the effect of passive elements of KI in an organisation on the KI
effectiveness.
1.2 To know the effect of active elements of KI in an organisation on the KI
effectiveness.
1.3 To know the effect of active KI among individuals on the KI effectiveness.
2 To investigate the effect of KI effectiveness on the goodness of individuals‟ ES-
knowledge base.
2.1 To know whether the KI effectiveness influences the increment of individual‟s ES-
knowledge base.
2.2 To know the types of ES knowledge involved in ES-knowledge base development.
3 To examine the impact of the goodness of individuals‟ ES-knowledge base on ES
success.
For the purposes of consistency, we restrict our study to ES utilisation in all three parts
of the investigation including the KI effectiveness, the goodness of ES-knowledge base
and the ES success. Details of this are discussed in Section 1.3 above on research
scope. To achieve the objectives, we formulate our main research question as follows:
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Main Research Question: “What is the impact of KI effectiveness on ES success?”
In order to answer the key research questions, we build a hierarchy of research
questions, headed by the main research question and followed by three subsidiary
research questions. The first part of the study seeks to understand what factors
influence the effectiveness of KI. As we discussed above in Section 1.1 regarding the
research background, there is a wealth of literature on KI in various disciplines. Since
there is little work focusing on KI from the KBT perspective, we drive our research
from the KBT theoretical viewpoint to guide our empirical data collection by the
following research question:
Research Question 1: “Do the constructs of KBT make a substantial positive
contribution to the KI effectiveness?”
Research Question 1 seeks to identify the significant antecedents for KI effectiveness
for the ES context. As an ES involves individual knowledge and collective knowledge in
an organisation (Lin and Rohm 2009), we aim to organise the antecedents into
organisational and individual aspects. Here, we propose three relevant factors that
contribute to the effectiveness of KI. First, we try to recognise the passive elements in
an organisation that influence KI effectiveness. Second, we aim to classify the active
collective KI practices in an organisation among employees. Third, we look for the
aspect of active KI practices from the individual view. We hypothesise that each
antecedent makes a unique contribution to KI effectiveness.
Our next research question seeks to know how the KI effectiveness can build a better
collective ES knowledge, which we refer to as goodness of ES-knowledge base. In order
to understand the concept of ES-knowledge base, we use the literature of ES
knowledge types as explained by Davenport (1998). The following research question
guides this part:
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Research Question 2: “What is the influence of KI effectiveness on the individual‟s
ES-knowledge base?”
Research Question 2 focuses on identifying the impact of KI effectiveness on the
goodness of individual ES-knowledge base for ES users. This question tries to
understand the extent to which the effectiveness of KI practices affects the level of an
individual‟s ES-knowledge base. In addition, we propose that integrating certain ES
knowledge types enhances the ES-knowledge base development. Thus, this research
question seeks to examine the formation of the ES-knowledge base from the
perspective of ES knowledge types.
One of our aims is to better understand the development of the ES-knowledge base
among individuals and how this accumulated knowledge can make an impact on the
success of ES in organisations. Thus, the third phase is to identify how the goodness of
individuals‟ ES-knowledge base has a better impact on ES success, as a result of the
effectiveness of KI. To measure the ES success, we use the IS-impact measurement
model of Gable et al. (2008). The research question that guides this part is:
Research Question 3: “What is the impact of ES-knowledge base on ES success?”
1.7 SIGNIFICANCE OF RESEARCH
This research contributes to KI in the ES context by proposing KI effectiveness as one
of the critical success factors for ES. The research specifically investigates the
antecedents and consequences of KI effectiveness, and the influence of KI effectiveness
on ES success in organisations. While many companies struggle to maximise the return
on their ES investments, there is limited understanding of the importance of KI on the
success of an ES after it has been implemented. Thus, this research aims to investigate
the impact of KI on ES post-implementation in terms of the ES operationalisation
success which has to date been neglected by researchers. In addition, this research is
among the first to empirically examine the impact of KI on ES success in the ES post-
Page | 22
implementation phase. Most research in KI has focused on group or project team
performance, with less emphasis on ES post-implementation and no studies examining
ES operationalisation from the viewpoint of ES success (Newell et al. 2004; Tiwana and
McLean 2005).
This research presents the first empirically validated antecedents for KI effectiveness of
the KBT in the ES context. Previous studies of applications of KI have focused on
qualitative research. The KI effectiveness measurement rests on a foundation of theory
proposed by Grant (1996). It is anchored to a main theoretical perspective of dynamic
capabilities of knowledge in relation to influence factors for KI effectiveness. By deriving
a set of specific measures that can be used to quantify the constructs of KI effectiveness
influence factors, this research makes some contributions to shifting the KI perspective
of KBT from a pure theoretical level to a more operationally oriented and empirically
testable ground. Thus, this research makes an important contribution by making the
quantitative measurement of antecedents for KI effectiveness feasible.
The study has practical implications as it offers a guideline on how to make KI practice
effective in organisations through evidence of the importance of an organisation‟s
passiveness and activeness and the importance of the activeness of individuals. This
study also offers a key message to organisations to consider that making huge
investments in ES without taking care of the KI factors among their employees will not
ensure the success of the ES. Figure 1.2 summarises the brief outline of our research
model.
Figure 1.2: Research model outline
Effective
integration
factors
KI
Effectiveness
ES-Knowledge
Base and ES
Success
Antecedents
Consequences
Page | 23
1.8 RESEARCH DESIGN
A research design is a logical plan that has a number of major steps including data
collection and analysis (Yin 2003). We detail our research design including the data
collection process in a flow diagram as presented in Figure 1.3.
The stages of process in this research are represented in rectangular text boxes: the
arrows refer to the direction of information flows, and others are shown in the legend.
Our research design contains six steps: 1) define the research problem; 2) review the
literature; 3) develop the theoretical model; 4) develop the hypotheses; 5) conduct the
survey; and 6) interpret the findings.
Figure 1.3: The research design
Legend
Process
Output
Document
Define research problem
Review the literature
Conduct survey
Interpret findings
Research questions
and objectives
Thesis Model and
hypotheses
results
Develop theoretical model
Develop hypotheses
Research
model
1
2
3
4
5
6
Survey
instruments Survey data
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As shown in Figure 1.3, the research problem is clearly defined, research questions and
objectives are produced (in this chapter) and the important terms related to ES are
described. These elements are informed by a thorough literature review (refer to
Chapter 2). We employ the knowledge-based theory of the firm by Grant (1996) to
operationalise our survey constructs of knowledge integration effectiveness and its
antecedents by identifying them from the literature and previous case study evidence.
Next, we develop our understanding of ES knowledge by Davenport (1998) to examine
the impact of KI effectiveness on the goodness of individuals‟ ES-knowledge base. Later,
we adopt the IS success quantitative surveys of Gable et al. (2008) for evaluating ES
success. We then develop our research model, followed by the research hypotheses
(refer to Chapter 3). The survey instrument is generated from the data collection
process (refer to Appendices). We discuss all of our findings regarding the research
model and hypotheses in Chapter 5.
1.9 SUMMARY
In this research, the main objective is to understand and investigate the impact of KI
effectiveness on ES success. This research is rested on a foundation of theoretical
propositions by Grant (1996) that are anchored to a central theoretical perspective of
dynamic capabilities of knowledge in relation to the influence factors for KI
effectiveness. The KBT places a great emphasis on the importance and relevance of KI
for an organisation‟s competitiveness (Grant 1996). To explain the impact of KI on ES
success, this research focuses on the perspectives of individuals in two groups of
employment cohorts, namely, operational and managerial groups. The research is
interested in identifying and validating individuals‟ perspectives in these two groups,
thus, the unit of analysis is the individual (Babbie 2001).
To identify the relationship between them, the research focuses on identifying the
antecedents and the consequences of KI effectiveness and the final impact on ES
success. It is argued that the knowledge, either explicit or tacit, in employees
contributes to a higher level of performance (Grant 1996), and we hypothesise that
Page | 25
having a better-integrated knowledge base yields an even higher level of ES success.
The ES success is identified from the IS-impact measurement model (Gable et al. 2008).
This model has four quadrants as discussed above, namely, individual impact,
organisational impact, information quality and system quality. The system quality
construct is used to measure the performance of the system from a technical and
design perspective. Information quality is a measure of the system output concerning
the quality of the information. Individual impact refers to the measure of influence on
an individual‟s capabilities and effectiveness, and organisational impact measures the
organisational results and capabilities. In light of the extensive evidence offered by the
model regarding the validity of ES success, this research uses all four quadrants.
In summary, this research has three main inter-related objectives. Firstly, to identify
and to validate the three antecedents of KI effectiveness; secondly, to investigate the
influence of KI effectiveness on individuals‟ ES-knowledge base; and thirdly, to examine
the impact of individuals‟ ES-knowledge base on ES success.
1.10 OVERVIEW OF THESIS
In this chapter, the need for better understanding of the impact of ES-knowledge base
on the success of Enterprise Systems is discussed. The intended research questions and
research objectives are also presented. Consequently, the remainder of this report and
the contents of each chapter are structured as follows:
Chapter 2: Knowledge Integration for ES Success
This chapter reviews the literature relevant to the research scope. In addition, the
chapter presents the current literature with some analysis that motivates the research
model of this study.
Chapter 3: The Research Model and Hypotheses
This chapter develops a theoretical model that is presented together with the research
questions and hypotheses.
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Chapter 4: Survey Development
This chapter discusses the methodology, including the survey design that is used to
accomplish the objectives of the research.
Chapter 5: Analysis and Results
This chapter discusses the research sample, the data analysis, hypotheses test and
discusses the research findings.
Chapter 6: Contributions, Limitations and Future Works
This chapter discusses the research contributions, limitations, implications and future
research.
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CHAPTER 2:
KNOWLEDGE
INTEGRATION FOR ES
SUCCESS
2.1 INTRODUCTION
Large, complex and multi-stakeholder Enterprise Systems (ES) have gained prominence
since the 1990s. Wang and Hamerman (2008) report that investment in the ES remains
the top general IT spending priority. Along with the growing interest in ES, knowledge
management (KM) has been identified as an important activity for the health and
longevity of an organisation (Gable 2005; Gable et al. 1998). A wealth of research
suggests KM is a critical success factor for the ES lifecycle and that managing knowledge
for ES success is vital (Lee and Lee 2000; Pan et al. 2007; Volkoff et al. 2004). Despite
KM being identified as a key critical success factor for ES in the late 1990s,
understandings of the impact of knowledge on ES success are still imprecise, with many
fundamental questions and enduring issues remaining largely ignored.
Given that KM is a broad area, to be more specific, we identify the importance of
knowledge integration (KI) as a part of the KM explanation for ES success. Integration
of ES knowledge is central to the success of ES analysis, design, implementation and
maintenance. Because of the complex and large scope of applications, an ES creates
substantial changes in organisational and employee work practices. KI is concerned
with the combination and re-combination of knowledge to synthesise other knowledge
(Grant 1996a; Grant 1996b; Kogut and Zander 1992). To build in-depth understanding
of KI, we need to further explain how individuals, groups and organisations integrate
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their knowledge. Seeking answers to these questions would not only add value to the
research field, but would also benefit industry. Furthermore, in the context of ES, the
common ES knowledge needs to be shared and re-used among ES stakeholders, so that
the essential ES knowledge can be successfully integrated.
By expanding the research design provided in Chapter 1, Figure 2.1 illustrates our
literature review process in more detail. In general, the process of searching the
relevant literature was carried out in five stages. In the first stage, we defined the
research strategy to find appropriate sources for our study. The strategy included
identifying top refereed journals in the information system area such as MIS Quarterly,
Journal of MIS, Journal of Association of IS, Information System Research and others
from popular databases ProQuest and Science Direct.
Figure 2.1: Literature review design
The A-ranking conferences in IS were also considered and prioritised, including the
International Conference on Information Systems, Pacific Asia Conference on
Information Systems, European Conference on Information Systems, and Australian
Conference on Information Systems. In the second stage, we searched the literature by
using key questions and terms. For example, papers were collected by the use of
search terms including “knowledge management”, “knowledge integration”, “knowledge
base”, “Enterprise Systems” and “Enterprise Resource Planning”. In the next stage,
abstracts from the collected papers were reviewed in order to ensure we captured the
issues relevant to our research topic, and to eliminate any irrelevant material.
Subsequently, in the fourth stage, all the appropriate papers, books, theses and other
resources including soft copies and hard copies were selected. Finally, in the fifth stage,
Define search strategy
Search key questions and
terms
Review abstracts
Select papers, books and
theses
Gather evidence to address key questions
1 2 3 4 5
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every source that provided evidence relevant to our key questions, terms and concepts
was gathered to ensure all the relevant literature was adequately covered. Some meta-
analyses were conducted for certain topics as discussed further in this chapter.
The following sections discuss the literature relevant to ES in the post-implementation
phase as our research context. Previous studies on knowledge, KM, KI from the
theoretical point-of-view and KI relationships with ES-knowledge base and ES success
are surveyed.
2.2 ENTERPRISE SYSTEMS: THE RESEARCH CONTEXT
Organisations make large investments in acquiring an ES or Enterprise Resource
Planning system, expecting positive impacts for the organisation and its functions
(Davenport et al. 2004). Yet, there exists much controversy surrounding the „potential‟
impacts of these systems with some studies reporting positive impacts of ES in
organisations, while others showing nil or detrimental impacts (Colmenares et al. 2008;
Francoise et al. 2009; Soh et al. 2000). Besides the huge investment, numerous studies
report the ES failures (Scott and Vessey 2002; Zabjek et al. 2009) with businesses
continuously losing billions of dollars annually (Zhang et al. 2005). Organisational
benefits from an ES continue to be difficult to achieve and unpredictable (Strong and
Volkoff 2010). Many organisations are dissatisfied with the benefits obtained from their
ES investments (Sedera and Gable 2010). For example, one Standish Group study
reported that less than 10% of ES implementations succeed with full functionality within
forecasted cost and timeframes (Momoh et al. 2010). ES implementation remains
problematic as many organisations have experienced (Sathish 2006), including Allied
Waste Industries, Waste Management Inc., Hershey Food Corporation, Dell
Computer, Boeing, Dow Chemical, Mobil Europe, and Kellogg‟s (Dey et al. 2010).
Considering the high cost of ES implementation, it has to be put to use in order to
receive the maximum benefit. Otherwise, there is no point in introducing the ES. While
an ES is a solution that has a number of potential applications, many companies have
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failed when trying to implement it, yet the ES market is predicted to grow at a steady
rate of 6.9% reaching $50 billion by 2012 (Sedera and Gable 2010). Many times it is
found that the ES does not serve the purpose for which it was implemented.
Therefore, understanding why these companies failed is an important factor in learning
how to operationalise an ES effectively.
2.2.1 ES Failures and Challenges
In the late 1990s, many companies were faced with the Y2K problem in their legacy
systems. With Y2K compliance as the driving concern, many companies decided to
implement an ES as the solution that would at the same time address the need for a
common systems platform to integrate all aspects necessary to support their business
(Ross et al. 2003). By 1999, most companies had already implemented their Y2K
solution which contributed to the rapid growth of ES sales. As well as a promise to
solve the specific Y2K problem, it was also claimed that the ES would meet
organisations‟ needs by reducing system operating costs, and increasing system capacity
with reliable information access via a single database. However, after spending millions
of dollars on an ES, many companies experienced serious problems with many of them
dissatisfied with results achieved to date from their ES implementation.
Since current research indicates a 90% failure rate (Zabjek et al. 2009) despite large
investments, the high failure rate in implementing the ES has become a major concern
(Dey et al. 2010). Some studies have identified factors for ES failure in organisations.
Recently, studies on critical success factors for ES have emphasised the importance of
human or social factors more than technical and economic aspects (Wang and Chen
2005; Yeh and OuYang 2010). These factors include failure to utilise the ES and
inappropriate or improper use (Klaus and Blanton 2010; Lin and Rohm 2009; Scott
2005). Other common causes of failure include problems with consultants, excessive
customisation and complexity of the ES (Davenport et al. 2004; Gargeya and Brady
2005; Sedera and Gable 2010). Misalignment of business strategy with the ES solution,
lack of adequate training for both management and employees, and inadequate
understanding of business requirements and ES implementation have also been
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identified (Momoh et al. 2010). Figure 2.2 illustrates the peak intensity of ES
dissatisfaction through the number of publications on ES challenges and failure factors.
0 10 20 30 40 50 60 70
1997-1999
2000-2004
2005-2009
ES papers
Figure 2.2: Publications on ES challenges and failures (adapted from Momoh et al.
(2010))
As shown in the figure, over more than a decade from 1997 to 2009, many research
papers discussed the ES challenges and failures in various industries. In the early stage
of ES implementation in the late 1990s, a few research papers had already reported on
ES challenges. Afterwards, more and more organisations realised the significant
problems in their ES implementations. We can see that numerous descriptions of ES
failures have appeared in research reports and the business press, with these
publications increasing to more than 60 papers in 2004. Publication figures continue to
show a high number of ES failure reports with only a slight reduction to 50 papers in
the period 2005 to 2009. This indicates that ES failures still receive significant interest.
This is because the failure of the ES will put at risk the performance of daily business
activities, and ES implementation continues to present risks to adopting organisations.
The failure risk of ES operationalisation in organisations can be decreased if
organisations know exactly what critical factors affect success. If these critical factors
are not established within the ES, success could be jeopardised.
2.2.2 ES Post-implementation: Why is it Significant?
Many ES studies focus on the implementation phase. In these works, the operational
phase or ES post-implementation phase is ignored (Wagner and Newell 2007).
However, the post-implementation phase is important, with numerous organisations
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reporting their ES projects to be fraught with challenges during this stage (Momoh et al.
2010). Although many IS researchers recognise the importance of ES post-
implementation (Colmenares and Otieno 2008; Francoise et al. 2009; Gargeya and
Brady 2005; Scott 2005), current research in this topic is extremely limited.
Considering the heavy investment in ES by organisations, research into this issue is
critical. One of the critical points of the ES post-implementation is the appropriation of
the system by its users. Worley et al. (2005) explain that the appropriation of the ES
depends not only on training, but requires the definition of how the ES and the users
will be mutually adapted with reference to their knowledge and competencies.
Managing knowledge in the ES post-implementation stage is an intensive process that
necessarily draws upon the experience of a wide range of people with diverse
knowledge capabilities. Stakeholders may possess diverse skills, expertise, control of
key resources and domain knowledge (Sathish 2006). The complex ES tasks involve
many stakeholders (such as managers and operational staff) and diverse knowledge
capabilities (ES knowledge types) across the complete ES lifecycle from implementation
to post-implementation. It is widely argued that the knowledge brought to bear at the
time of implementation changes vastly as a result of employees interacting with the ES
in the post-implementation phase. Certainly, ongoing changes and adjustments are
necessary to optimise the way the ES is operating and to improve the way it supports
the business. This is because what people learn, and what the organisation comes to
know, arises from interactions among employees to exchange and build their collective
knowledge in each of the specialty areas (Ruggles 1998). Of more critical consequence,
the failure to appropriately manage the employees‟ ES knowledge may lead to an
organisation‟s business disaster.
According to Ross et al. (2003), the ES journey has five phases that relate to
organisational performance: design, implementation, stabilisation, continuous
improvement and transformation. As shown in Figure 2.3, stabilisation, continuous
improvement and transformation are the phases that occur after the implementation
stage. The stabilisation process obliges organisations to pass through certain difficult
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periods to receive post-implementation benefits in the subsequent stages of continuous
improvement and transformation. Figure 2.3 illustrates the phases of the ES.
Figure 2.3: ES phases (adapted from Ross et al. (2003))
The complexity of an ES can affect nearly every aspect of organisational functioning
(Markus et al. 2003). Implementation brings a new management challenge, as
management must take an approach to the ES that is different to the approach to their
legacy systems (Sumner 2003). Other challenges involve ongoing system maintenance
and upgrading, sharing knowledge among implementation partners to reduce
knowledge asymmetry and barriers, and ensuring all stakeholders are in sync about the
ES (Sathish 2006). Another major challenge is ensuring adequate knowledge transfer
from the project team to the support team that is responsible for ES post-
implementation (Maheshwari et al. 2010). Loss could be caused or the success of the
ES could be threatened if risk factors such as diverse employee or user skills,
management structure, training and social commitment are not dealt with
appropriately. Insufficient user training, for example, is common in ES implementation.
Social commitment, such as poor communication between employees or departments,
may prevent organisations from achieving long-term ES success. These failures can
cause severe problems in ES post-adoption (Peng and Nunes 2009).
While process integration is critical in the ES design and implementation phases, KI
needs serious consideration in the ES post-implementation phase. The ES
implementation introduces major organisational changes, which mean that when an
organisation implements an ES, it can expect performance to drop and, revenues to fall
Design
Implementation
Stabilisation
Continuous improvement
Transformation
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as a result of the employees learning the new system. Organisations require sufficient
training on how the system changes their business processes, and need to work with
experts to resolve any possible bugs to adjust to the new environment immediately
following the implementation. Failure to provide sufficient training and failure to
prepare new roles for system users are among the common problems that cause
delayed or diminished performance benefits to organisations.
The best system in the world will not improve an organisation‟s performance if the
employees do not know how to use it (Francoise et al. 2009). Accordingly, the ES
would certainly fail if the key employees lack relevant skills and knowledge about the
new process or if they are not educated properly (Zabjek et al. 2009). One of the
common problems associated with ES post-implementation is poor ES-related
knowledge among end-users, who, when the system is up and running, do not know
how to use and maintain it continually. It is important for the ES users to capture
implicit ES knowledge from the ES experts in their organisations. This knowledge is
then required to be shared effectively across colleagues or staff in their organisations.
However, problems occur when the employees show inadequate knowledge sharing
behaviour, and lack KI practices. Problems of insufficient training, poor user interface
and system design, perceptions of the system being too hard to learn (or to
understand), lack of experience in using an ES, demotivation and lack of confidence in
the system are critical problems in the ES post-implementation phase. As a result, a
number of issues arise such as erroneous data input, poor use of the system and
employee resistance (Momoh et al. 2010).
In certain situations, due to reluctance to change and insufficient training, the managers
as key users of the ES (Shang and Seddon 2002) may refuse to use the ES in real
practice. This may affect the success of the ES as a result of the organisation not being
able to improve planning or forecasting activities. There are cases of top managers
lacking sufficient experience of operational situations, operational expertise or technical
knowledge to make appropriate decisions (Peng and Nunes 2009). If decisions are
made without the involvement (integration of knowledge) of other experts, it will lead
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to inappropriate ES maintenance. Top managers‟ attitudes will also affect their
subordinates‟ views of the project. Top management support is frequently reported as
a crucial factor affecting the success of the ES (Gargeya and Brady 2005). For example,
without this support, conflicts cannot be solved efficiently. In addition, if operational
staff (as the main users of the ES who are using it extensively in their daily work) are
reluctant to use the ES, an organisation‟s operational efficiency can be significantly
reduced.
2.3 KNOWLEDGE MANAGEMENT
In parallel with the rapid increment of ES implementation, there has been growing
recognition of the importance of managing knowledge for ES longevity (Sedera and
Gable 2010). Information Systems research has contributed to the better management
of ES by identifying the salient factors influencing ES performance, and managing
knowledge has been suggested as one of the most influential success factors that has
the potential to affect all phases of the ES lifecycle beyond implementation.
2.3.1 Knowledge: Is it so Important?
Organisations have engaged in “hectic” business activities, and knowledge has been
identified as a strategic resource (Grant 1996). Defined by Davenport (1998) as a fluid
mix of framed experience, values, contextual information and expert insights;
knowledge underlies software, technologies, business operations and organisational
activities. In organisations, knowledge often becomes embedded not only in documents
or repositories but also in organisational routines, processes, practices and norms
(Worley et al. 2005). Thus, to remain competitive, organisations must find the best
ways to manage their knowledge resources.
The ability of organisations to manage their knowledge resources is linked to their
ability to better integrate specialised knowledge (Kogut et al. 1992). The types of
knowledge that are transferred are described as tacit and explicit knowledge. However,
most of the knowledge that makes an organisation competitive is tacit. The integration
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of tacit knowledge is difficult because it is difficult to write such knowledge down, or to
transfer or express it. That is why the integration process is highly dependent on
people and their personal interactions.
Tacit knowledge is that which is difficult to articulate, in a way that is meaningful and
complete (Teece 1998). It is learned through practice, trial and error, and also through
experience (Leonard and Sensiper 1998). Individuals having a given competence can
create knowledge, but also require knowledge from others in order to be able to apply
their competencies (Worley et al. 2005). In organisations, individuals from different
backgrounds, experiences and disciplines draw upon their pool of knowledge
(knowledge base) to contribute, and this knowledge cannot be obtained in any other
way except through interaction (Leonard and Sensiper 1998). The integration of tacit
knowledge can be achieved only when communications take place in face-to-face
situations (Teece 1998).
Supported by information, communication technology and environment, each individual
in an organisation facilitates the integration. Technology, however, must be married to
face-to-face interactions in order to create more effective integration. This is because
each individual in an organisation brings their held knowledge, expertise and specialised
skills to bear on tasks of varied nature (Hoegl and Gamuden 2001).
2.3.2 Knowledge Management Process
KM strategies appear to be necessities for organisational effectiveness and
competitiveness in the new millennium (Alavi and Tiwana 2002). Alavi and Leidner
(2001) describe the KM lifecycle in four phases: knowledge creation, knowledge
retention, knowledge transfer, and knowledge application (or re-use). The knowledge
creation process refers to the development of new knowledge through the interplay of
tacit and explicit knowledge at different levels (Alavi and Tiwana 2002). It can increase
the employees‟ competence, increase their performance of existing tasks (Chen and
Edgington 2005) and problem-solving (Alavi and Tiwana 2002), and is essential for
generating knowledge synergies or renewing existing ones (Tanriverdi 2005). As
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knowledge creation is an ongoing process, it can be used in further rounds of
knowledge application (re-use) in other problems (Markus 2001).
To improve performance, the knowledge processes should create value. Value is
created only when knowledge is distributed, transferred and applied where it is needed
(Alavi and Tiwana 2002). Organisational value is created when knowledge is used to
produce effective performance through its application. Markus (2001) identifies four
different types of knowledge re-use situations involving shared work producers, shared
work practitioners, expertise-seeking novices and secondary knowledge miners.
Because of the differences in knowledge, re-users need different things from their
knowledge repositories; hence, Markus argues that the quality and contents of their
knowledge repositories are important factors in the success of knowledge re-use.
2.3.3 Knowledge Management Focus
In surveying the 240 refereed articles collated for the literature review, we find that
many areas of KM have been researched since 2000. Figure 2.4 illustrates the evolution
of the research from 2000 to 2008. As shown in the graph, the areas receiving the
most focus in the period 2004 to 2008 are KM exploration and KM process. Studies on
knowledge in organisations and teams reached the top position of KM research in 2000
but focus on this area diminished gradually from 2002 to 2006. However, investigation
of this area experienced a resurgence in attention with a steep climb since 2006. Levels
of focus on the areas of KM success and KM research in industry fluctuated. In general,
the data shows that the areas of KM exploration, KM process and KM in organisations
and teams are the top ranking in KM research focus with the focus in these areas
continuing to increase in the three years to 2008.
Analysis of the research trends in this period indicates that our study is in line with
recent research interests as it is closely related to KM for organisations, KM process
and KM for industry. Our research focuses on the impact of integrating knowledge on
ES success by investigating the integration influences from various industries. In
particular, we investigate the influence factors of KI effectiveness and the consequences
Page | 38
of KI for goodness of ES-knowledge base among employees, and the impact of the ES-
knowledge base on ES success. Each of these aspects is discussed in the ES context.
Figure 2.4: Evolution of KM research
In Information Systems research, there has been extensive research conducted on ES
and KM (Devadoss and Pan 2007; Jones et al. 2006; Lee and Lee 2000; Pan et al. 2001).
Li and Kettinger (2006) suggest that good KM leads to business success. In particular,
managing knowledge has been identified as a critical success factor for the ES lifecycle in
management in several IS studies in the ten years since 2000 (Davenport 1998; Gable
et al. 1998; Klaus and Gable 2000), with the emphasis on how effectively KM can
facilitate the health and longevity of the ES lifecycle. In more recent years, Gable et al.
(2008) have identified poor management of in-house expertise and ineffective ES
lifecycle KM as key contributors to disappointing ES benefits.
2.3.4 Knowledge Management and Knowledge Integration
KI is a key facet of knowledge application (knowledge re-use), the least theoretically
attended phase of KM in organisations (Alavi and Tiwana 2002). Knowledge, especially
KM Focus
0
5
10
15
20
25
2002 2004 2006 2008
Year
Nu
mb
er o
f ar
ticl
es
KM process
KM success
KM exploration
KM for organisation/team
KM for industry
KM model, network,integration and others
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tacit knowledge such as expertise and know-how, is held only in the individual‟s mind.
This tacit knowledge is manifested only through action, which resides primarily in its
application. This knowledge can be pooled and recombined to create group-level
knowledge as the outcome of the integration process (Alavi and Tiwana 2002).
Distributed knowledge can be applied either through transfer or integration. However,
transfer application is inherently time-consuming and inefficient. According to Alavi and
Tiwana (2002), integration provides a faster and relatively inexpensive mechanism
because it involves synergistic synthesis of different specialised knowledge without
extensive communication or transfer of that knowledge. The terms „knowledge
transfer‟ and „knowledge integration‟ are sometimes confused. To precisely distinguish
between these two processes, knowledge transfer refers to the situation where
individuals identify and communicate their uniquely held information, while KI refers to
the situation where several individuals combine their information to create new
knowledge (Okhuysen and Eisenhardt 2002).
2.4 KNOWLEDGE INTEGRATION: A THEORETICAL
VIEWPOINT
2.4.1 The History of Knowledge Integration
The research focus on integration has been led by Lawrence and Lorsch since 1967.
They described integration as “the quality of the state of collaboration that exists
among departments that are required to achieve unity of effort by the demands of the
environment” (Lawrence and Lorsch 1986, p.11). In a hectic world, organisations
urgently need process effectiveness with shorter lead-time, higher product quality, and
lower manufacturing costs (Hauptman and Hirji 1999). These requirements triggered
awareness of the importance of KI in the mid-1990s, with integration of knowledge
suggested to be the basic necessity for organisations to produce competitive goods and
services (Demsetz 1991).
Page | 40
Accordingly, Grant (1996) introduces the knowledge-based theory of the firm (KBT) to
enlighten the importance of knowledge that insufficiently explains by the resource-
based view of the firm (RBV). The RBV places emphasis on the properties of resources,
and accounts for both tangible and intangible resources and capabilities, including
human resources, skills, brand recognition and knowledge (Barney 1991). Among these,
knowledge is often considered an organisation‟s most important resource (Grant
1996a; Wang et al. 2009).
Building on the RBV viewpoint, the KBT considers knowledge as a unique and
strategically significant resource (Grant 1996a; Grant 1996b) by focusing on KI (Barney
et al. 2001; Barney 2001) as an important factor in achieving and maintaining
competitive advantage. In this view, knowledge is more important for organisations
than financial resources, technology or any other company asset (Marquardt 2002).
One notable feature of KBT is its emphasis on the importance of specialists‟, or
specialised, knowledge. An integration of the specialised knowledge is fundamental to
enhance organisational capability to create and sustain competitive advantage. Likewise,
an ability to integrate ES stakeholders‟ knowledge (specialised knowledge) is essential
to ensure the success of the ES. In fact, knowledge integration is identified as a key
problem in the ES implementation (Pan et al. 2001). This is due to the integration of
knowledge that plays an important role in affecting the success or failure of an
organisation (Ravasi and Verona 2001).
2.4.2 The Recognition of KBT
To show the relevance and importance of the KBT in this study, 240 articles from
literature were analysed to identify the wide use of the KBT. Figure 2.5 summarises the
theories that were employed in the KM articles, showing the five most cited theories.
The x-axis refers to the number of articles cited, with the theories shown on the y-
axis.
Among the five theories, the knowledge-based theory of the firm was the most cited in
KM research, followed by organisational theory, resource-based theory, grounded
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theory and social exchange theory respectively. The analysis indicates that the KBT was
top ranked in KM studies.
0
5
10
15
20
25
30
Theory
Knowledge-basedtheory of the firm
Organizationaltheory
Resource-basedtheory
Grounded theory
Social exchangetheory
Figure 2.5: The theory used in KM articles
KBT stands on KI as its key theoretical viewpoint. From the ES post-implementation
perspective, the ability to integrate specialised ES knowledge between employees is
among the primary tasks for organisations in order to ensure good performance of
their ES. Employees must effectively integrate their wide range of ES knowledge, skills
and experiences to address ES operational issues. Effectively integrating their diverse ES
knowledge into collective know-how can be expected to deliver good performance of
the system, which we refer to as ES success. Therefore, we believe that KBT provides
the ideal theory for the ES context of this study because of the crucial need for KI in
the ES adoption stage, as well as it being one of the prominent theories in the KM
research field as demonstrated above in Figure 2.5.
2.5 KNOWLEDGE-BASED THEORY OF THE FIRM
As an extension of the RBV, Grant (1996) proposes the knowledge-based theory of the
firm to emphasise the importance of KI in creating value for organisations. Kogut and
Zander (1992) propose that KI is a recombination of existing knowledge to exploit and
apply the potential knowledge. In agreement with this position, Grant (1996) argues
that knowledge is a key competitive resource that enables organisations to transform
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old capabilities into new ones by recombining existing knowledge (Hustad 2007).
Consisting of both the shared knowledge of individuals and combined knowledge, KI
emerges from their interactions (Okhuysen and Eisenhardt 2002). This combination of
individual knowledge will be transformed into organisational knowledge as a collective
knowledge which is developed communally, over time, in interactions among individuals
in organisations (Leonard and Sensiper 1998). It involves a dynamic process of linking,
connecting, distinguishing, organising and structuring ideas (Clark and Linn 2003). Once
specialised knowledge is sufficiently integrated, individuals may contribute to innovation
without explicit communication because they understand how all the individual
operations in an organisation fit together. The various definitions of the concept of KI
from previous studies are showcased in Table 2.1.
Table 2.1: Knowledge integration definitions
Source Knowledge integration definitions
Alavi and Tiwana (2002) “The synthesis of individuals‟ specialised knowledge into
situation-specific systemic knowledge”.
Okhuysen and Eisenhardt (2002) “The actions of group members by which they share their
individual knowledge within the group and combine it to
create new knowledge”.
Clark and Linn (2003) “The process of adding new ideas and sorting through
connections to develop a cohesive account of scientific
phenomena”.
Huang and Newell (2003) “An ongoing collective process of constructing, articulating
and redefining shared beliefs through the social interaction of
organisational members”.
Tiwana and McLean (2005) “The coordinated application of individually held specialist
expertise in the accomplishment of tasks at the project level”.
From the various definitions of KI, we can conclude that KI depends on how individuals
know and integrate their individually held knowledge, as the same knowledge can be
known in multiple ways. For example, by seeking, confirming, combining or presenting
knowledge, shifting leadership in the group or asking others to contribute, individuals
create new knowledge from the same information. To synthesise individuals‟ specialised
knowledge into the collective knowledge of an organisation, Tiwana (2001) suggests
that three conditions must coexist. First, individuals must have access to essential
Page | 43
knowledge components. Second, they must be willing to integrate the knowledge
components and lastly, the individuals must be able to integrate the knowledge.
Although knowledge is rooted in individuals, knowledge must be integrated as a
collective knowledge to affect the organisation. According to Nonaka (1994), the
process of integrating knowledge is the movement of knowledge in an upward spiral
that can be simplified into three stages relating to individuals, teams or groups, and the
organisation. Once individuals integrate their knowledge, a collective knowledge is
formed as group knowledge. When the group knowledge is combined with other group
knowledge, it will form organisational knowledge. Figure 2.6 illustrates the movement
of different collective levels of knowledge.
Figure 2.6: Different collective levels of knowledge (adapted from Tiwana (2001))
The argument in the KBT is that competitive advantages and key competences for an
organisation are obtained by a capability to integrate various sources of expertise
(Hustad 2007). Past research has found a positive relationship between KI and
performance (Robert et al. 2008), such as KI within teams and reduced software
defects (Tiwana 2004), KI and project teams‟ performance (Newell et al. 2004), and KI
for distributed networks (Hustad 2007). KI minimises the unintended spill-over of
knowledge, reduces cross-learning among employees and improves team or
organisational performance (Tiwana and McLean 2002).
Individuals
Teams/groups
Organisation
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2.5.1 Knowledge Integration Mechanisms
The integration of knowledge is considered to be a series of iterative activities in terms
of sharing, interpretation, transaction, transformation and negotiation of knowledge
(Hustad 2007). Referring to the KBT perspective by Grant (1996), the efficiency and
effectiveness of integrating the individual‟s knowledge base is affected by various
mechanisms: (1) rules and direction (communication, manual, directives, policies,
procedures); (2) sequencing; (3) routines that are not dependent upon the need for
communication, that is, developing sequential patterns of interaction that permit the
integration of the knowledge base without the need for communicating the knowledge;
and (4) group problem-solving and decision-making.
Rules are viewed as standards which regulate and facilitate the interactions between
individuals, while directions are a method of communication between specialists and
the large number of persons who are either non-specialists or who are specialists in
other fields (Grant 1996). Directives are defined as the specific set of rules,
procedures, heuristics and instructions developed through the articulation of
specialists‟ tacit knowledge for efficient application by non-specialists (Alavi and Tiwana
2002). Sequencing can be explained by organising activities in a time-patterned
sequence which can minimise communication by integrating individuals‟ specialist
knowledge.
Some knowledge is explicated and codified in the form of documents, procedures and
organisational routines (Alavi and Tiwana 2002). The knowledge that is embedded in
business routines and processes is quite tacit in nature (Teece 1998). However,
routines are often built over time, and consist of both tacit and explicit knowledge.
Knowledge can come in the form of documented, verbatim instructions (where it is
primarily explicit) or auxiliary learning as an individual goes through the process and
develops habits (where it is primarily tacit). Here, routines refer to the development of
a sequence of individual or organisational actions to execute the task so that the task
execution becomes reliable, easily reproducible and efficient (Galunic and Rodan 1998).
To simplify, Alavi and Tiwana (2002) refer to routines as organisational protocols,
Page | 45
process specifications and interaction norms through which individuals apply and
integrate what they know without having to communicate it explicitly, since it is
patterns of behaviours or grammars of action. These routines support complex
patterns of interactions between individuals in the absence of rules, directives, or even
significant verbal communication. It may involve group problem-solving that requires KI
and engages individuals with specialised knowledge in a collective problem-solving task
(Okhuysen and Eisenhardt 2002).
From the processes described above, the conclusion can be drawn that KI capacity is
determined by two crucial mechanisms, namely, direction and organisational routines
(Huang and Newell 2003). Direction enables communication between specialists by
codifying tacit knowledge into explicit rules, and organisational routines reduce the
need for communicating the explicit knowledge. In addition to these mechanisms,
Grant (1996) proposes that the effectiveness of KI is determined by three factors:
efficiency, flexibility and sufficient scope.
2.5.2 Factors of Knowledge Integration Effectiveness
Based on KBT (Grant 1996a), there are three distinctive constructs that facilitate the
KI pertinent to competitive advantage: efficiency, scope and flexibility. In general,
efficiency increases the access and utilisation of component knowledge areas. It is about
the extent to which there is the capability to access and utilise the specialist knowledge
held by individuals in an organisation. The scope of integration is the breadth of
combining component knowledge areas or specialised knowledge. The flexibility of
integration increases the utilisation of additional component knowledge (Krogh 2009),
which is about the extent to which an organisation can access additional knowledge and
reconfigure existing knowledge. In our context, we argue that better KI yields a better
ES performance, thus gaining a competitive advantage for the organisation. In this sense,
we propose that these three factors of effective KI are important for the ES success.
Page | 46
(a) Efficiency
According to Grant (1996), competitive advantage depends upon how productively
firms are utilising the skill or specialised knowledge stored within individual
organisational members. In this light, Grant claims that the efficiency of integration
between different specialists depends upon the existence of sufficient common
knowledge, good organisational structure and enough frequency of integration.
First, efficiency depends upon the extent to which the employees have established a
common base of knowledge (Hustad 2007). At its most simple, common knowledge
consists of those elements of knowledge common to all organisational members; it is
the intersection of their individual knowledge sets. Common knowledge or knowledge
redundancy refers to the common understanding of a subject area shared by
organisational members who engage in communication (Huang and Newell 2003). The
importance of common knowledge is that it permits individuals to share and integrate
aspects of knowledge which are not common between them. In the context of ES, the
prerequisite for communication between different ES players is the presence of
common knowledge between them. For example, to facilitate a discussion to solve
problems in using the ES between operational staff and technicians, it is crucial for the
staff to have some basic understanding about the software and the business process. If
the level of common knowledge is low, then the integration between them becomes
difficult.
Common knowledge is a key component of effective communication as it allows
integration to occur effectively and efficiently. Common understanding eases
interpretation of the information that is communicated among people. Normally,
knowledge becomes common through joint training and development, shared
experiences, direct observation, meetings and joint problem-solving among employees
(Alavi and Tiwana 2002). If the level of common knowledge is insufficient, individuals‟
ability to integrate knowledge is low (Huang and Newell 2003). To increase the level of
common knowledge, individuals must have some basic understanding to avoid barriers
among them and to effectively communicate. Since the knowledge common to various
Page | 47
ES players can be shared, knowledge deficiency or inconsistency in the ES environment
can be minimised (Newell et al. 2004).
Second, the efficiency of integration requires a good organisational structure. Proper
management structure can help organisations generate innovation and build knowledge
assets (Teece 1998). Here, knowledge assets can include ES knowledge. The structure
facilitates innovation through reconfiguration of distributed organisational knowledge.
Through the structure, diverse knowledge and expertise of individuals in various
locations in an organisation can be assembled, integrated and applied to the task at
hand (Alavi and Tiwana 2002). The organisations should clearly specify who should be
responsible for authorising access (Loh and Koh 2004) to the ES. For example, Peng
and Nunes (2009) discuss the importance of having a clear policy to outline knowledge
access rights in organisations according to departments or job functions. To be
effective, individuals in an organisation should know who has the required knowledge
and expertise, where the knowledge and expertise are located, and where they are
needed. This is to avoid the ES information being accessed and modified by irrelevant
users, which may result in information loss, errors and information leakage and lead to
business crisis. Furthermore, a good structure plays a significant role in organisational
learning and innovation by creating the conditions for frequent communication and
knowledge exchange among employees (Hustad 2007).
Third, efficient KI depends upon frequent communication among employees. A
sufficient level of frequency (coordination) is essential to ensure consistency in ES
performance. Integration or coordination occurs through repetition and continuous
practice. This includes frequent communication among individuals in meetings,
workshops, training and brainstorming sessions.
(b) Scope of Integration
The effectiveness of integration depends on the breadth of knowledge being integrated.
The scope of integration must be sufficient. The two important measures of scope here
are: complementary rather than substitute knowledge; and a greater scope. From the
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KBT perspective, KI effectiveness can be improved by having complementary
knowledge and a greater scope of integration. Both can enhance ES performance and
subsequently create competitive advantage for the organisation.
Different types of specialised knowledge are complementary rather than substitutes
(that is, the same knowledge). The integration of complementary knowledge is better
because this leads to meaningful integration: the ES knowledge will be enhanced, which
gives rise to a goodness (better level) of ES knowledge. Complementary knowledge
requires straightforward integration and is necessary for innovation to occur (Leonard
and Sensiper 1998). This situation is important for improved performance of the ES.
Greater scope creates a „causal ambiguity‟ which other competitors cannot replicate
(Grant 1996). A wide scope of KI refers to integration of highly diverse pieces of
knowledge where knowledge boundaries need to be crossed (e.g. divisions,
departments, branches) (Okhuysen and Eisenhardt 2002). If the scope is too wide, the
knowledge is too complicated to integrate due to the involvement of more people and
a larger environment (Hustad 2007). An excessively large range of expertise, skills and
experiences will lower the level of common knowledge of the system (e.g. technical
terms and language) and cause misunderstandings and conflicts. Moreover, there is no
doubt that the greater the scope of knowledge being integrated within a capability, the
greater the difficulty faced by ES players. Thus, some effort must be made to ensure all
ES players have a common understanding on the related ES subject matters and to
manage scope carefully.
(c) Flexibility of Integration
An integration of knowledge must be flexible. Two aspects are important here: 1)
flexibility to extend the existing capability; and 2) flexibility to reconfigure knowledge
into new capabilities. In the context of ES, when using an ES, all ES players can continue
their performance if there is flexibility of KI. The flexibility of KI can enable employees
to maintain the ES performance. It can lead to innovation to create better ES
performance (Newell et al. 2004).
Page | 49
Flexible integration becomes possible through transformation into new knowledge that
happens either by adding new knowledge or by reconfiguring existing knowledge
(Hustad 2007). The additional knowledge can be accessed from multiple knowledge
bases, and can be achieved by utilising knowledge sources. The knowledge might be
gained from past experiences (Pan et al. 2007). Based on the experience, individuals can
create innovations (Grant 1996) and make improvement in the context of either
architectural aspects (e.g. product, process, modules or problem-solving) or strategic
aspects (e.g. new approach of ES training). This can ensure an organisation is consistent
in having good performance in the context of its ES performance.
2.6 KNOWLEDGE INTEGRATION IN ENTERPRISE
SYSTEMS
KI is essential for the successful implementation of an ES (Srivardhana and Pawlowski
2007) due to the diversity of the Enterprise System‟s knowledge sources, stakeholders
and types of knowledge to be shared. The requirement to effectively integrate
knowledge continues after the ES becomes operational in the ES post-implementation
phase, where the adoption of the system may be difficult for employees due to the
complex system changes (Klaus and Blanton 2010). In general, to achieve effective KI,
Grant (1996) proposes efficiency, scope and flexibility as the important factors, as
discussed in the previous section. Utilising Grant‟s three factors of KI effectiveness, we
extend the understanding of the critical factors in the ES context by assuming that
effective KI is influenced by individuals and organisations, through passive and active
integration practices. We investigate factors in the complex ES context that best
represent the antecedents of KI effectiveness.
Since the 1990s, the knowledge perspective has been explained from both the
organisational (collective) and individual (Krogh 2009) perspective. For example, the
knowledge interaction between individuals and their organisation as collective
knowledge was researched in the prominent work of Nonaka (1994). Even the earlier
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studies of technology and improvement of work processes focused not only on
individual jobs, but also on employees in the broader context of the organisational
environment (Davenport 1993). Although knowledge is inherently rooted in individuals‟
expertise, organisational performance may also be hindered by employees‟ failure to
integrate all available knowledge as a collective (Robert et al. 2008).
From the viewpoint of knowledge of technology in the ES context, ES implementation
requires the inclusion of ES users from various levels, from individuals to units and
departments, within the organisation (Lin and Rohm 2009). The ES implementation
influences the structure and content of organisational knowledge at the individual and
organisational level (Srivardhana and Pawlowski 2007). For example, the ES
configuration options may affect the organisation by creating more complex tasks
requiring a broader set of business and software knowledge, and can increase the
requirement of employees‟ knowledge. Grant (1996) argues that the integration
process of specialised knowledge of individuals in organisations occurs through rules
and directives (facilitated by the passive organisational form of hierarchical levels),
sequencing of tasks and organisational routines (active socialisation capabilities and
frequency of integration), as well as group problem-solving and decision-making (active
interaction accessibility). Thus, it is understood that the integration of knowledge in the
ES context needs to cover and distinguish individuals and organisations by taking the
passive and active perspectives into consideration.
The focus of this research is not, however, on the implementation of ES, but on its
deployment and use. ES knowledge is grounded in the experience and expertise of
individuals. How the ES knowledge is configured and deployed will shape competitive
outcomes and the success of the ES.
2.6.1 Passive Integration in an Organisation
a) Organisational Structure as a Passive Element
An organisation‟s integration capability is influenced by organisational structure (Grant
1996). The structure of an organisation is typically characterised by hierarchical levels
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and the way activities are grouped and functions are separated, through which the
integration of knowledge can be facilitated by rules and directives (Kenney and
Gudergan 2006). An organisational structure guides stable productivity as the activity is
conducted in teamwork and cannot be done by individuals (Davenport 1993). The
structure builds social interaction into work processes under organisational control.
When an ES is implemented, such a structure specifies the key roles and responsibilities
of employees, and identifies who will participate in the newly implemented processes.
Clarity in task boundaries, decision-making authority and roles is among the most
important success factors for employees in carrying out their tasks as part of a team in
an organisation (Hendriks 2008; Klaus and Blanton 2010). Organisations can take full
advantage of their new ES functionality through effectiveness of their decision-making,
and can develop even more effective business processes (Adam and O‟Doherty 2003).
Grabski et al. (2003) concur that one of the risks associated with ES failure is a lack of
role clarity or role definition of individuals, which affects the system use.
By introducing new processes and structures, an ES results in structural changes, which
are reflected in new tasks and responsibilities (Zabjek et al. 2009). The jobs of ES users
become more complex, requiring a broader set of business knowledge as well as
software knowledge (Srivardhana and Pawloski 2007). Therefore, it is vital to have a
formal and clear description of all tasks and responsibilities driven by the new process
(Zabjek et al. 2009). Users need to learn the new ways of operating the ES effectively
and of cooperating in a network system (Yeh and OuYang 2010).
Many organisations have underestimated the need for alignment between the ES and
their organisational structure. Structure must clearly define who needs what
information, who is supposed to provide it, when it should be transmitted and by what
medium. Employees must have adequate authority based on their functions, transfer
authority and responsibility, and become involved as soon as possible as must the
company‟s management (particularly important if ES problems exist). It is important to
make use of employees‟ knowledge in areas where the other employees lack expertise
(Francoise et al. 2009). A new ES creates new organisational structure: the change of
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organisational structure will lead to new or changed jobs and the workforce will have
new responsibilities (Liu and Seddon 2009).
(b) Scope of Integration as a Passive Element
The scope of KI refers to the breadth of specialised knowledge the organisation
combines (Grant 1996; Kenney and Gudergan 2006). The scope can be defined by the
organisation. It needs to be managed carefully as the range of integration might affect
the integration effectiveness. The scope of integration of specialised knowledge may
need to cross different practices, which increases the requirements of communication
iterations (Hustad 2007). In a wider context, besides having a positive impact by
creating greater integration across functions, the social interactions of employees may
also lead to conflict and misunderstanding, for example, in cross-functional teams or
divisions (Davenport 1993). The conflict may significantly decrease the performance of
the organisation. Even though greater scope may lower the KI effectiveness, it increases
competitive advantage. If the scope is too low, it might lead to an ineffective or
inadequate integration (Huang and Newell 2003). If an organisation is faced with an
insufficient scope of KI, the KI will become ineffective, and the ES performance will be
affected as well as the organisation‟s competitive advantage (Newell et al. 2004; Pan et
al. 2007). Therefore, greater scope needs to be managed carefully. This requires ES
knowledge on business and software as well as organisational knowledge.
2.6.2 Active Integration in an Organisation
In a situation of high complexity like an ES, directives and routines that occur in passive
integration may not be enough to enable effective KI. Active integration from
individuals and the organisation may be more appropriate.
(a) Active in Creating Common Knowledge
Once the ES is implemented, staff in organisations must maintain their active
relationship through communication to redress the post-implementation dip as quickly
as possible (Adam and O‟Doherty 2003). Communication could be based on a
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discussion of the level of software performance and how to take it further to achieve
common understanding of the ES. According to Krogh (2009), individuals contribute
their knowledge to the organisation, and in return, the organisation contributes
common knowledge to individuals through group problem-solving and decision-making
as a result of active communication. The communication allows knowledge flows in
groups, teams, departments and divisions so that they can collectively plan, act and
solve problems of relevance to the organisation. Common knowledge includes the
software language of the ES, the symbols and terms used in the system, in-depth ES
specialised knowledge and shared system meaning (Patnayakuni et al. 2007).
Communication is needed to share important knowledge about either current
processes or future visions. Communication has been identified as an important factor
for ES success (Lin and Rohm 2009). Lack of shared ES understanding among
employees may contribute to difficulties in knowledge innovation and limit the ES
contributions (Peng and Nunes 2009; Santhanam et al. 2007).
Most teams today are digitally enabled – meaning that they use traditional face-to-face
communication, as well as a host of other media, including phone, video and digital
networks (Robert et al. 2008). Woo (2007) proposed communication as a critical
success factor for ES (Momoh et al. 2010; Al-Mashari et al. 2003; Nah et al. 2003).
Language must be easy for everyone to understand. A common understanding of
decisions and conflict resolution processes is important (Francoise et al. 2009).
Organisations realise that poor communication skills of the support staff make ES
implementation difficult (Maheshwari et al. 2010). A lack of shared understanding
between managers contributes to difficulties in an innovation implementation and limits
the ES contribution to organisational competitive advantage (Lin and Rohm 2009).
In study of Japanese manufacturing processes, job rotation was cited as a key factor in
skill enhancement (Davenport 1993). Since business processes are typically collections
of functions, employees should know about other functions and activities in order to be
able to effectively integrate knowledge across them. The collective knowledge extends
beyond individual knowledge as the re-use of the collective knowledge is critical to the
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organisation (Krogh 2009). Rotation through various jobs in related processes enables
process innovation in the organisation (Huang 1999). In ES implementation, knowledge
can be integrated through formal and informal practices (specifically job rotation)
(Patnayakuni et al. 2007).
(b) Active in Providing a Frequency Integration Platform
In the post-implementation phase, the ES team is disbanded and the commitment of
staff is tested to the utmost. Staff must return to their usual tasks with active hands-on
roles and support each other to achieve the organisation‟s targets from the ES (Adam
and O‟Doherty 2003). Active integration of knowledge supports a variety of activities
to facilitate group work such as group brainstorming, communication, meetings and
frequent participation in training (Davenport 1993). According to Davenport,
employees are expected to participate in teams, for instance, by learning about their
jobs and the jobs of other team members through training to acquire skills in applying
the system. Learning is influenced by the organisation as knowledgeable individuals can
circulate their knowledge to other individuals in the organisation (Krogh 2009).
The frequency capability facilitates integration of knowledge through learning in
organisations such as training and meetings (Krogh 2009). Training has been widely
recognised as an important factor for successful ES (Lin and Rohm 2009). During the ES
post-implementation phase, an organisation‟s efforts in areas such as training can be
crucial (Srivardhana and Pawlowski 2007) to avoid failures in achieving intended ES
benefits (Newell et al. 2004). When new processes are introduced in ES
implementation, training programs must be undertaken, in which employees who will
execute the process must be trained to appropriately operate the ES. This type of
training, such as specific process training or on-the-job training, should be carefully
designed by management. Management staff derive motivation to develop new skills in
employees by providing training and coaching in teamwork for their employees.
Training users to use the ES is important because an ES is not easy to use even for
highly educated managers with good IT skills. In some ES implementation experiences,
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many employees were not trained to use the ES and many were unfamiliar with
computers (Koh et al. 2006). Among the ES challenges are: 1) lack of adequate training
at all levels for both management and employees to ensure that there is a good
understanding of the impact of implementing the ES as well as what it is and is not; and
2) inadequate understanding of business requirements and the implications of the ES
before implementation (Momoh et al. 2010). Organisations must be aware of the
importance of training. It can help staff to understand the issues and to become more
comfortable in working and dealing with system changes (Liu and Seddon 2009). The
more frequently training is conducted, the higher the level of common understanding
to be developed among employees, and the more effective the KI that is likely to be
gained.
Management might encourage a positive working environment by connecting experts
through formal events such as meetings, workshops and social interactions. In
situations where urgent attention is needed from experts, managers may initiate the
establishment of ad-hoc meetings, training sessions or workshops (Hustad 2007).
Meetings and training sessions, for instance, are conducted to integrate specialised
knowledge of the system with participant employees to deal with complex ES
problems, where employees learn through discussions with experts. The direct
involvement of management in initiating the frequency of integration through meetings
and trainings, for example, is one of the reasons why we place frequency of integration
in the AIO construct.
2.6.3 Active Integration by the Individual
ES implementation always entails new roles and responsibilities for an organisation. This
requires more knowledge of other functional areas (Lee and Lee 2003). Lee and Lee
(2003) argue that in the ES post-implementation stage, individuals in an organisation
actively begin seeking knowledge sources to understand the requirements of a broader
scope. The complexity of an ES provides new opportunities to acquire knowledge from
external sources and among employees from different functional areas, and to
implement new improvements that can significantly and innovatively increase the level
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of employees‟ knowledge. Innovation often involves the requisite new skills that may
entail both greater depth of job knowledge and greater breadth of task expertise
(Davenport 1993). The diversity of ES knowledge and expertise in an organisation
increases the possibility that employees will be able to link new knowledge to their
existing knowledge and strengthen their ES knowledge innovation (Srivardhana and
Pawlowski 2007).
At the individual level, knowledge allows the capacity to reflect, decide, think and
formulate solutions in the workplace (Krogh 2009). Grant (1996) argues that
innovation is created by the existence of flexibility in extending knowledge and
reconfiguring existing knowledge. Creating new knowledge by re-arranging information
already in use and incorporating previous information, and actively seeking knowledge
to learn new practices and technology can generate creativity and improvement
(Srivardhana and Pawlowski 2007). The requirement to actively integrate knowledge
for individuals is caused by the complexity of the ES, which expands the area of
knowledge search and the ability to interpret knowledge at the individual level.
Open communication can facilitate information sharing and promote a common
understanding and innovative behaviour in the organisation. Sufficient communication
with employees can help them to recognise the impact of the ES and encourage them
to provide timely feedback from different perspectives about the effectiveness of the ES
and processes (Liu and Seddon 2009).
2.6.4 Restructuring the Antecedents of KI Effectiveness
Addressing the distinction of both individual and organisational aspects with an
emphasis on the active and passive viewpoints provides greater clarity about the
antecedents that facilitate KI effectiveness. Table 2.2 shows how we restructure the
components of the KI effectiveness influence factors of Grant (1996) by taking into
account these perspectives. The details are discussed in Chapter 3.
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Table 2.2: Restructuring the antecedents of KI effectiveness
Grant’s theory The research model
Efficiency
Common knowledge
Frequency of integration
Organisational structure
Passive Integration of an Organisation
(PIO)
Organisational structure
Complementary knowledge
Greater integration
Scope
Complementary knowledge
Greater integration
Active Integration of an Organisation
(AIO)
Common knowledge
Frequency of integration
Flexibility
Reconfigure knowledge
Extend knowledge
Active Integration of the Individual (AII)
Flexible to reconfigure knowledge
Free to extend knowledge
2.7 ES-KNOWLEDGE BASE
KM has been presented as a fundamental strategic initiative and the most important
guarantor of sustainable competitive advantage for organisations (Easterby-Smith and
Prieto 2008; Grant 1996a). KM has a strong link to learning processes. Learning can be
defined in terms of the processes of knowledge creation, retention, transfer and
application. Thus, Easterby-Smith and Prieto (2008) consider KM to be “managed
learning”.
In order to demonstrate the use of the knowledge base concept, we analyse prior
studies. The studies were selected employing a keyword search of “knowledge base” in
popular academic literature databases such as ProQuest and ScienceDirect. The studies
were examined using the following criteria: (1) physical knowledge base, (2) conceptual
knowledge base, (3) combination (physical and conceptual), and (5) knowledge base for
competitive advantage.
The summary presented in Table 2.3 shows that the knowledge base can be viewed
from the physical and conceptual aspects or a combination of both the physical and
conceptual. In the physical view, the term „knowledge base‟ refers to the technical
resources that exist through the development of a formal organisational system, tool
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and repository for its identification, definition and evaluation where the knowledge
base is continually updated (Kumar and Thondikulam 2005; Vertommen et al. 2008).
Having analysed the knowledge base literature, we recognise that the knowledge base
is embedded in people (consultants and vendors), client organisation, technology,
expertise, social and other secondary sources.
Table 2.3: Summary of knowledge base discussion
Physical knowledge
base
Conceptual knowledge
base
Combination
(physical and
conceptual)
Knowledge base for
competitive
advantage
Knowledge base from
physical perspectives
(repositories, tools) (Markus
2001)
Interactions between individuals with
different knowledge bases
(heterogeneous and complementary
skills) will increase organisations’
capacity to achieve innovation
beyond that which any individual
member can achieve (Zarraga-
Oberty and Saa-Perez 2006).
Knowledge base is found
in the human and cultural
aspects of businesses
(experiences, tacit
knowledge of employees),
integration of 'hard'-
technology, 'soft'-
organisation and human
and 'abstract'-
philosophical (Hlupic et
al. 2002).
Creation of new value using
existing knowledge base of
the firm is significant source
of innovation and competitive
advantage in industries
(Nielsen 2006).
Physical knowledge base-
digital library, system
(Bieber et al. 2002).
Knowledge base applications (active
learning activities) are becoming the
key success for many businesses
(Martz and Shepherd 2003).
Individuals should be
encouraged to make their
knowledge explicit, and
store in knowledge base
for later re-use (Rech et
al. 2007).
Knowledge base has been
created and deployed to gain
and sustain a significant
competitive edge
(Stonehouse and Minocha
2008).
Structuring an expert's
knowledge leads to the
ability to store their expertise
in a computer knowledge
base (Herschel and
Yermish 2008).
In order for individual A to
understand individual B’s knowledge,
there must be some overlapping in
their knowledge bases (a shared
knowledge space) (Alavi and
Leidner 2001).
Individuals may utilise
knowledge base when
needed, and they are
required to have a
substantial knowledge
base to complete task
(create web server, e-mail
server, setting up windows
etc.) (Chilton and
Bloodgood 2008).
The role of knowledge base
process has been central:
dynamic capabilities evolve
through pathways that can be
described in terms of
knowledge evolution within
organisations (Prieto and
Easterby-Smith 2006).
Knowledge base as system
(Gray and Durcikova
2005).
Key users, IS personnel and vendors
have different knowledge bases that
are difficult to transfer (different
backgrounds and interests) (Ko et
al. 2005).
Knowledge base provides
valuable support to deal
with tricky repair problem
(knowledge workers/
network technicians)
(Buchel and Raub
2002).
An organisation can nurture,
adapt, and generate its
knowledge base and develop
and retain the organisation’s
capabilities that translate the
knowledge base into useful
actions (Marsh and Stock
2006).
All past incidents created
knowledge base in incident-
tracking support system to
aid the solutions of similar
problems in future (Barrett
et al. 2004).
Worker's knowledge base increases
by continuous learning processes and
the breadth and depth of expertise
(Paul 2006).
An organisation's
knowledge base changes
when knowledge workers
leave the organisation;
computational knowledge
base should be updated
(Chen and Edgington
2005).
Organisations can continually
reconfigure their knowledge
base by spotting trends in
their external environment
and internalising the
knowledge, so competitive
advantage can be obtained
(Liu 2006).
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E-mail as part of a
knowledge base (Griffith et
al. 2003).
Sender and receiver form
expectations of the value of
knowledge base on their information
(Lin et al. 2005).
Each knowledge base
may learn more or less
than the other. Firms may
learn and internalise
foreign knowledge base to
their own local knowledge
base (technology,
practices, skills,
information, know-how)
(Assudani 2005).
To develop and maintain
knowledge base, competitive
advantage should be
preferably rooted on implicit,
collective, firm-specific
knowledge (Andreau et al.
2008).
Database (used to capture
and store complaints,
customer details, solutions) is
a treasure of knowledge
base (Koh et al. 2005).
Research can be defined as a
knowledge-based activity, involving
the researcher in KM process (Land
et al. 2007).
KM is one of the most
important aspects of
knowledge-based society,
where most of the
processes that add value
to end products are
derived from knowledge
base services activities
(Szczerbicki 2008).
The development of
knowledge base and
knowledge systems is largely
a technical process
(Sparrow 2001).
To derive knowledge-based
competitive benefits, a firm needs to
integrate, combine the specialised
knowledge of its employees. IS
integrates skills and expertise,
allowing firms to develop knowledge
base (Zhang 2007).
Knowledge base to
support decision-making
in KM system (Massa
and Testa 2008).
Organisation's knowledge base
allows individuals to learn,
experiment, communicate with
each other (Blosch 2001).
Small-Medium
Enterprises create new
knowledge into their
knowledge base which is
further enhanced by
practical experience
(Moffett and McAdam
2006).
Intellectual resources as a
knowledge base (Alavi et al.
2005).
Knowledge base helps
service representatives
solve particularly difficult
problems, where solutions
are input into it (Huber
2001).
Individuals as part of
organisation's knowledge base
(Nidumolu et al. 2001).
People share the
outcome, collaborative
works promoting
contributions to
knowledge bases can be
searched using KM
System (Kulkarni et al.
2006).
Knowledge can be
accumulated in structured
knowledge base that can
be screened and used as
a source for competence
creation (Maula 2000).
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A knowledge base means different things when it is viewed from different perspectives.
The various definitions of knowledge base, the range of contributors to the ES-
knowledge base, and the important goal of the knowledge base when engaging with
external parties, need sound concepts. As different knowledge base concepts are used
interchangeably throughout the literature, we need to define our knowledge base
according to our research perspective. For our purposes, the definition of ES-
knowledge base needs to consider the involvement of the Enterprise System lifecycle,
the sources of knowledge by Gable et al. (1998), and the knowledge types identified by
Davenport (1998) and Sedera et al. (2003). ES knowledge (referred to as ES-knowledge
base) consists of business process knowledge, organisational knowledge and software
knowledge (Davenport 1998). These types of knowledge are contributed by the ES key
players – consultant, vendor and client organisation. The consultant and ES vendor will
create the ES-knowledge base through interaction with the client organisation, and the
client organisation also shares the organisation‟s business process with them.
Taking these points into consideration, we define knowledge base as “a combination of
knowledge of software and business process that is brought to bear by consultant,
vendor and client in the organisation through integrating knowledge”. We suggest that
the knowledge base is created and used within the process of KM, and is embedded in
practices and experiences, training and education involving internal and external
stakeholders. Herein, the term „ES-knowledge base‟ does not necessarily refer to a
physical knowledge base (such as that of a knowledge database), but instead refers to
the conceptual aspect of a collection of all necessary knowledge.
In the ES context, prior research suggests that an ES involves three types of knowledge,
namely, software knowledge (product knowledge), business process knowledge and
organisational knowledge (Davenport 1998). Each of the type of knowledge is discussed
in relation to the ES context below.
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2.7.1 Software Knowledge
Software knowledge refers to knowledge about the product (the ES), which includes
the knowledge on how to use it. Software knowledge represents the selection and use
of technical knowledge to analyse (e.g., capture requirements), design (e.g., decide on
the design pattern and identify best practices), implement (e.g., programme) and
maintain (e.g., troubleshoot) the ES software. It reflects the need for knowledge specific
to one ES solution. The ES is usually a comprehensive package such as a Systems
Application and Products (SAP) solution. Understanding the ES package requires a
product-specific knowledge.
2.7.2 Business Process Knowledge
The business process knowledge is related to the organisation‟s business processes and
operational procedures. It covers the business issues before the actual implementation
of the ES, such as issues related to functional knowledge (e.g., purchasing and
accounting), educational knowledge (e.g., training) and knowledge about enterprise
culture (e.g., computer literature).
2.7.3 Organisational Knowledge
The organisational knowledge includes business process management and
communication policies, and organisational procedures and structures. Knowledge of
the organisation is important in creating and identifying the user profile (staff profiles),
staff roles and their employment cohorts. Precisely understanding the end-user
characteristics is a critical success factor for an ES project. This is because the ES
software is selected, implemented, used and changed in a specific company with
individual characteristics and an individual organisational population. This type of
knowledge is also related to specific business and technical knowledge.
2.7.4 Types of ES Knowledge and Employment Cohorts
Each employee brings a different level of ES-knowledge base, as the types of ES
knowledge in terms of software, business processes and organisational requirements
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vary for each level of employment. As introduced in Chapter 1 (Section 1.1.2), there
are three levels of employment cohorts in an organisation at the strategic, management
and operational levels (Anthony 1965). The strategic level involves complex, irregular
decision-making and focuses on providing policies to govern the organisation. However,
knowledge that is required by the management level is different from the knowledge
needed by the strategic level. The management level focuses on ensuring that the
organisation‟s resources are used effectively and efficiently to accomplish the goals
identified by the strategic level (Sedera 2007). In contrast, the operational level is
involved in highly structured and specific tasks that are routine and transactional.
Organisational knowledge is essential to the strategic employees, and is less significant
for the management and operational employees. At the operational level, software
knowledge is crucial. In contrast, business process knowledge is important for
employees in management groups. It is a necessity for management staff to have a deep
knowledge of business processes to achieve greater efficiency and better quality of ES
usage (Sedera 2007). Therefore, if the current business practices and procedures need
to change, management staff can review and make innovations to the processes,
services or business functions which fit the ES. ES knowledge requirements vary for
each level of employment. Although all staff are connected to the knowledge sources of
ES, not all levels of staff need to know all the ES knowledge. For example,
organisational knowledge of the organisation is required for ES end-users, and
employees need to know how their tasks fit into the overall process and how the
process contributes to the achievement of the organisation‟s goals (Vandaie 2008),
including strategic planning, management control and operational control. More
importantly, they need to be able to recognise and obtain valuable ES knowledge from
other employee groups and subsequently integrate that knowledge with their existing
ES-knowledge base.
Following Wyssusek (2005), this study takes the approach that the knowledge base in
the ES perspective requires a suite of concepts and theories. The knowledge-based
theory of the firm confirms this approach. In its theoretical viewpoint, knowledge base
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resources are hard to imitate: the knowledge base is a unique characteristic that
provides an organisation with capabilities, competitive advantage and performance
(Grant 1996a; Grant 1996b). A diverse range of contributors to the ES-knowledge base
can be identified in the ES knowledge types by Davenport (1998), knowledge
integration by Grant (1996) and knowledge creation by Nonaka (1994).
It is argued herein that the ES-knowledge base is created in a process where the
knowledge is developed by the key stakeholders, retained by the organisation and its
employees, transferred to where the knowledge is required through learning
interactions and is applied throughout the ES lifecycle. Key sources of ES knowledge
are the stakeholders who make significant contributions to the formation of the ES-
knowledge base. These include: (1) the client organisation; (2) the ES software vendor;
and (3) the consultant or implementation partner (Gable et al. 1998; Soh et al. 2000).
The ES software vendor is an important business partner who will customise, clarify,
install and support an ES software system. On the other hand, the consultant will deal
with the ES implementation process. To ensure the organisation is getting the right
system, the vendor and the consultant work closely with each other and the client
organisation. During implementation, consultants and vendors bring together their
prior work experience, work values, norms, philosophies and problem-solving
approaches (Ko et al. 2005). The combined collection of all the knowledge, both tacit
and explicit, within the individuals, systems or physical entities creates the knowledge
base.
2.7.5 Significance of the Knowledge Base
Establishment and maintenance of an ES-knowledge base are important goals. The
significance of the knowledge base is evident from the above discussion of knowledge
sources in KM literature. When engaging external parties, organisations typically have
goals that go beyond the successful implementation of the new system; they also have
the less tangible goal of acquiring knowledge pertaining to implementation, operation,
maintenance and training. Views in the literature on the significance of the knowledge
base are summarised in Table 2.4.
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Table 2.4: Significance of the knowledge base
No. Source Significance of knowledge base
1 Gold et al. (2001) Knowledge-based capability is the key for organisational success.
2 Herschel and Yermish (2008) Structuring an expert's knowledge leads to the ability to store their
expertise in a computer knowledge base.
3 Paul (2006) Worker's knowledge base increases by continuous learning processes
and the breadth and depth of expertise.
4 Buchel and Raub (2002) Knowledge base provides valuable support to deal with tricky repair
problems (knowledge workers/ network technicians).
5 Barrett et al. (2004) All past incidents create a knowledge base in incident-tracking support
system to aid the solutions of similar problems in future.
6 Chen and Edgington (2005) An organisation's knowledge base changes when a knowledge worker
leaves the organisation. Therefore computational knowledge base
should be updated.
7 Zhang (2007) To derive knowledge-based competitive benefits, firm needs to
integrate, combine the specialised knowledge of its employees.
8 Chilton and Bloodgood (2008) Individuals may utilise knowledge base when needed, and each
individual is required to have a substantial knowledge base to complete
their task.
9 Assudani (2005) Knowledge from feedback becomes new knowledge. Firms may learn
and internalise foreign knowledge base to their own local knowledge
base (technology, practices, skills, information, know-how).
10 Koh et al. (2005) Database (used to capture and store complaints, customer details,
solutions) is a treasure of knowledge
11 Nielsen (2006) Creation of new value using existing knowledge base of the firm is
significant source of innovation and competitive advantage in industries.
12 Faucher et al. (2008) New data can resonate with the knowledge base and lead to the
creation of new wisdom.
13 Marsh and Stock (2006) An organisation can nurture, adapt, and generate its knowledge base
and develop and retain the organisation’s capabilities that translate the
knowledge base into useful actions.
14 Blosch (2001) Organisation's knowledge base allows individuals to learn, experiment
and communicate with each other.
15 Huysman and Wit (2004) The stored knowledge base is used to support client interaction,
socialisation and training of the call centre operators.
16 Liu (2006) Organisations can continually reconfigure their knowledge base by
spotting trends in their external environment and internalising the
knowledge, so competitive advantage can be obtained.
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17 Andreau et al. (2008) To develop and maintain competitive advantage, knowledge base
should be rooted in implicit, collective, firm-specific knowledge.
18 Stonehouse and Minocha (2008) Knowledge base has been created and deployed to gain and sustain a
significant competitive edge.
19 Moffett and McAdam (2006) Small medium enterprises create new knowledge into their knowledge
base which is further enhanced by practical experience.
20 Martz et al. (2003) Knowledge base applications are becoming the key to success for many
businesses; changes in implicit knowledge base upon an active learning
activity can be identified and measured.
21 Zarraga-Oberty and Saa-Perez
(2006)
Interactions between individuals with different knowledge bases
(heterogeneous and complementary skills) will increase an
organisation's capacity to achieve innovation beyond that which any
individual member can achieve.
22 Huber (2001) Knowledge base helps service representatives solve particularly difficult
problems, as solutions are input into it.
23 Prieto and Easterby-Smith (2006) The role of knowledge base process has been central: dynamic
capabilities evolve through pathways that can be described in terms of
knowledge evolution within organisations.
24 Huggins (2008) Knowledge base of an economy can be defined as the capacity and
capability to create and innovate new ideas, thoughts, processes, and
products, and to translate these into economic value and wealth.
As shown in the literature, knowledge base is clearly a significant factor for
organisations‟ competitive advantage. Applying this premise to the study context of ES,
it can be argued that knowledge base leads to ES success. Individuals have their own
knowledge base which refers to their expertise or specialised knowledge. Therefore,
the more an individual‟s specialised system knowledge is integrated, the higher the
system‟s capabilities to achieve better performance, which leads to the success of the
system. In understanding the relationship between KM and ES success, the KBT can be
considered the appropriate theory to draw links on the impact of the KI and the
success of the ES.
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2.8 KNOWLEDGE INTEGRATION AND ES-KNOWLEDGE
BASE
The creation of the ES-knowledge base reflects the interaction of the employees‟
knowledge base. The knowledge of the system continuously produces and reproduces
as the learning process occurs through the interactions. Individuals can learn from each
other, articulate and test their knowledge, and clarify and refine their understanding of
concepts through discussion with peers and the development of skills. When individuals
participate in group work, they usually interact, distribute and discuss the work. They
gain knowledge and competencies through such processes (Fernandez-Breis et al.
2009). Internal stakeholders, who are involved in the ES, use the ES differently to suit
their own purposes and interests. This learning activity develops incrementally while
tasks are being executed. The group may consist of members with different views, such
as superiors and workers, who may learn together about the planning of their work
(Poell and Krogt 2003). Members of a learning group must have a desire to learn
together. The team learns about topics that are relevant to their work to improve their
performance as members of the organisation.
When knowledge of the ES or the business process is integrated, the individual‟s ES-
knowledge base then changes. For example, managers may engage in discussions with
technical staff on the consequences of a software problem as they try to develop a
solution and fix the ES. This interaction enables them to learn and to obtain knowledge
about the system bugs and knowledge about misalignment between the system design
and actual practice (Santhanam et al. 2007). With this knowledge, managers can better
align the ES solution with the organisation‟s business processes. The knowledge that is
learned through active interactions facilitates the combination or integration of diverse
knowledge, skills and experiences from different staff or experts. When this occurs,
previously created ES-related knowledge becomes the input in a new round of ES-
knowledge base creation. Individuals then apply their new ES-knowledge base, and the
evolutionary process continues. Figure 2.7 shows the ES-knowledge base development.
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Figure 2.7: ES-knowledge base development
2.9 ENTERPRISE SYSTEM SUCCESS
An ES includes a set of software modules linked to a common database. These modules
can handle basic organisational functions such as manufacturing, finance, resources,
sales and management (Xuea et al. 2005). The high ES failure rate motivates this
research to identify what factors affect the ES success. An ES is embedded in complex
social contexts that influence the ES implementation and use. The nature of the ES is
complex because it involves multiple stakeholders, including ES vendors, consultants
and the client organisations (Sedera and Gable 2010). The multiple stakeholders, within
and outside the organisation, possess diverse portfolios of requisite know-how, skills
and abilities and individuals must integrate these portfolios to develop a timely and
workable solution (Tiwana 2003). Individuals in organisations bring together a wide
variety of know-how, skills and abilities relating to the ES. These stores of individual
knowledge may not be adequate to make the ES successful unless they are integrated
and applied to the usage of the ES and its problem solutions. Therefore, the ES needs
the integration of specialised knowledge in order for the system to be successfully
implemented in an organisation. When distributed ES knowledge is effectively
integrated, then organisations can have a better performing and well-coordinated ES.
Previous research has observed the importance of KI for ES (Pan et al. 2007; Huang
and Newell 2003; Krone et al. 2009). KI is essential to the successful implementation of
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an ES and is also one of the most difficult ES challenges (Srivardhana and Pawloski
2007). The need for increased social integration across an organisation continues after
the ES becomes operational. Since knowledge is inherently rooted in individual
members‟ expertise and experience, an organisation‟s performance may not be
hindered by its individual employees‟ lack of ability but instead by their failure to
integrate all available knowledge (Robert et al. 2008).
Effective KI is important in every phase of the ES lifecycle, particularly in maintenance
and upgrades (Markus et al. 2003), and is a frequent organisational concern that
appears to be closely related to ES success (Ross et al. 2003; Sumner 2003). Shared
experience allows people to understand what other group members do, what their
intentions are, and what help they need to solve tasks and problems (Erden et al.
2008).
To measure the ES success, this research employs the success measures of the IS-
impact measurement model (Gable et al. 2008). The four quadrants of that model are
derived from the most widely cited IS success model by DeLone and McLean (1992)
which consisted of six constructs: quality measures of system and information,
performance-related outcomes of individual and organisational impacts, and attitudinal
outcomes of use and satisfaction. For a range of reasons, use and satisfaction
constructs are not included in the Gable et al. (2008) success model. Gable et al. argue
that the use construct is inappropriate to measure IS success as it is considered to be
an antecedent to IS impact. They also believe that the satisfaction construct is an
immediate consequence of IS impact. Furthermore, early studies of IS success, such as
the work of Rai et al. (2002), report that the satisfaction construct is readily measured
indirectly through other constructs such as information quality and system quality.
In addition, the existing models developed for measuring IS success in a traditional IS
context do not properly measure the ES success (Gable et al. 2003) due to the
complex nature of an ES (Ifinedo 2006) and its specific characteristics (Zach 2010).
Taking into account the above factors, Gable et al. (2008) proposed the IS-impact
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measurement model as a set of overarching dimensions to evaluate IS success, as well
as to address the issue of inappropriate measurement of ES success. The proposed
model is the first attempt that successfully develops a specific success measurement
model for the ES context (Gable et al. 2008; Zach 2010). Furthermore, the model is
found to be the most comprehensive tool for IS measurement that captures the
complex nature of the ES (Petter et al. 2008).
The IS-impact model adopts four constructs from DeLone and McLean (1992) and
employs them in two categories: impacts (individual impact and organisational impact)
and quality (system quality and information quality). The four dimensions avoid
overlapping between constructs and measures, and have strong construct validity (Zach
2010). The model of IS-impact is depicted in Figure 2.8.
Figure 2.8: IS-impact measurement model
Gable et al. (2008) propose individual impact (II) as individual capabilities and
effectiveness that are influenced by IS application. This construct accommodates
diverse individual impact measurements of system usage to all employment cohorts,
applications, capabilities and functionalities of the ES. Organisational impact (OI) refers
to benefits received by the IS application at the organisational level, focusing on
variables related to organisational results and capabilities. Their validated instruments
for organisational impacts include items of cost reduction, productivity improvements
and business process change. The system quality (SQ) construct represents the quality
of the IS itself, and is designed to capture how the system performs from technical and
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design perspectives. This construct is measured by items such as ease of use, ease of
learning and alignment with user requirements. In contrast with the system quality, the
construct of information quality (IQ) is concerned with the system‟s output quality and
refers to the information produced in reports and on-screen (DeLone and McLean
1992; Gable et al. 2008; Gorla et al. 2010). Table 2.5 sets out the measures offered by
the IS-impact model for the validity of ES success. There are 27 measures left, which
appropriately assess the ES success and avoid overlapping measures as in the IS success
model by DeLone and McLean (1992) as shown in the table below.
Table 2.5: IS-impact measures
Constructs Measures
System Quality Ease of use
Ease of learning
User requirements
System features
System accuracy
Flexibility
Sophistication
Integration
Customisation
Information Quality Content accuracy
Availability
Usability
Understandability
Format
Conciseness
Individual Impact Learning
Awareness/recall
Decision effectiveness
Individual productivity
Organisational Impact Organisational cost
Staff requirements
Cost reduction
Overall productivity
Improved outcomes/outputs
Increased capacity
Business process change
E-government
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2.10 SUMMARY
This study is rooted in the knowledge-based theory of the firm. This literature review
began its discussion with an overview of the research and literature review strategy. It
was then followed by a discussion of the issues related to our research context,
namely, ES operation in the post-implementation phase. A detailed explanation of
knowledge, knowledge management and knowledge integration was provided next. The
chapter then introduced KI through the theoretical lens of KBT, followed by a
discussion of KI effectiveness, its antecedents, and the KI relationships with ES-
knowledge base and ES success. While the KI of KBT has been widely cited in the IS
literature as shown in this chapter, it was discovered that the antecedents of KI
effectiveness have not been quantitatively tested in their entirety (Caya 2008). Further,
to the best of our knowledge, the impact of KI effectiveness on ES success has also not
been tested in a complete nomological net. The literature indicates that although the
KBT is prominent, the theory still needs further investigation in many respects. The
next chapter discusses the research model and the development of our hypotheses.
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CHAPTER 3:
RESEARCH MODEL
AND HYPOTHESES
3.1 INTRODUCTION
This chapter introduces the research model and the research hypotheses. The research
model is developed in recognition of the importance of Enterprise System (ES)
operations once such a system is implemented in an organisation. As noted by Chen
(2009), the ES value is created only when the system is used properly. This research
model has two main parts: the antecedents and the consequences of knowledge
integration (KI) effectiveness. The knowledge-based theory of the firm (KBT) provides
a theoretical base for KI effectiveness and its antecedents, while the understanding of
the consequences lies on ES knowledge types (Davenport 1998) for the goodness of
ES-knowledge base and on the IS-impact measurement model (Gable et al. 2008) for
the ES success examination.
The chapter is structured in the following manner. First, we discuss the development of
the research model. Then, we explain our research model in regard to the antecedents
of KI effectiveness. Subsequently, we clarify our proposal regarding the consequences
of KI effectiveness, which leads to the definition of ES success as our final proposition.
We then discuss the hypotheses development and conclude with a summary.
3.2 RESEARCH MODEL
An ES is related directly to the people in the organisation who have diverse types of ES
knowledge, expertise and skills. This complexity warrants the investigation of KI to
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determine the success of the ES performance. The ability to successfully integrate
various sources of ES knowledge among ES users is what we refer to as KI effectiveness
and is the main focus of our research. This effective integration influences the ES users
to operate the ES successfully. The KBT of Grant (1996) provides a theoretical base for
understanding KI and factors that facilitate its effectiveness. In agreement with the KBT,
this research model builds on Grant‟s identification of three different factors in
achieving effective KI in terms of efficiency, scope and flexibility. As discussed in
Chapter 2, efficiency refers to the efficiency of organisations in accessing and utilising
existing employees‟ specialised knowledge. The efficiency can be improved from
common knowledge, organisational structure and the frequency of activities. The scope
can be identified from the types of specialised knowledge being integrated:
complementary rather than substitute knowledge and greater (wider) scope of
integration. The last factor, flexibility, relates to the extent to which innovation and
new capabilities can be developed by employees.
The early part of this research model is developed by restructuring Grant‟s three
factors of efficiency, scope and flexibility to better fit the ES context as antecedents of
KI effectiveness. Our research model looks at two main perspectives in identifying the
antecedents of KI effectiveness: individual and organisational knowledge, and passive
and active KI practices. The two main perspectives are then re-grouped into three
constructs (passive integration of an organisation; active integration of an organisation
and active integration of individuals) that we represent as the antecedents for KI
effectiveness. The advantage of grouping the KI antecedents by the three factors is that
it is easier to understand the issues associated with integrating knowledge for ES by
categorising them into the passive and active practices of individuals and organisational
perspectives. We believe that this is a useful approach as it reflects the reality of
knowledge in the ES context. Individuals and organisations can also gain benefits from
the classification as it places a different focus on specific factors in a constructive way.
We believe that by having our research model as a framework, management of
organisations could review their actions and consider different initiatives or approaches
to KI practices among ES users on the basis of the specific aspects identified in this
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model, and use them to provide innovative solutions for KI effectiveness in order to
have better ES success.
The second part of our research model is built by looking at the consequences of KI
effectiveness. Here, we separate the consequences into two sequential aspects: the
goodness of individuals‟ ES-knowledge base, and the ES success. We consider the
goodness of ES-knowledge base to be a result of KI effectiveness by looking at the
types of ES knowledge (Davenport 1998) from the individual perspective. Accordingly,
the ES success is then measured by employing the IS-impact measurement model from
Gable et al. (2008). Figure 3.1 summarises our research model in brief.
Figure 3.1: Brief outline of the research model
3.3 SIGNIFICANCE OF RESTRUCTURING THE
ANTECEDENTS OF KI EFFECTIVENESS
As mentioned above, the research model is developed by re-structuring the factors
that facilitate KI effectiveness as originally developed by Grant (1996) into two main
aspects: knowledge of individuals and organisations, and KI practices in their passive
and active aspects.
3.3.1 Individual and Organisational Perspectives
First, we identify the integration of ES knowledge based on individual and organisational
aspects. The ES will perform best by incorporating individual and organisational
knowledge. An ES entails a wide range of knowledge ranging from the software to the
business processes and the knowledge of the organisation. However, a single individual
KI
effectiveness
Antecedents
Consequences
Part 1
Part 2
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cannot have the required breadth and depth of knowledge as the individual is restricted
by learning capacities (Berends et al. 2008). In particular, no individual can possess all
knowledge (Munkvold 2008) as individuals have to specialise in a certain aspect to
develop the level of expertise required depending on their employment cohort. For
example, software knowledge is highly significant for operational workers, less
important for management staff and a very low requirement for strategic staff (Sedera
2007; Sedera and Gable 2010; Sedera et al. 2007). This differentiated knowledge
creates the need for KI of different specialised knowledge to enable an organisation to
acquire both the required breadth and depth of knowledge.
In addition, understanding the process of KI involves understanding the interactions of
individuals as well as groups as a collective to make sense of both organisational
processes and the ES (Pan et al. 2007). It is important to point out that collective ES
knowledge in an organisation must consider the aspect of the individual level of ES
knowledge, as the collective knowledge must be established at the individual level.
Although knowledge is owned at the individual level, the integration of knowledge at a
collective level is also necessary (Okhuysen and Eisenhardt 2002). This not only
requires individual and collective knowledge, but also requires individuals‟ efforts to
form collective knowledge in the organisation. Individual efforts must exist for the
development of personal capabilities which then contribute to form collective norms,
motivation and skills for the organisation. This means that the ES not only needs
employees‟ ES knowledge as an individual, but also requires the ES knowledge as a
collective contribution from its employees.
More precisely, the KI view emphasises the importance of integrating specialised
knowledge among individuals into a collective knowledge as the key ability of
organisations to achieve competitive advantage. Previous research distinguishes
individual and organisational knowledge where individual knowledge becomes
organisational knowledge when it is socialised in a group or organisation (Alavi and
Leidner 2001; Nonaka 1994). For example, to manage employees‟ resistance to an ES,
one strategy is to classify the problem into organisational and individual aspects
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(Aladwani 2001). Organisational strategies might refer to organisational structure,
managerial style and technique and communication, while individual examples can
include employees‟ attitude (effort) and awareness of involvement (Aladwani 2001; Al-
Mashari and Zairi 2000; Sarker and Sarker 2000). Here, management can use the
classification of individual and organisation to set up strategies that can best manage KI
effectiveness among employees, and try to affect individuals‟ skills and attitudes by
emphasising the importance of communication to build better ES operationalisation
(Aladwani 2001). In addition, management can deal with the needs of individuals and
the organisation by using appropriate strategies and methods in order to improve KI
practices to promote ES success.
3.3.2 Passive and Active Perspectives
Second, we classify the antecedents of KI effectiveness with regard to passive and active
elements. In our research model, active KI practices tend to be more resource
intensive for employees as individuals and as collective efforts in an organisation, while
passive KI elements are more likely to be initiated and guided by management of an
organisation. Active integration of individuals looks from an individual‟s line of sight
without considering any passive practices as the individual passive practices do not
contribute value to the KI effectiveness. Kitto and Higgins (2010) contend that active
individuals play a particular role in an ES, as they have the capacity to not only maintain
their freedom and avoid dependency but also to use this freedom in a wise manner.
In contrast, we propose the concept of active integration in an organisation through
cooperative activities from employees and management towards the achievement of a
high degree of common knowledge of ES among employees, and the frequency of
integration to communicate ES knowledge such as providing sufficient ES training or
frequent meetings. As one example, Sagawa and Jospin (2009) argue that incorporating
communications into regular training and providing opportunities for employees to
interact with the system will help to effectively develop common system knowledge
among them.
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In terms of ES, mandatory use is common (Zach 2010), whereby staff are obliged to
use the system. However, studies have found that user resistance is one of the roots of
many ES project failures (Klaus et al. 2007). Some of the reasons for user resistance
are: a lack of the ES skills required to perform the jobs, the complexity of the system,
and the lack of communication to users regarding the ES benefits. One way to address
this issue is to integrate staff knowledge, through frequent communication regarding
the plan and potential outcomes of the ES benefits (Oliver and Romm 2002) and by
effective internal training (Umble et al. 2002). Some formal ES training is important, but
those receiving it should use the ES in their day-to-day jobs. Otherwise, the training is
useless, and significant amounts of money can be spent without real benefits to either
the ES user or the organisation.
On the other hand, passive integration in an organisation refers to the passive elements
that contribute to the KI effectiveness indirectly such as a clear structure of employee
roles, and the scope of integration being managed by the organisation according to the
employees‟ roles and tasks (Klaus and Blanton 2010). Organisational structure is an
important aspect of knowledge as it concerns the establishment of work relationships
(Hendriks 2008), and the scope of employees‟ knowledge within the newly
implemented ES is a significant factor to be considered by the organisation (Aladwani
2001). There is a practical advantage of having these passive elements, when applied
efficiently to the entire organisation. For example, management in the organisation can
estimate the sufficient scope of integration of ES knowledge among their employees
within and across departments and find the best plan that suits the circumstances of the
organisation. The plan or strategy can be implemented in ways such as by providing
better policy, restructuring employees‟ portfolios or promoting new tasks, or
encouraging better KI practices through identifying a sufficient range of integration
among employees. Management also can improve the standards, guidance and
requirements for the integration of the passive elements in the organisation.
The passive elements of integration are essential for an organisation to consider
because once the ES is implemented, employees may face difficulties from changes to
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the authority structures that are caused by the new ES implementation, and not know
how to do their jobs within the scope of the new system (Aladwani 2001). Hence, the
ES could be viewed negatively if employees perceive the ES to be a threat to their jobs.
Thus, a part of this research investigates how closely the passive integration of
authority structures in the organisation correlates with the effectiveness of the
integration of employees‟ knowledge. Figure 3.2 shows the KI management framework
that is used in our research model, as adapted from a previous study of managing
knowledge by Wunram et al. (2003).
Figure 3.2: KI management framework for ES (adapted from Wunram et al. 2003)
The KI management framework outlined above reflects the significance of KI practices
in the forms of active practices and passive management across individual and
organisational levels of knowledge cooperation. The individual level of knowledge
requires employees to actively and freely integrate their efforts, skills and experiences
to improve their level of ES knowledge without taking into consideration other
external factors from other employees and management in the organisation. In
contrast, achieving the organisational level of knowledge requires cooperation from
individuals, teams or departments in the organisation. This integration of organisational
ES knowledge can be divided into passive and active elements. Examples of active
integration in an organisation are the shared common understandings of ES among
employees and the common methods being implemented to integrate ES knowledge
Shared common understanding,
motivation, methods
Efforts & skills
Strategies, policies, leaderships, roles
Individual Organisational
Active practices
Passive management
KI practices
Level of knowledge
cooperation
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among employees. The passive elements that are generated by management also need
to be considered for effective KI, such as strategies and policies to cooperatively
exchange knowledge, clarify the structure of employee roles and enhance leadership in
the organisation.
3.4 THE ANTECEDENTS OF KI EFFECTIVENESS
Having discussed the importance of understanding and identifying the active and passive
elements for KI practices across individuals and an organisation, we now discuss how
we restructure the antecedents of KI effectiveness from the KBT of Grant (1996), as
shown in Figure 3.3.
Figure 3.3: Restructuring the antecedents of KI effectiveness
As shown in Figure 3.3, we classify the antecedents of KI effectiveness in three main
constructs (in bold boxes): (1) the passive elements of KI in an organisation, which we
refer to as the passive integration of the organisation (PIO); (2) the active elements of
Organisational structure
Common knowledge
Frequency of integration
Complementary knowledge
Greater integration
Extend knowledge
Re-configure knowledge
Passive Integration of an Organisation (PIO)
Active Integration of an Organisation (AIO)
Active Integration of the Individual (AII)
Efficiency
Scope
Flexibility
Grant’s constructs Factors This research constructs
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KI practices that we submit are the active integration of the organisation (AIO); and (3)
the active KI practices for ES users individually that we present as the active integration
individual (AII). We explain the details of these three antecedents in the next sub-
sections. Within the KBT outlook, this research seeks to examine the three
antecedents and consequences of KI effectiveness in the ES context, where we derive
two KI effectiveness consequences: (1) the goodness of individuals‟ ES-knowledge base,
which then leads to (2) the ES success in an organisation. Figure 3.4 presents our full
research model.
Figure 3.4: The research model
3.4.1 Passive Integration of an Organisation
We define the passive integration of an organisation (PIO) as the passive elements that
contribute to the KI effectiveness indirectly and are being administered by the
organisation according to the employees‟ roles and tasks. The PIO enables an
organisation to provide some kind of knowledge integration platform to their
employees. Organisations can provide the platforms in two ways.
First, the organisation builds a clear structure of authority regarding employees‟ roles
and decision rights. The structure of authority of an organisation enables employees to
interact in excellent design work (Sagawa and Jospin 2009) and provides leadership
opportunities. A clear structure, including the proper resource and management, can
help organisations generate innovation and build knowledge assets (Teece 1998). The
PIO H1
H2
H3
AIO
AII
KI
Effectiveness
ES-
Knowledge
Base
ES Success
H4
H5
Antecedents
Consequences
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KBT argues that the structural arrangements allow for the cooperation and
coordination of the knowledge. As such, the structures should be arranged to facilitate
the flow and access to people‟s knowledge. In traditional forms of organising, KI is
preserved by a hierarchy wherein knowledge flows are mainly vertical and
unidirectional between headquarters and departments, business units and operating
units (Pettigrew and Fenton 2000). Despite that, rigid and hierarchical organisational
structures may limit the interaction among different functional areas in departments
and in the organisation (Mohamed et al. 2004). However, in an organisation where the
hierarchy remains present, the vertical knowledge flow is supplemented by horizontal
and multidirectional knowledge flows in a department (Van Wijk and Van den Bosch
1998).
The way in which departments are internally designed determines how employees
familiarise their approach to communicate their knowledge to superiors such as
managers, group leaders or experts (Krone et al. 2009). Organisations may decide to
assign tasks to facilitate the flow of knowledge among employees, and provide the
infrastructure required for their task completion (Hendriks 2008).
Implementing an ES is not an easy task and can cause dramatic changes to an
organisation (Colmenares and Otieno 2008). When the ES is implemented, the impact
of all the change management and innovation on business processes, systems and
organisational structures should be fully monitored (Badii and Sharif 2003). To be
effective, individuals in any organisation should know who has the required ES
knowledge and expertise, where the ES knowledge and expertise are located, and
where they are needed. Thus, good structure allows the employees to solve their ES
problems and maximises the efficiency of the ES knowledge integration. Through a
good structure, diverse knowledge and expertise of individuals in various locations in
an organisation can be assembled, integrated and applied to the task at hand (Alavi and
Tiwana 2002).
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The second way in which an organisation can provide a platform for knowledge
integration is to establish a sufficient scope of integration among employees in order to
build an adequate knowledge interaction capacity. As established in literature review,
managing ES-related knowledge is complicated as it involves many ES users from
different cohorts (e.g. managers, operational staff) and diverse knowledge capabilities
(Sedera and Gable 2010). The different knowledge bases and views of diverse
employees will bring varied perspectives to the ES knowledge, which leads to greater
knowledge integration impact. From a knowledge-based perspective, a central challenge
to employing a system is the integration of knowledge that is dispersed across people
with business and technical (software) domain knowledge (Patnayakuni et al. 2007). The
software knowledge can be learned outside the boundaries of an organisation, while
business knowledge is learned through a long-term learning process which may need to
constantly interact with the organisation‟s stakeholders who possess the required
business knowledge (Dibbern et al. 2002). The example of software knowledge is
knowledge of the specification and software functionality in configuration tasks.
The sufficiency of scope is dependent on the sufficient breadth of knowledge being
integrated. There are two important measures of scope here: complementary rather
than substitute knowledge, and a greater scope. The complementary knowledge leads
to a meaningful integration, through which the ES knowledge will be enhanced and a
goodness of ES-knowledge base will be provided for innovation to occur. As ES users
come from different employment cohorts, and employees in every cohort are
concerned with the distinct types of ES knowledge that are significant to them,
integration of complementary knowledge can create synergistic value for the
organisation (Kim et al. 2010). Complementary knowledge requires straightforward
integration; however, for a greater scope of knowledge, there must be some effort
carried out to ensure all ES players have a common understanding on the relevant ES
subject matters. According to Hustad (2007), a wide scope of knowledge will create a
lower level of common knowledge such as shared common technical terms. This means
that the scope of integration should not be too narrow or too broad. If the scope is
too narrow, knowledge may become weak and vulnerable. On the other hand, when
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the scope is too broad, it increases the difficulty in integrating people‟s knowledge, as
the knowledge may be uncontrollable and lose effectiveness. Hence, management may
provide instructions for employees to follow and help guide the width of integration of
ES knowledge in their work. This consideration is important for better performance of
the ES.
3.4.2 Active Integration of an Organisation
We identify the active integration of an organisation (AIO) as cooperative activities by
employees and management towards creating a high degree of common knowledge of
ES among employees, and frequent efforts to communicate ES knowledge. A sufficient
level of common knowledge among employees and adequate frequency of ES
knowledge integration are crucial to ensure consistency in ES performance. We
present these aspects in an AIO construct. Communication among employees, which
involves conveying to and receiving knowledge from employees, is very important in
organisations to progress the ES mission and goal. If all employees who use the ES have
the required ES knowledge to do their jobs, better ES operationalisation will be
achieved.
For knowledge to be integrated successfully across individuals, an organisation first
needs to have a common understanding of the ES knowledge among its employees.
Grant (2000) refers to the common knowledge as shared knowledge with a common
understanding by individuals who engage in communication. When the level of common
knowledge is insufficient, individuals‟ ability to integrate knowledge is low. To increase
the level of common knowledge, individuals must have some basic understanding
among them by avoiding barriers to effectively communicate. Researchers identify
common understanding as a prerequisite for knowledge integration (Maaninen-Olsson
et al. 2008), that is necessary for the sharing and integrating of knowledge among
employees in an organisation (Grant 1996).
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Multiple perspectives, views, and intentions in utilising the ES are the important issues
when dealing with the integration of ES knowledge. To reduce conflict, users have to
meet each other, learn each other‟s point of view, exchange opinions and information
regarding the issue and learn how to communicate effectively with each other (Roloff
2008). Effective communication and common understanding of ES affect the collective
perceptions of the ES. This common understanding enables organisations to acquire a
range of their required ES expertise. For example, to be effective in integrating
knowledge, two participants should have some basic understanding; whereas, if the
common knowledge is low, then the integration becomes more difficult. Thus, it is
critical to establish common meanings across the specialised knowledge domains of the
system (Patnayakuni et al. 2007) and this is an important step in order to avoid
confusion due to different perceptions. The failure to establish common ES knowledge
among employees can cause severe problems in ES post-implementation (Peng and
Nunes 2009).
The second aspect of knowledge integration across individuals, frequency, can be
referred to as a coordination that happens through repetition and also through
continuous practice to improve the quality of coordination. The coordination ensures a
consistency of performance. Frequent opportunities for interactions enable
constructive knowledge and common understanding among employees. For example,
individuals can be involved in a satisfactory frequency of meetings for ES, ES workshops
and ES training.
Training is one of the critical aspects to increasing employees‟ knowledge base, leading
to enhanced individual and organisational performance (Weldy 2009). As the ES
implementation involves a new program throughout the organisation, teaching or
training people about the new processes is difficult. ES end-users are the people who
will be most affected by the ES implementation, so management needs to make sure
the end-users are trained effectively (Chen 2009). Normally, the knowledge becomes
common through joint training and development, through shared experiences, direct
observation, meetings and joint problem-solving among the users (Alavi and Tiwana
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2002). Management must provide an effective training program to ES users in all
affected areas of the organisation, to contribute to the essential support that is needed
by the organisation for ES success (Duplaga and Astani 2003). Sagawa and Jospin (2009)
propose that incorporating communications into regular training and providing
opportunities for employees to frequently interact about the system will help to
effectively develop common system knowledge among them. When the level of
frequency is adequate, the ES performance can improve. Thus, to communicate ES
knowledge, integration through methods such as training and meetings should be
facilitated consistently or frequently to allow employees to operate the system
effectively.
3.4.3 Active Integration of the Individual
We classify the active integration of the individual (AII) as individuals having high
flexibility in extending and reconfiguring their knowledge to produce an innovation.
This flexibility is closely related to how continuous innovation is exploited and nurtured
(Huang and Newell 2003). Innovation matters in the AII construct in two important
ways. First, individuals should be able to freely discover better knowledge by extending
their ES knowledge. Second, individuals must flexibly reconfigure their existing ES
knowledge to produce an improvement to their knowledge of the system. Individuals‟
capabilities can be enhanced if they can access additional knowledge, such as knowledge
from different knowledge bases, to reorganise their existing knowledge into new types
of capabilities (Hustad 2007). Without sustaining innovations, individuals may lose their
knowledge relevance and effectiveness.
In general, individuals do not have all the necessary ES knowledge and need to acquire
additional knowledge to effectively accomplish their tasks (Hong et al. 2008). The ES
users should freely practice to decide how and to what extent to utilise, extend or re-
build their knowledge. Sufficient flexibility of integration can enable them to maintain
the ES performance. It can create a new innovation for better ES performance. This is
necessary to ensure that every individual consistently has good ES performance. Here,
individuals develop and strengthen their own ES knowledge, and adapt, change and
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exchange their knowledge across multiple sources in order to have better knowledge
of the system (Ying et al. 2006). The flexibility allows each individual the freedom to
create new knowledge according to his or her needs and specific interests (Saari and
Talja 2009). When using the ES, all ES players can continue to make their performance
better if they can access the additional ES knowledge that is required for their tasks.
For instance, individuals may gain additional knowledge not only from standard
procedures, system guidelines and updated information from others, but also can
creatively apply any suitable knowledge and methods to solve problems related to the
ES. How the ES knowledge is configured and deployed will shape the effective
outcomes of the KI among employees.
3.5 KI EFFECTIVENESS
KI is concerned with the combination of knowledge to synthesise other knowledge
(Grant 1996; Kogut and Zander 1992). It involves a dynamic process of linking,
connecting, distinguishing, organising and structuring ideas (Clark and Linn 2003). KI
can be seen as the sharing and synthesis of specialised knowledge through the ongoing
collective processes of the social interactions of the organisational members. For
example, the ES team may facilitate and configure the adoption of the ES, access and
share widely-distributed knowledge and integrate the knowledge in new ways when
designing new organisational processes that will be supported by the system (Pan et al.
2007). For this reason, the ES team needs to interact among themselves and with other
stakeholders to make sense of both the organisational processes and the ES.
Accordingly, effective KI is observed when employees are successful at coordinating the
utilisation of other employees‟ specialised knowledge (Alavi and Tiwana 2002; Caya
2008). Once specialised knowledge is sufficiently integrated, individuals may contribute
to innovation without explicit communication because they understand how all the
individual operations in an organisation fit together. However, while the KI is
important, it is not always effective (Okhuysen and Eisenhardt 2002) because critical
information is often not used by individuals. KI effectiveness leads to a situation where
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the breadth and depth of specialised knowledge are leveraged at a collective level
through which employees are able to extract other‟s expertise (Alavi and Tiwana 2002;
Caya 2008; Tiwana and McLean 2005). To achieve at the collective level, each individual
must not only share his or her own knowledge and resources, but must also learn the
work of others to have an integrated view of the knowledge (Janz and Prasarnphanich
2009). When knowledge is leveraged, employees can benefit from the diversity of
expertise and skills that exists among them. As a result, employees may minimise the
unnecessary duplication of other‟s time, energy and talent (Caya 2008). Therefore, we
define our KI effectiveness as “the ability to successfully combine and synthesise the ES
value from others‟ expertise”. This KI effectiveness is gathered from the three
antecedents of passive and active integration of organisations, and also from the active
integration practices of individuals.
3.6 THE CONSEQUENCES OF KI EFFECTIVENESS
Consistent with Markus et al. (2003), this research argues that having goodness in the
ES-knowledge base depends on how well the knowledge is integrated. For this reason,
we suggest that the KI effectiveness leads to: (1) goodness of individuals‟ ES-knowledge
base, which then contributes to (2) the ES success in an organisation. Both of these
consequences are discussed in this section.
3.6.1 The Goodness of Individuals’ ES-Knowledge Base
As raised in the previous discussion, each employee brings a different level of ES-
knowledge base, because the types of ES knowledge in terms of software, business
processes and organisational requirements vary for each level of employment cohort
(strategic, management and operational). Citing Davenport, Sedera and Gable (2010)
contend that the production of knowledge requires a coordinated effort of individual
specialists who possess many different types of knowledge including the software and
„best-practice‟ business processes. ES knowledge type requirements vary for each level
of employment as suggested by Anthony (1965). For example, knowledge that is
required by the management level focuses on ensuring that the organisation‟s resources
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are used effectively and efficiently to accomplish the goals identified at the strategic
level (Sedera 2007). In contrast, the operational level is involved in highly structured
and specific tasks that are routine and transactional. Software knowledge is crucial for
operational employees, as their technical knowledge of the ES is one of the critical
success factors for ES success (Colmenares and Otieno 2008). On the other hand,
business process knowledge is very important for employees in management groups. In
simple terms, not all levels of staff need to know all the ES knowledge, yet the effective
integration of ES knowledge among them leads to improvement of their ES-knowledge
bases. The more effectively people‟s knowledge is integrated, the better their ES-
knowledge base. Having emphasised the importance of the ES-knowledge base, we
define our ES-knowledge base as “the combined collection of all the individuals‟ ES
knowledge types, including tacit and explicit knowledge”. This is consistent with
Dibiaggio and Nasiriyar (2009) who describe the knowledge base as a pattern of
knowledge elements, and we refer to the knowledge elements in this research as the
knowledge types. In this context, to measure the goodness of ES-knowledge base, we
examine the ES knowledge understandings through ES knowledge types and individuals‟
viewpoints about the knowledge.
Creation of the ES-knowledge base occurs through the integration of the employee
knowledge, and knowledge of the system is continuously produced and reproduced as
the learning process occurs from interactions. Markus (2001) suggests that the
application of the knowledge base can be a source of competitive advantage. Revilla and
Curry (2008) also argue the importance of managing the knowledge base to create new
knowledge and recombine existing knowledge for an organisation‟s competitive
advantage. More importantly, they suggest that the capabilities of the knowledge base
depend on the function of KI and how people integrate specialised knowledge.
3.6.2 The ES Success
As a causal effect of ES-knowledge base improvement, we suggest that the goodness of
an individual‟s ES-knowledge base creates a positive impact on the ES success in an
organisation. With the cost of ES implementation typically in the millions of dollars, ES
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failure can significantly impact on organisational stability and can cause the failure of an
organisation (Chen 2009). Consistent with Revilla and Curry (2008) who argue that
knowledge base capabilities lead to better product development performance, our
research model accounts for the role of the goodness of individuals‟ ES-knowledge base
in creating the ES success. The goodness of the ES-knowledge base helps individuals
know how to use the ES effectively. It improves and increases their understanding, and
ensures consistency and quality in ES performance. Moreover, a high-level ES-
knowledge base ensures staff understand the ES procedures and processes, and know
how best to respond to the ES problems. In addition, it may increase their commitment
to use the ES at optimal levels.
To measure the success of the ES, we employ the success measures from the IS-impact
measurement model (Gable et al. 2008). This model is multidimensional. As discussed
earlier, it comprises four quadrants, namely, individual impact, organisational impact,
information quality and system quality. The system quality construct is used to measure
the performance of the system from a technical and design perspective. Information
quality is a measure of the system output concerning the quality of the information.
Individual impact refers to the measure of influence by an individual‟s capabilities and
effectiveness, while organisational impact measures the organisational results and
capabilities. This IS-impact model is claimed to be one of the most comprehensive
validated IS success measurement models to date (Petter et al. 2008; Sedera and Gable
2010). According to the extensive evidence offered by Gable et al. (2008) regarding the
validity of Enterprise System success, this research uses all four quadrants.
3.7 HYPOTHESES DEVELOPMENT
The research hypotheses are formulated from our research questions. Our utmost
interest is to investigate “What is the impact of KI effectiveness on the ES success?” To
understand the issue, we explore the antecedents of KI effectiveness, and its
consequences for ES operationalisation. The research question that guided the
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formulation of our hypotheses on the antecedents is “Do the constructs of KBT make
a substantial positive contribution to KI effectiveness?”
The development of our research hypotheses is well substantiated by the literature
review. Building on Grant‟s (1996) conceptualisation of KI, we conclude that there are
three factors that influence KI effectiveness, and this leads to our first three hypotheses
(shown as H1, H2 and H3 in the research model - Figure 3.4):
Hypothesis 1 (H1): The passive integration of an organisation has a
positive influence on KI effectiveness.
Hypothesis 2 (H2): The active integration of an organisation has a
positive influence on KI effectiveness.
Hypothesis 3 (H3): The active integration of an individual has a
positive influence on KI effectiveness
In answering the question “What is the influence of KI effectiveness on the goodness of
individuals‟ ES-knowledge base?”, we hypothesise that by having effective KI, an ES
user‟s level of ES-knowledge base will be increased. Typically, there are three types of
ES knowledge: business process knowledge, organisation knowledge and software
knowledge (Davenport 1998). These types of knowledge are contributed by the ES key
players of consultant, vendor and client organisation. During implementation,
consultants and vendors bring together their prior work experience, work values,
norms, philosophies and problem-solving approaches (Ko et al. 2005). The combination
of all the ES knowledge, including tacit and explicit knowledge, within the individuals,
systems or physical entities purportedly creates the ES-knowledge base. Although a
knowledge base entails both human and physical entities, this research focuses only on
the ES knowledge on the human side within organisations and from the client
organisation perspective.
As people interact with each other on the basis of their own beliefs and interests
throughout the learning process, people adapt and adopt new knowledge, and create
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and store up their own dynamic ES-knowledge base (the knowledge of software,
business process and organisation either in tacit or explicit forms). Individuals in
organisations bring their ES software-specific knowledge, business process knowledge
and organisational knowledge to bear. When the knowledge of the ES is integrated, the
ES-knowledge base then changes, which triggers the process of producing and
reproducing their knowledge of ES during integration. With an expectation that
effective integration of ES knowledge among individuals will improve the level of their
ES-knowledge base, we propose our fourth hypothesis (H4 in Figure 3.4):
Hypothesis 4 (H4): KI effectiveness has a positive influence on the goodness of
ES-knowledge base.
Goodness of the ES-knowledge base helps individuals know how to use the ES
effectively. It improves and increases their understanding, and ensures consistency and
quality in ES performance. Moreover, an improved ES-knowledge base will ensure users
understand the ES procedures and processes, and know how best to respond to the ES
problems. It also may increase their level of commitment to using the ES optimally. We
submit that, the better the employees‟ ES-knowledge base, the better their quality of
work and decision-making, and the more clarity they have about what, how, why and
when the ES knowledge suits their jobs and tasks. This leads to our final hypothesis.
This research argues that the goodness of the ES-knowledge base depends on the
effectiveness of the individual‟s ES knowledge integration in the organisation, which in
turn plays an important role in the success of an Enterprise System‟s use.
Subsequently, we examine the question “What is the impact of ES-knowledge base on
ES success?” In response to this question, we predict that the goodness of the ES-
knowledge base among ES users will benefit the ES performance by producing better
knowledge to operationalise the ES, which is the outcome we refer to as the ES
success. This hypothesis is represented as H5 in our research model in Figure 3.4:
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Hypothesis 5 (H5): The goodness of ES-knowledge base has a positive influence
on the ES success.
To explain the impact of the KI on the ES success, this research focuses on individuals‟
perspectives in two groups of employment cohorts: the operational and managerial
groups. In the next section, we summarise the relationships among the constructs that
are being investigated in our research model.
3.8 SUMMARY
This research offers a new contribution to approaches that examine the relationship
between KI and ES success, which is best understood by using the theoretical view of
KBT. This research aims to meet the demand for understanding ES success by
highlighting the importance of KI practices among employees in an organisation. With
the cost of ES implementation typically in the millions, ES failure can significantly impact
on an organisation‟s stability, which in turn can cause the failure of the organisation
(Chen 2009). Table 3.1 summarises the key sets of relationships that are being tested in
our research model.
Table 3.1: Summary of hypotheses tests
Hypothesis Hypothesised effect
H1: Passive integration - organisation (PIO) KI effectiveness +
H2: Active integration - organisation (AIO) KI effectiveness +
H3: Active integration - individual (AII) KI effectiveness +
H4: KI effectiveness ES-knowledge base +
H5: ES-knowledge base ES success
+
The research model posits two main components: the antecedents of KI effectiveness,
and the consequences of KI effectiveness. For the first part, we identify three salient
antecedent of KI effectiveness for the ES context, which are the passive integration of
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an organisation (PIO), the active integration of an organisation (AIO), and the active
integration of individuals (AII). The advantage of grouping the KI antecedents by these
three factors is that each of the antecedents makes its own unique contribution
towards KI effectiveness. It is easier to understand the issues associated with
integrating knowledge for ES by categorising the antecedents into passive and active
factors of individuals and organisational perspectives. We believe that this is a useful
approach as it reflects the reality of knowledge in the ES context. Organisations can
gain benefits from the classification as it places a different focus on specific factors in a
constructive way. Organisations can consider different initiatives or approaches to KI
practices among ES users with reference to these different aspects. In the second
component of the research model, we propose that KI effectiveness can contribute to
the goodness of individuals‟ ES-knowledge base; in turn, a good ES-knowledge base
creates ES success. In sum, we believe that by applying our research model as a
framework, managers could initiate actions and provide innovative solutions for KI
effectiveness in order to have successful ES use.
To test our research model and hypotheses, data were collected using the survey
technique in a questionnaire format. Details of the data collection are discussed in
Chapter 4 which outlines the survey development.
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CHAPTER 4: SURVEY
DEVELOPMENT
4.1 INTRODUCTION
In Chapter 3, we discussed the research model and hypotheses development. The
chapter reported how our research model and hypotheses were derived from the
knowledge-based theory of the firm (KBT) of Grant (1996) and other prominent
literature. In this chapter, we discuss further the operationalisation of the research
model and the application of the survey method (as shown in Figure 1.3 in Chapter 1).
This chapter presents the design of the survey process in detail.
First, the chapter explains the unit of analysis, the data collection objectives and the
steps taken to minimise the common method variance (CMV). It then presents the
overall survey design process in detail, including the procedures to operationalise the
research model constructs. Next, the respondent anonymity and confidentiality are
discussed. The chapter then concludes with an overview of the survey method as the
research methodology employed.
4.2 THE UNIT OF ANALYSIS
The unit of analysis in this study corresponds to the individual level. Our research
observation is nested within persons where the unit of analysis question is related to
the individual perspective of employees in our sample organisations. The analysis is
conducted to identify the KI effectiveness and its antecedents, and the goodness of ES-
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knowledge base and the ES success as the consequences of KI effectiveness. To
investigate the influence of KI effectiveness on ES success, we believe it is important to
collect data in settings where the KI has been integrated in operationalising the ES with
respect to each respondent‟s viewpoint. Given that this ES knowledge is totally held by
individuals, we focus our data collection on the factors that contribute to KI
effectiveness, the impacts of effective KI in terms of individuals‟ ES-knowledge base and
their views on the ES success.
4.3 DATA COLLECTION OBJECTIVES
The primary goal of this study is to determine the role of KI practices in an
organisation (passive and active mode) and of individuals (active mode) on the
effectiveness of KI, the impact of KI effectiveness on the goodness of ES-knowledge
base, and consequently, the significance of the goodness of the ES-knowledge base for
ES success. To achieve this goal, this study aims to develop valid and reliable
measurements for the antecedents of KI effectiveness, KI effectiveness and its
consequences. The passive integration of an organisation (PIO), active integration of an
organisation (AIO) and active integration of an individual (AII) are determined to be the
drivers of KI effectiveness; while the goodness of individuals‟ ES-knowledge base and
the successful use of ES are the results of practicing effective KI.
4.4 MINIMISING THE COMMON METHOD VARIANCE
During data collection, we put in place the KBT (Grant 1996) explanation of influence
factors for KI effectiveness to ensure the research maintains integrity with the original
thinking. We carefully align our research with Grant‟s theoretical position. We are in
agreement that, in general, KI effectiveness is influenced by factors including
organisational structure, the level of common knowledge, and the frequency, scope and
flexibility of integration. The decision was taken to minimise common method variance
in our research. Chang et al. (2010) suggest that researchers should avoid or reduce
any potential CMV by constructing variables using information from different sources,
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and mixing the order of the questions to reduce the likelihood of theory-in-use bias.
This procedure is taken to reduce potential bias during our data collection. We believe
that being consistent with the recommended approach enhances the validity of our
results.
In developing our questionnaire survey, we were aware that our questions should be
short, simple and specific as the wording of questions has an important influence on the
responses that are given (Williams 2003). Difficult questions may produce inaccurate
answers, or the respondents may fail to complete the questionnaire. Following the
guidelines, we designed our survey questions with a consistent format throughout the
instrument and logically organised the questions without rigidly following the structure
of the research model. This was done to receive a high quality response as well as to
minimise the CMV. We grouped items in the questionnaire in logical coherent sections.
Having put aside the sequence constructs and components from the KBT and the
research model, we grouped the similar questions to make the questionnaire easier and
more comfortable to complete. This method was also taken to minimise the CMV.
4.5 SURVEY DESIGN
Figure 4.1 depicts the main steps of our survey design. This survey design is expanded
from our research design as previously shown in Figure 1.3 (in Chapter 1). The survey
design process included six steps: 1) design the survey instrument; 2) translate the
survey instrument; 3) select the research sample; 4) validate the content of the survey
instrument; 5) revise the survey instrument; and 6) deploy the survey1. Details of the
process are presented in the following sections.
1 The application for ethics clearance was reviewed by the university research ethics committee and was
approved until 17th September 2010 (ID number 0900000981) for data collection in Malaysia.
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Figure 4.1: The survey design
4.5.1 Survey Instrument
This section describes the questionnaire development and data collection procedures
used for this research. The new scale development of the antecedents of KI
effectiveness are first described, followed by the KI effectiveness construct, and the
consequences of KI effectiveness. Where possible, measures were adapted from
existing instruments in the literature. The process of translating the survey instrument
and our strategy in selecting the research sample are outlined. Next, we explain the
process of establishing the questionnaire validity and revising the instrument, and we
then explain the survey deployment.
a) Rating Scale Development
Responses were presented in rank choices asking respondents to complete a Likert
scale indicating their level of agreement. The scale has 44 questions in 6 constructs
Design survey instrument
Translate survey instrument
Revise survey instrument
Deploy survey
Select research sample
Validate the content of survey instrument
1
2
3
4
5
6
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with a seven-point rating scale, in which we display the scale from negative to positive,
left to right. Using the seven-point scale is more accurate, and gives much more
information to generate statistical measurements of respondent's attitudes and opinions
(Flynn et al. 2004)2. The scale is based on how respondents feel, indicated as: Strongly
Disagree, Disagree, Slightly Disagree, Neutral, Slightly Agree, Agree and Strongly Agree
as points 1, 2, 3, 4, 5, 6 and 7 respectively, as seen in Table 4.1. To understand the
frequency of integration, we also employ a seven-point scale of the activity regularity,
with 7 being the most frequent, as shown in Table 4.2. The following sections discuss
the six constructs and the rationale of their measures in the survey instrument.
Table 4.1: Rating scale of agreement
Strongly do
not agree
Do not
agree
Slightly do not
agree Neutral Slightly agree Agree
Strongly
agree
1 2 3 4 5 6 7
Table 4.2: Rating scale of frequency
A few times
a year
Once a
month
A few times a
month
Once a
week
A few times a
week Once a day
A few times a
day
1 2 3 4 5 6 7
4.5.2 The Antecedents of Knowledge Integration Effectiveness
(a) The Construct of the Passive Integration of an Organisation
Following the explanation of KI effectiveness by Grant (1996), and in accordance with
our interpretation of the construct, two dimensions, namely organisational structure
and scope, are covered in the construct of passive integration of an organisation (PIO).
2 Also see the report entitled “Rating scales can influence results,” Quirk’s Marketing Research Review,
http://www.quirks.com/articles/a1986/19861003.aspx?searchID=4971371&sort=9
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(i) Organisational Structure Measures
Structure is about the systematic arrangement of the functions, responsibilities and
authorities of individuals within or across different departments in organisations (Krone
et al. 2009). Responsibilities refer to what they do, who they report to or who reports
to them. There are many people working toward a common objective who may often
be in different locations. When organisations become large, they become
unmanageable, inflexible and difficult to focus. Thus, organisations must have a clear
hierarchy in terms of roles or positions of its staff which can facilitate communication
and increase spontaneous interactions, and maximise coordination among sources of ES
specialised knowledge while minimising the extent of unnecessary communication.
The hierarchy of staff, departments or work units has implications for how ES
specialised knowledge is effectively combined. A good hierarchy in organisations is
essential for productivity and efficient decision-making (Dibachi and Dibachi 2003).
Clear organisational structure is very important to manage staff. If there is a question
about how staff should do something, there must be a staff member with responsibility
to decide the issue.
The organisational structure measure that we use aims to understand whether there is
a clear organisational structure that enhances the knowledge flow between employees
and facilitates KI among employees effectively. Even though a number of measures for
organisational structure were found in previous studies, the new measures are
introduced in this research to ensure an explanation from KBT is closely followed. A
good organisational structure increases access to ES resources, and allows employees
to learn from others who have better ES expertise through staff skill recognition
(Grant 1996; Pan et al. 2007). This allows easy communication among knowledgeable
people. People can group together according to their similarities in their positions, and
can easily communicate and share ES information with each other. If each individual in
the organisation knows what he or she is supposed to be doing, there is less room for
confusion and poor judgement. The structure also makes it easier for people to learn
from another‟s experiences. It can facilitate face-to-face problem solving, allowing staff
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members to assess the skills and knowledge of the most appropriate person. That is
how the clear structure of an organisation can facilitate the effective knowledge
integration among its members.
A second measure is used to identify employees‟ decision-making and authority by
reference to their functions or departments. According to Dibachi and Dibachi (2003),
when a work group is very small, and face-to-face communications is frequent, good
structure might not be very important, but the delegation of various tasks is very
important in larger organisations. Since all our sample organisations are large, decision-
making responsibilities must be determined and need to be distributed. In these
settings, management should be able to delegate authority down the hierarchy where
this function is identified as a path for successful KI (Krone et al. 2009). Thus, a clear
hierarchy or structure addresses the efficiency of decision-making within an
organisation. This provides staff with clear roles and accountability for decision-making.
If there are unclear tasks, staff‟ functions would overlap and work might be unfinished,
which then increases conflict among staff. When staff know how individuals, jobs,
functions or activities are differentiated or combined, this affects the performance of
how ES issues and problems are attended to, and how decisions are made. When the
roles are clearly defined, doubt is removed and accountability is clear, and the speed of
decision-making improves.
(ii) Scope Measures
Following Grant (1996), two aspects are classifiable under the construct of scope:
complementary knowledge and greater scope of integration. Grant proposes that in
order to have an effective KI, the scope of integration is adequate if the integration of
knowledge is complementary, and the integration is in a greater scope. We can best
explain the importance of sufficient integration scope by relying on the example of case
study observations by Zakaria and Sedera (2009), Pan et al. (2007) and Huang and
Newell (2003). In general, insufficient integration caused their respondents difficulties in
using the ES and left them struggling constantly for ES knowledge. Senior staff did
communicate to their junior workers but did not engage in broader community
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outreach. Thus, employees missed the chance to share the common goals of the
system due to low levels of interaction among them, and were unable to fulfil their
potential. Some respondents were trapped by traditional unproductive practices, which
limited knowledge interactions and contributed to frustrations among employees.
Regarding this observation, we agree with Grant (1996) that insufficient scope is a
causal factor for a gap in KI effectiveness.
To measure scope, first, we enquire into the scope of KI in terms of complementary
knowledge rather than substitute knowledge. Complementary knowledge is important
to avoid the duplication of efforts among staff and to avoid staff having to solve the
same problems from the beginning again and again. It is better to use knowledge that is
already available in the organisation. If the knowledge that is integrated is redundant,
this will not create any new value for the individuals or the organisation, and it will
waste time and performance. Knowledge obtained from other staff must not be
redundant, to avoid the unproductive preservation of the status quo.
Second, we examine the size and geographic dispersion which make it difficult to locate
existing knowledge and get it to where it is needed. The scope must be sufficient, that
is, not too narrow or too big, as these extremes will affect the integration
effectiveness. If the scope is too narrow, it increases the occurrence of interruptions
and non-value-added communications/meetings. Greater scope entails the involvement
of other work units or departments and increases access to more diverse sources of
ES knowledge and innovation (Huang and Newell 2003). Therefore, the greater the
span of knowledge being integrated, the more difficult it is to accomplish the
integration (Grant 1997).
(b) The Construct of the Active Integration of an Organisation
In the construct of active integration of an organisation (AIO), we identify two
dimensions that link closely with the active integration in organisations. The first aspect
is common knowledge among employees, and the second is frequency of activities that
allow knowledge integration between employees.
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(i) Common Knowledge Measures
Common knowledge among staff can be developed through formal and informal
communications. The communications gather common concerns through compromise
and resolution, and allow staff to reach decisions and clarify follow-up actions.
Individuals gain their common knowledge through meetings as they gain information
and instructions, and clear up misunderstandings by integrating ideas and views
expressed during the sessions. Interactions in formal (organised by management) and
informal (initiated among staff) meetings form group consensus with the purpose of
establishing common understanding and clarifying responsibilities among staff. Such
interactions are useful to inform staff of their duties and equip them with the
knowledge required to carry out their duties, enabling them to measure the outcome
of the ES, make improvements and increase their knowledge. The interactions can be
used to determine what knowledge they have in common, whether staff are satisfied
and if there are any issues to address. Thus, the common knowledge among them is
generated. As reported in the literature (e.g. Cardon 2001; Caya 2008; Haddad 2008),
communications which engage staff contribute to the development of common
knowledge in organisations.
Our first question enquires into the function of job rotation as a process to improve
common ES knowledge. Job rotation is the lateral transfer among staff with different
positions, tasks, duties and responsibilities (Haddad 2008). We determine that this
activity is designed to give exposure to a breadth of knowledge (Davenport 1993),
which we refer to as the ES knowledge. It involves assigning staff to various jobs, so
they obtain a wide base of knowledge and skills. Job rotation helps staff understand
how their effort affects the quality of the ES, how each staff contributes to ES
utilisation, and the different steps that go into utilising the ES. It encourages staff to
stimulate the growth of basic understanding of the ES. Intra-functional rotation is
designed to train staff for better performance of their job within the ES utilisation. It
creates generalised abilities and understanding of the ES among staff.
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In our second line of questioning, we seek to identify the support of communication in
exchanging ideas and questions among staff (Cardon 2001; Caya 2008; Haddad 2008).
Openness in communication among staff improves the staff productivity. Effective
communication depends on understanding the language and different styles among staff.
Common knowledge represents elements of ES knowledge that are common to all
staff. Various forms of common knowledge can be found in the ES such as ES terms,
syntaxes, codes and shared meaning. Addressing communication difficulties due to ES
languages and codes among staff in an organisation is necessary to minimise
misunderstandings.
People interact with each other with the expectation of enhancing their performance
through sharing or transferring knowledge. Due to the complexities of the ES, new ES
users often speak different technical and procedural languages. This makes
communication and knowledge transfer complicated. It can cause missed opportunities,
miss-specified ES requirements, and missteps at critical junctures in ES utilisation.
Specialised ES common knowledge is essential for ongoing ES maintenance,
customisation efforts and utilisation.
(ii) Frequency Measures
As reported by Haddad (2008), KI occurs through meetings that involve lively
discussion and open dialogue on the real issues of the ES. Such meetings engage all
participants, reach decisions and clarify follow-up actions. Adequate ES training that
includes extensive staff interactions during the sessions helps them learn how to use
the ES effectively (Scott 2005). This indicates that training improves and increases staff
members‟ common understanding of the system, and ensures consistency and quality in
ES performance. Through such training, staff gain basic knowledge of the ES procedures
and processes, and learn how best to respond to ES problems.
A sufficient level of frequency is important to ensure the consistency of ES utilisation.
The frequency can be referred to as a coordination that happens through repetition
and continuous practice. It is very hard for an idea or piece of information to be
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completely understood the first time it is seen. Thus, people learn through repetition,
which enables them to grasp the ideas completely and gain confidence. When the level
of frequency is adequate, the ES performance can be improved. Table 4.3 lists the
questions about frequency that we asked our respondents. We asked these questions
to identify the level of frequency of interactions that allow knowledge integration
among staff and create consistency in ES utilisation.
Table 4.3: Frequency questions
Items Questions
Freq1 In the workplace, how many times do you get involved in informal
discussion regarding the system usage with other staff?
Freq2 In the workplace, how many times do you get involved in formal
meetings to share recent knowledge and system solutions?
Freq3 How frequently do you receive training or guidance (formal or informal)
on how to perform your job using the system?
Freq4 How frequently do you receive new information of feedback on the parts
of the system you are expected to use to perform your job?
(c) The Construct of Active Integration of an Individual
In addition to increasing the ES performance, staff need to utilise the ES at optimum
levels. KI effectiveness is driven by the need to continually innovate the existing
knowledge. Staff should be given the freedom to develop and improve their own
knowledge, and to extend and re-configure their on-hand knowledge. This process,
known as flexibility of integration, can make a positive impact on staff knowledge
(Huang and Newell 2003; Pan et al. 2007).
(i) Extend Measures
If staff are able to improve their productivity and increase their performance through
practice and self-perfection, the result will be an innovation to their knowledge. For
people to learn and retain their knowledge or skills, they must apply their knowledge.
First, individuals must learn how to use the system, and experiment to solve problems
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and apply new knowledge as part of their learning process (Huang and Newell 2003;
Barrett et al. 2004). New knowledge does not exist to a significant extent if people
merely listen, memorise or receive knowledge without practising. They must reflect on
what knowledge they have from past experience, and apply it to their daily tasks.
Innovation comes from learning, experimentation and re-use of knowledge. It comes
from learning by doing, learning by using technologies and learning by interacting with
others (Smith 2006).
Second, innovation is a change in the process of doing something (Badii and Sharif
2003). The goal of innovation is positive change, to make something or someone
better. Since innovation leads to an incremental improvement in performance, it is
considered to be critical. Building flexible ways to obtain knowledge means that staff
can respond quickly to any problems. Staff may cope with change by adjusting their
procedures to be in accord with their future requirements as long as they have a clear
picture of where they are headed. They can use their own discretion to make decisions
based on the circumstances, not standard procedure. To obtain new knowledge, staff
are not limited to a specific procedure or bound to a standard process.
(ii) Re-configure Measures
To improve ES knowledge, the knowledge should be connected to previous knowledge
as many as possible. Staff need to creatively use knowledge from others to add value to
ES performance. In order to gain better understanding of this aspect, we measure
employees‟ creativity in adapting and adopting the ES, such as creating new ES solutions
and providing better task completion from their existing knowledge in ways that may
be distinct from standard procedures or established methods.
4.5.3 The Construct of KI Effectiveness
Employees have a wide variety of know-how, skills and abilities in utilising the ES. This
variety is normally due to their education levels, experiences, work styles, and many
other factors (Wagner and Newell 2004; Wagner and Newell 2007). However, to have
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good ES performance in any organisation, the variety of knowledge itself is inadequate,
unless it is integrated among employees (Alavi and Tiwana 2002; Worley et al. 2005).
Thus, staff should know how they fit in the organisation and how others fit as well.
They should know who has the most appropriate knowledge resources. This makes the
knowledge accessible and easily available. It saves staff unnecessary effort.
Employing Tiwana (2001) and Mehta (2006), first, we look into whether employees
successfully combine their knowledge to be more competent than they were
previously. Combining knowledge helps staff formulate new ideas relevant to the work
they are doing. This eventually creates new ES knowledge by helping them synthesise it.
The accumulated knowledge creates value to them, which can make a difference to the
success of the system.
Second, we examine whether respondents are able to share their expertise through
discussions and joint activities that help staff learn from each other. Such interactions
enable staff to develop a set of ES knowledge that becomes shared expertise for their
practice. People who are freely exchanging ideas are better, smarter and more efficient
in their work.
Third, we look for individuals‟ capabilities in combining their complementary ES
knowledge to achieve ES-specific approaches and applications. A combined expertise
allows them to take collective responsibility for recognising problems and developing
solutions. This creates better ES performance as it involves more people‟s knowledge.
Next, a measure is developed to assess respondents‟ clarity regarding their
responsibilities and the ways in which their work will benefit others. The more
knowledge integration, the more people understand how their work fits together and
they can then review each other‟s work. This is essential to make it easier for people
to clearly define the required ES skills and expertise relevant to their work.
Fifth, we determine respondents‟ awareness of other roles. People who do not know
how their work effectively fits with others cut their skills off from the rest as they do
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not know how to use their knowledge when working with others. This is addressed in
two separate questions regarding respondents‟ perspectives on behalf of themselves
and also their awareness of others.
Lastly, following Stringfellow (1998), we provide a new scale to verify whether people
gain sufficient skills through shared experience among employees. Shared experience is
a mechanism in which an individual learns every time another person does something
that the individual is able to observe or participate in. Shared experience contributes to
the development of people‟s skills. Employees are able to develop more skills and
sustain their skills by sharing experience, which in turn gives them experience with ES
expertise that carries over to others and leads to improvement in how ES problems
are resolved.
4.5.4 The Consequences of KI Effectiveness
(a) The Construct of ES-Knowledge Base
This research uses the construct of ES-knowledge base to empirically examine the
impact of the goodness of ES-knowledge base on the success of the ES. Knowledge
becomes powerful when it is combined among individuals and develops individuals‟
knowledge sources to expand their ES-knowledge base. In line with the
conceptualisation of the ES knowledge proposed by Davenport (1998), we consider
system knowledge, business process knowledge and organisation knowledge as
dimensions which exist at a deeper level of the ES-knowledge base. In a very small
organisation, it may be sufficient to convey many simple business processes verbally.
However, problems can occur when best practices erode and people informally train
each other and leave steps out (Dibachi and Dibachi 2003). As employees learn new
things during interactions, explicit knowledge such as system processes, business steps
and procedures are captured. Thus, they integrate and update their own knowledge
bases with minimal effort. As they become more experienced and are exposed to
different situations, they will likely run across additional knowledge that can then
increase their knowledge base. This study justifies the items selected to operationalise
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each dimension by reference to the extant literature. However, due to the scope of
this study, we neither use nor analyse the organisational knowledge dimension.
Referring to Cardon (2001), Hong (2000) and Ranniar (2005), we provide six new
scales to measure the goodness of individuals‟ ES-knowledge base. The goodness of the
ES-knowledge base is gained from the KI effectiveness and provides more competent
staff, giving them a morale booster in doing their jobs as they have the necessary skills.
A good level of ES-knowledge base among staff ensures that they know how to
operationalise the ES effectively and benefit fully from the system to support the
organisation‟s business requirements. Generally, the improvement of staff ES-
knowledge base enables them to work more effectively and the goodness of their
knowledge base has important positive outcomes for ES performance, resulting in ES
success.
(b) The Construct of ES Success
Gable et al. (2008) identify twenty-seven (27) measures that can be used to measure ES
success. They improve the DeLone and McLean (1992) dimensions and measures. The
improvement of the model has been validated in several publications (Gable et al. 2008;
Sedera 2006; Sedera and Gable 2004; Sedera et al. 2004). Using the 27 measures, we
follow all four (4) dimensions, as shown in Table 4.4, namely, system quality (SQ),
information quality (IQ), individual impact (II) and organisational impact (OI). However,
to match our sample context, we make some changes to the presentation of the
measurement questions by combining several questions that appropriately fit with
Malay language3 and that promise the same meaning and similar objectives as the 27
measures.
3 The survey instruments were developed in Malay language (national language) as we gathered our
sample in Malaysia.
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Table 4.4: Dimensions of ES success
Dimension Definition
Individual impact Individual impact measures are the items that assess
the extent to which the ES has influenced the
capabilities and effectiveness of key users on behalf
of the organisation.
Organisational impact
The measures of organisational impact represent
the assessment of the extent to which the ES has
promoted improvement in organisational results
and capabilities.
Information quality
This is to measure the quality of the ES outputs.
System quality The system quality measures are used to examine
the performance of the ES from a technical and
design perspective.
As a result, there are thirteen (13) items, of which four (4) items represent the SQ
dimension and nine (9) measures determine the IQ, II and OI dimensions with three (3)
questions corresponding to each of those dimensions. For the II dimension, we
combine the question about awareness (Question 2) and the question about learning
through the presence of the system (Question 1) into one concise question4. According
to an online encyclopaedia dictionary5, awareness may refer to understanding
something or knowledge, which also applies to Question 1.
For the OI dimension, three questions related to cost effectiveness and cost reductions
are merged into one concrete measure in Malay language. The merge affects Questions
5, 6 and 7 in the pool of IS-impact measures (in Appendices). The measures of
productivity improvement and increment capacity to manage a growing volume of
activity are also joined together into one item that applies to Questions 8 and 10. We
consider both questions to have the same objective, as „productivity‟ relates to having
the power to produce, while „capacity‟ means having the ability to perform or produce.
As a result of the face validation that was done with our respondents, we remove
4 Refer to Appendices (The Pool of 27 IS-Impact Measures)
5 The dictionary can be found at http://www.thefreedictionary.com
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Question 11 which measures business process improvement and Question 12 that
assesses the e-Government aspect. It was learned from organisational representatives
and from information published by the organisations, that all the systems they have
used were developed to prepare for e-Government (SPEKS and SAGA) or e-business
purposes. Thus, the items were removed in order to save our respondents‟ time. The
same decision was applied to the measure of business process improvement as the
representatives argued that this item is covered in Question 3 which determines
effectiveness increment. With respect to the raised issue, we refer to a dictionary3
which states that “the outcome of a well designed business process increased
effectiveness [value for the customer *Question 3] and increased efficiency [less costs
for the company *Questions 6 and 7]”. Therefore we came to the same conclusion by
taking out the measures out of respect for the respondents‟ efforts and their valuable
time.
In the IQ dimension measurement, we combine Questions 14, 15 and 17 into one item,
and simplify Questions 13 and 18 within one question. We conclude that the item that
assesses the output provided by the system shares the same meaning with the question
that determines whether the information from the system is concise. The word
“concise” in Question 18 relates to Question 13 that examines “the brief and to the
point output from the system”.
As for the SQ dimension, we identify the measures that have the same objective of
assessment which can be simplified in Malay translation and reduce the number of the
questions. This applies to: Question 20 that has the same implication as Question 24;
Question 19 that concerns the similar condition as Question 25; and Questions 21, 22
and 23. The meaning of meets “requirement” in Question 21 refers to “something that
is required, something demanded, required activity or a necessity” which also can be
identified by Questions 22 and 23. Question 26 was not asked in the survey instrument
as the fully integrated aspect is one of the main characteristics in identifying the
Enterprise Systems that have been employed by the sample organisations.
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Table 4.5: Summary of research constructs and measures
Construct Description Variable Names Sources
Passive
Integration
Organisation
(PIO)
The PIO is determined by
identifying how the
organisational structure
aligns with the nature of
tasks integrated by
employees. The construct is
also recognised by examining
sufficient scope of integration
either from complementary
or greater aspects.
All new measures: based on
literature and previous case
studies
Organizational structure:
Items: OrgStruct1, OrgStruct2
Complementary:Scope1
Greater: Scope2, Scope3
Cardon (2001),
Huang and Newell
(2003), Grant
(1996a,b), Pan et al.
(2007).
Active
Integration
Organisation
(AIO)
The AIO is obtained from
activities that gain common
knowledge among employees
and the frequency of
integration.
All new measures: based on
literature and previous case
studies
Common knowledge:
Items: CommK1, CommK2
Frequency:
Items: Freq1, Freq2, Freq3,
Freq4
Caya (2008), Cardon
(2001), Haddad
(2008), Huang
(1999), Huang and
Newell (2003), Grant
(1996a,b), Pan et al.
(2007).
Active
Integration
Individual (AII)
The AII relates to the
employees‟ flexibility of
integration which is
determined either from
aspects of extended or
reconfigured knowledge.
All new measures: based on
literature and previous case
studies
Extend: Flex1, Flex2:
Reconfigure: Flex3, Flex4:
Huang and Newell
(2003), Grant
(1996a,b), Pan et al.
(2007)
KI effectiveness
The degree of the KI
effectiveness is determined
through the employees‟
ability to combine, synthesise
and recognise others‟
knowledge domains.
4 measures (Tiwana 2001)
1 measure (Mehta 2006)
1 new measure: based on
literature
KI1, KI2, KI3, KI4, KI5, KI6
Mehta (2006),
Tiwana (2004),
Tiwana and McLean
(2005), Tiwana
(2001)
ES-knowledge
base
The degree of the goodness
of ES-knowledge base is
determined by the software
knowledge and business
process knowledge.
All new measures: Software
knowledge:KBs1, KBs2, KBs3,
KBs4
Business process knowledge:
KBbp1, KBbp2
Cardon (2001),
Davenport (1998),
Hong (2000), Ranniar
(2005)
ES-Success The success of the ES is
determined by individual
impact, organisation impact,
information quality and
system quality.
All measures from Gable et
al. (2008)
Individual impact: II1, II2, II3
Organization impact: OI1, OI2,
OI3
Information quality: IQ1, IQ2, IQ3
System quality: SQ1, SQ2, SQ3,
SQ4
Gable et al. (2008)
Table 4.5 summarises our survey instrument by providing the research model
constructs‟ descriptions, variables and the reference studies. The full instrument can be
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found in the Appendices. Since the assessment was gathered in Malay language
questionnaires, the presentation of our measures is believed to be pertinent and
sufficient as we aim to identify ES success in general, as a result of understanding the
impact of knowledge integration effectiveness on the ES success.
4.5.5 Survey Translation
The objective of this technique is to compare the translation with the original measures
in order to achieve the quality of translation. In this study, four translators were
involved and the back-translation was done by a different translator than the one who
did the forward translation. Since the survey instrument was conducted in Malaysian
organisations, and most of the measures in this research were adapted from established
scales in prior studies that were operationalised in English, a translation of the
questionnaire was necessary. The translation process was quite complex, with
difficulties arising in preserving the conceptual equivalence of the original measures.
Some of the issues arose due to the English word not simply translating to Malay
language because: (1) the word does not have an equivalent meaning, or (2) the word
requires explanation in a few words. To achieve conceptual similarity across English and
Malay languages, we use the back-translation technique (Behling and Law 2000). The
technique is an iterative process that involves the cycles of: (1) translating the English
questionnaire into the Malay language, (2) translating the Malay instrument back into
the English language by a person who has no knowledge of the original English wording
of the instrument, (3) comparing the original English questionnaire and back-translated
Malay version, and (4) if substantial differences exist between the two versions,
correcting the translation to eliminate the inconsistencies and to more accurately
reflect the intent of the wording in the English language version.
4.5.6 Research Sample Selection
A few criteria were determined to identify appropriate respondents and organisations
before gathering data for this research. First, we identified that the setting of our
sample must be a developing country, and we chose Malaysia. Due to our research
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objective to identify the impact of KI effectiveness on ES success, a developing country
is our target for data collection as the current trend of ES implementation is mainly
evident in these countries (Molla and Bhalla 2006). Literature has shown that ES
implementation faces additional challenges in developing countries in Asia and Latin
America compared to developed countries such as the US, Canada and the UK (Huang
and Palvia 2001) and many ES failures have been reported in developing country
settings (Rajapakse and Seddon 2005). ES implementation is less likely to succeed in
organisations in developing countries due to a lack of ES experience and low IT
maturity among employees (Lin and Rohm 2009). Therefore, we believe that gathering
data from one developing country is adequate to get a clear indicator of the
importance of KI effectiveness where many KI issues might still be unresolved.
Second, organisations in our sample must be sufficiently large. We determine the
organisations should have more than 500 employees (Blaxter 2006). We do not
consider small and medium enterprises as these types of organisations tend to not have
enough capital to implement the ES: as Chen (2009) reported, the cost of ES
implementation is typically huge, in the range of millions of US dollars.
Third, we scope our respondents to include the employees who serve at the
management level and in operational tasks only. These two groups of employees were
nominated since they are the direct users of the ES and are commonly the employees
who use the system very frequently. This was decided in order to recruit respondents
who are satisfactorily knowledgeable about the ES.
Lastly, we limit our respondents‟ length of experience using the ES to a minimum of 6
months. We set this as one of our criteria in order to ensure the quality of responses.
By having this limitation, we believe that our research data is sufficient and appropriate
as the data is sourced from experienced respondents who have adequate knowledge to
answer the questions.
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4.5.7 Sample Overview
Company A, a Cisco partner, operates throughout the Asia Pacific with service and
support centres in 38 locations staffed by more than 900 industry specialists. A has a
wholly-owned subsidiary who are training partners dedicated to the business of
networking, security, data centre and Microsoft solutions with advanced skills in
consulting, integrating, training and managing services for IT solutions for businesses.
The company established a standard operating environment across the company and
moved to a single instance of the SAP system several years ago in order to leverage a
central database. Large enterprises and mid-sized clients receive extensive support
from Company A in building and managing their IT infrastructure.
A popular automobile firm in Malaysia, Company B has implemented a comprehensive
solution for vehicle distributions, sales, after-sales and finance by applying SAP ERP and
SAP Solution Manager. The solution integrates manufacturing systems and enables
collaborations with its dealers and also government entities. In 2005, the company
launched the ES to streamline operations by replacing its legacy business software and
designing a big-bang initiative to replace existing systems all at once. With the ES,
processes in the company now operate with greater speed, accuracy and transparency,
cutting down on overheads while maintaining high levels of customer satisfaction.
Company C is an organisation entrusted to develop, operate and maintain the power
generation in Malaysia. This company has thermal generation assets and major hydro-
generation schemes in Peninsular Malaysia. The company has employed SAP/R3
solutions to integrate all customer-facing activities including collection and accounts
receivables. A total of RM32 million was spent on an ES solution from SAP which went
live in 2004 and is now used by 5200 of its staff throughout Malaysia.
Since January 2007, Company D, a provider of information communication technologies
has been running on a SAP system. This integrated tele-communication company and its
suppliers benefit from more efficient processing of business transactions by integrating
payments, inventory and asset register. The changes are in line with the company‟s goal
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to deliver efficient, cost-effective and timely services to all its customers and business
partners.
Besides collecting our data in four large and varied industry organisations, we also
gathered data from government agencies to balance our analysis. In this study, we
gathered data from two financial systems: the Standardized State Government
Electronic Accounting System (SPEKS) for state governments and the Standard
Accounting System for Government Agencies (SAGA) for federal government bodies.
The survey instrument was administered to employees in the financial department to
assess the impact of knowledge integration effectiveness on the success of the SPEKS at
state government agency E, and in the IT department to evaluate the achievement of
the SAGA at federal government agency F as a result of knowledge integration
effectiveness among their staff. The Accountant General Department has developed an
electronic financial records system for the use of federal and state public agencies
through the introduction of e-Government flagship applications in 1996. In line with the
government‟s vision for Malaysia to become a developed country by the year 2020,
many systems under the electronic government project have been developed (Hussin
et al. 2008).
The goal of the systems such as the SAGA and SPEKS is to produce financial
management that is standard, high quality and accurate within all government agencies.
For instance, SPEKS is an integrated accounting system developed specifically to
increase productivity, efficiency and financial accuracy and to prepare the state
government for the e-Government era. This web-based application was developed by
KJSB using Oracle technologies. It involved a complex business logic validation on both
client and server sides, and integration with the other custom developed system
architecture based on the Oracle 9i Application Server, Forms 6i and Report 6i
services. The system development lasted two years and was fully completed in 2004.
As significant as SPEKS, in 2007 there were 12 government agencies fully utilising the
SAGA system as part of the e-Government implementation. The SAGA, launched in
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early 1996, has two main components: an operational accounting system and an
accounting information system. Century Software Malaysia and the Accountant General
Office of Malaysia were appointed to implement and maintain the system. Table 4.6
below summarises our research sample.
Table 4.6: Summary of research sample
Organisation Description
Company A A Cisco partner; operates throughout the Asia
Pacific with service and support centres in 38
locations staffed by more than 900 industry
specialists; networking, security, data centre and
Microsoft solutions with advanced skills in
consulting, integrating, training and managing
services for IT solutions for businesses.
Company B A popular automobile firm in Malaysia; has
implemented a comprehensive solution for vehicle
distributions, sales, after-sales and finance by
applying SAP ERP and SAP Solution Manager.
Company C An organisation entrusted to develop, operate and
maintain the power generation in Malaysia; has
employed SAP/R3 solutions to integrate all
customer-facing activities including collection and
accounts receivables.
Company D A biggest provider of tele-communication services
and technologies in Malaysia; has been running on a
SAP system.
Company E A state government agency; uses the Standardised
State Government Electronic Accounting System
(SPEKS) – data gathered from financial (treasury)
department.
Company F A federal government agency; uses the Standard
Accounting System for Government Agencies
(SAGA) – data gathered from IT department.
As summarised in Table 4.6, Companies A, B, C and D are using a few versions of SAP,
while the other two companies (E and F) implement ES for e-government purposes.
SAP provides solutions that work across a range of implementations depending on the
kind of solutions that are truly needed by organisations. In principle, the SAP
functionality installed should include all the possible future situations that can emerge
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(based on the organisation‟s business objectives) as well as the organisation‟s current
needs. It is important to understand what solutions the company will need and what
planned solutions will fit those needs. For example, SAP R/3 is used to manage
functional systems such as human resources, sales and distribution and material
management.
Even though the data were gathered from different industries – namely, financial
services (from 2 organisations), networking (from 2 organisations) and IT (from 2
organisations) – the respondents from the selected departments shared common
characteristics that were necessary for the purpose of this study. These characteristics
include the frequent use of the ES and users with adequate experience.
4.5.8 Content Validation
Research can gather valuable information by conducting a content validity study.
Content validation is important to ensure that all individual items of the questionnaire
match the intended concepts sufficiently well (Sekaran 2000). Content validity refers to
the extent to which the items on a measure assess the same content or how well the
content material was sampled in the measure, which can be characterised as face
validity (Rubio et al. 2003). As far as content validity is concerned, and following Bollen
(1989) and Schouten et al. (2010), all the items that encompass the constructs in this
study result from: 1) a strong review of literature, and 2) face validity.
(a) Strong Literature
The greatest care has been taken to ensure that the study responds to the conceptual
definitions and that it reflects the relevant constructs in the literature. Theoretical
papers, including the references list of the papers, were reviewed to identify the
potential determinants and appropriate measures (Schouten et al. 2010) for our
research constructs. This procedure is important for measuring whether all relevant
aspects of the constructs are covered. The assessment of scale items should
thoroughly, adequately and appropriately represent the concept. Initially, we derived all
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measures from the literature, ensuring the study was strongly grounded in existing
theory. The questionnaire is considered to have content validity as its content matches
an actual situation that is being studied. The more the measure items represent the
domain of the construct, the higher is the scale‟s content validity (Tiwana 2001).
(b) Face Validation
We use face validation to examine the appropriateness of the questionnaire items‟
soundness, language and appearance. This is essential for validating our survey
instrument as to whether it looks valid to our respondents and whether the language is
appropriate to ensure all the questions meet the research intention and can be easily
understood by respondents6. Previous studies (Grant and Davis 1997; Lynn 1986;
Rubio et al. 2003) recommend a minimum three experts with a range of up to ten
experts depending on the desired diversity of knowledge. Prior to distribution of the
questionnaires in this study, the questions were discussed with five experts from our
sample organisations including a manager of business services and quality management,
assistant manager of network operation, senior manager, head administration officer
and technical supervisor. The experts from the sample organisations provided
constructive feedback about the quality of the newly developed and established
measures and the objective criteria to evaluate each item, and offered concrete
suggestions for improving the measures (Rubio et al. 2003). These knowledgeable
respondents helped to identify problems with wording or meaning, readability, ease of
response and content validity (Schouten et al. 2010).
4.5.9 The Survey Instrument Revision
The research constructs were revisited and re-evaluated based on the content,
purpose and wording for each question to follow the constructs‟ definitions and their
intended measures. Following the detailed review and feedback from the discussion
with the experts from our sample organisations, several questionnaire items were
6 Note that our survey instrument was developed in Malay language (national language) as we gathered
our sample from Malaysia.
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refined in terms of the wording and language used, some questions were recombined
as they conveyed the same meaning and objectives, and a few measures were removed
due to the rationale and significance to the sample organisations.
4.5.10 Survey Deployment
Fieldwork took place between August 2009 and November 2009. Six (6) large size
companies including industry and government agencies were contacted through phone
and e-mail to pursue agreements to carry out our data collection in their companies.
Face meetings also applied to some of those companies to further discuss data
collection procedures. Some organisations required several processes to be completed
as the information to be taken from the organisations would be considered
confidential. Therefore, the approval took longer than expected. The success of this
stage of the data collection relied on the right contacts among family members and
friends working in those organisations.
Data were collected from one state government agency in the east coast of Peninsular
Malaysia, and one large size private sector company from two branches (one from
company headquarters in Marang and one from a branch in Kuala Terengganu). Other
respondents were gathered from one federal government agency from the southern
part of Peninsular Malaysia and from three large size private sector organisations in the
capital city, Kuala Lumpur. All four corporate organisations have been using SAP for
their ES, while the government agencies have been using a customised ES which was
developed specifically for the government financial and accounting settings. To increase
the volume of responses, we provided small gifts to all respondents and offered some
incentives for questionnaire collectors in each organisation. However, we experienced
a delay in the collection process of almost three months which related to delays in the
formal approval from the organisations as some applied strict procedures. In addition,
respondents were from management and operational groups involved in busy activities
such as system upgrade applications, outstation assignments, ad-hoc meetings and
sponsored events.
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Prior to the distribution of the questionnaires, face-to-face discussions were conducted
with five knowledgeable respondents as the representatives of the organisations to help
suggest improvement to the content validity of our survey instrument (as explained in
Section 4.5.8.2 above). The survey questionnaires were distributed to three hundred
(300) respondents and we received one hundred and ninety-six (196) of the final
completed returned forms, giving a response rate of 65.3%. Each questionnaire
consisted of six (6) constructs and 44 questions. In order to prevent the risk that
answers may not be independent if questions in the same dimension are presented in
the related constructs, the study randomised question presentation, mixing them with
other items.
This study employed the survey-based approach for the managerial and operational
employment cohorts, as these two groups use the system frequently. Different
employment cohorts may have different views on the success of an ES. It was expected
that in seeking the views of operational staff and of managerial staff regarding ES
success, it would be found that their observations were different. The data analysis
procedures used to test the research hypotheses and the results are discussed in
Chapter 5.
4.6 RESPONDENT ANONYMITY AND CONFIDENTIALITY
An anonymous study is important to guarantee confidentiality so we promised not to
reveal the survey information to anyone and promised that nobody would be able to
identify who provided the data. For the purpose of follow-up, we appointed a
questionnaire collector in each organisation. As the questionnaires were handed out
personally, agreement on our collection schedule was made with the collectors. We
contacted the collectors and reminded them of the convenient return date as
previously agreed.
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4.7 SUMMARY
The methodology that has been selected in this research is believed to be the most
appropriate methodology for the research, as the research model was explored by a
thorough literature review, using a theoretical explanation of KBT, and the construct
measures were adapted from established scales in prior studies. Therefore, we believe
that the validity of our data is satisfactory, and that the data can contribute strong
research findings.
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CHAPTER 5: DATA
ANALYSIS, RESULTS
AND DISCUSSION
This chapter describes the quantitative analysis including empirical results and
hypotheses tests. The chapter is divided into four sections. The first part focuses on
descriptive statistics, while the second part provides detailed analysis of the
measurement research model. In the third section, the structural model including
nomological validity is explained under the heading “Hypotheses Testing”.
Subsequently, we conduct additional analysis to uncover the findings that are valuable
to this research and discuss the research findings.
5.1 DATA ANALYSIS DESIGN
The process of the interpretation and evaluation of findings, as illustrated in the
research design in Chapter 1, is expanded in more detail in Figure 5.1. The data analysis
design consists of five processes: data preparation, data description, model
measurement, hypotheses testing and discussion of findings. Statistical data analysis was
performed using the Statistical Package for the Social Science (SPSS) 16.0 and
nomological net analysis was implemented using smart partial least square (SmartPLS
2.0), which adopted the structural equation modelling (SEM) technique.
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Figure 5.1: Data analysis design
In the first process, we prepare our data to be ready for analysis. In this process, we
create our data file, enter the data and do the cleaning process. In step 2, we describe
what the data shows in a manageable form. In further detailed analysis, we measure the
model of our research by validating the constructs according to the available reflective
and formative tests. The tests were conducted using SPSS 16.0. In the next process, we
test all of the research hypotheses by adopting the SEM technique that was performed
with SmartPLS 2.0. Lastly, we discuss our conclusion with regard to the model and
hypotheses analysis.
5.2 DATA COLLECTION OVERVIEW
Data were collected from 196 ES users in managerial and operational groups who use
their organisation‟s ES daily. Six large organisations in Malaysia were involved. The
maximum organisation size was 29,000 employees, with 100 employees as the
minimum. Only organisations that have been implementing an ES were chosen. Staff
from managerial and operational groups who use the ES daily were selected as
respondents. In the first phase, the organisations were contacted via phone and e-mail
through either their manager, head of department or executive officer. This was done
prior to the questionnaire distribution to seek their approval for involvement in this
study. After obtaining the necessary approvals, meetings were arranged with the
organisations‟ representatives including managers, assistant managers, executives and
administration officers. The presentation of survey questions (including sentence
structure and clarification) was refined through a round of feedback from those staff to
Prepare data
Create data
file
Enter data
Clean data
Describe data
Characterise
sample
Assess
normality
data
Measure model
Test
reflective
constructs
Test
formative
constructs
Test hypotheses
Bootstrap
sample
Test
relationships
of constructs
Conduct
additional
test
&
Discuss
findings
1 2 3 4 5
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make the survey clear and understandable. Interviews were also conducted with key
staff from the organisations‟ managerial groups to better understand the ES utilisation
and their problems.
The questionnaires were then distributed to the selected 300 ES users from managerial
and operational groups in those organisations. The respondents were identified by the
organisations‟ representatives who were either human resource officers or managers.
To get a maximum commitment from the respondents, the questionnaires were
distributed personally to the respondents and company representatives. Reminders to
complete the survey were sent via phone and e-mail. Completed questionnaires were
collected at meetings with nominated collectors as previously arranged.
5.3 DATA PREPARATION
The number of completed questionnaires represented overall response rate of 65.3
percent which we consider to be a sufficient achievement. The data were prepared in
Microsoft Excel and then imported to SPSS for analysis. The data screening process
then took place in which we looked at whether all the survey questions were answered
and completed.
The survey data were carefully screened for unusual patterns, non-response bias and
outliers. The responses were reviewed to determine if the respondents were diligent in
completing the questionnaires. To examine non-response bias, the surveys of
respondents who gave the highest points to all the questions were removed after
comparisons with the overall survey positions. Of the 196 responses, 3 were not
completed properly, 2 were biased as they gave the highest score to all questions based
on their positions, and 3 were not serious as they gave the same neutral score to the
whole questionnaire, and thus were invalid. Thus, these 8 responses were excluded
from analyses. Removal of these responses left 188 useable surveys. The following
sections discuss the analyses in detail through four topics: descriptive statistics,
research model measurement, hypotheses testing, and discussion.
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5.4 DESCRIPTIVE STATISTICS
We use descriptive statistics to describe the basic features of our data. This section
outlines the demographic statistics through the classification of employment cohorts,
responses by ES solution type, the length of ES usage, working experience and the data
distribution. The intentions of the analysis are: 1) to demonstrate that our sample has
all appropriate cohorts to examine integration of knowledge across employment
cohorts; 2) to show that our sample sufficiently represents the ES users from private
and public sectors (note that all private organisations have used SAP solutions while the
public agencies have their own in-house ES development of SPEKS and SAGA systems);
and 3) to reveal that all our respondents have adequate knowledge of the ES, as
demonstrated by the relationship of knowledge and the length of ES usage. The
subsequent sections discuss the descriptive statistics in further detail.
5.4.1 Responses by Employment Cohort
Table 5.1 presents the employment cohort demographics of the respondents. The table
shows the proportion of the research respondents in the managerial and operational
groups. About 59.6% of the sample was obtained from the operational group, while
40.4% were gained from the managerial group. As the data were almost equally
obtained from management and operational employees, the respondents can be
assumed to be satisfactory for this research due to the typical frequency of ES use
among these groups of staff.
Table 5.1: Response rate by employment cohort
Employment cohort Frequency Percentage
Managerial 76 40.4%
Operational 112 59.6%
Total 188 100%
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5.4.2 Responses by ES Solution Type
The 188 survey responses were analysed by reference to the three different ES
solution types that were being used in the 6 companies. Respondents were categorised
by their use of SAP, SPEKS and SAGA. The statistics show that 47.9% of respondents
were using SAP, 28.2% were SPEKS users and 23.9% of the respondents were applying
the SAGA. The results are set out in Table 5.2 and simplified through graph format in
Figure 5.2.
Table 5.2: Response rate by ES solution
types
Enterprise
System Frequency Percentage
SAP 90 47.9%
SAGA 45 23.9%
SPEKS 53 28.2%
Total 188 100%
Of the three different types of ES, the SAP solution has been used by Companies A, B,
C and D. These are large-sized organisations from various business backgrounds
ranging from tele-communication, power supply, and information technology
consultation to the motor vehicles industry. While SAP was being employed by diverse
types of businesses, other survey responses were completed by employees who use ES
types that solely focus on government services, namely, SPEKS and SAGA. The SPEKS
and SAGA systems are used widely in governments across Malaysia for financial
functions. SPEKS is administered from the Treasury Department of one of Malaysia‟s
state governments. The respondents who were using SAGA work in a federal agency
under the Malaysian Ministry of Finance.
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5.4.3 Responses by Length of ES Usage
Table 5.3 and Figure 5.3 show the length of ES usage in our sample organisations. One
of our sample organisations has used the system within the last 3 years, two companies
have used the ES for 5 years, and the three others have employed the ES for more than
5 years.
Table 5.3: Length of ES usage
Company Year
Company A 5
Company B 5
Company C 6
Company D 3
Company E 8
Company F 8 0
1
2
3
4
5
6
7
8
9
Company A
Company B
Company C
Company D
Company E
Company F
Length of ES usage
Year
Figure 5.3: Length of ES usage
5.4.4 Responses by Length of Working Experience
Eighty-four respondents reported their length of working experience in their
companies to be within five years and below. This data contributes around 45% of the
sample. About 27% of the respondents counted their working experience in a range of
6 to 15 years. The other 28% were more experienced respondents, who have over 16
years of familiarity with their job environment. Table 5.4 shows the results and Figure
5.4 simplifies the description.
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Table 5.4: Working experience
Working
experience
Fre-
quency
Per-
centage
5 years and
below 84 44.7%
6 years to 15
years 51 27.1%
16 years and
above 53 28.2%
Total 188 100%
0 20 40 60 80 100
5 years and below
6 years to 15 years
16 years and above
Working Experience
Frequency
Figure 5.4: Length of working experience
This analysis concludes that while there were about 45% of the respondents who had
five years of working experience in their companies (the minimum reported years of
experience was 6 months), the majority of the data were obtained from employees
who were knowledgeable regarding their tasks and their companies‟ business processes
(55% of respondents who have more than five years of experience). Thus, the
respondents can be assumed to have adequate knowledge to respond to the
questionnaire effectively.
5.4.5 Mean and Standard Deviation
This section presents the descriptive statistical analysis to describe the characteristics
of the sample. The analysis was also used to test the violation of variables. Standard
deviation is the most common measure of statistical dispersion, measuring how widely
spread are the values in a data set. The purpose of a standard deviation is to express
on a standardised scale how different the actual data is from the expected
average value. If the data points are all close to the mean, then the standard deviation is
close to zero. If many data points are far from the mean, then the standard deviation is
far from zero. If all the data values are equal, then the standard deviation is zero. Table
5.5 shows the mean and standard deviation values for the individual measures.
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Table 5.5: Suitability of the measures
Measures N Mean
Standard
Deviation
Passive Integration of an Organisation
Organisation structure 1 188 5.82 .887
Organisation structure 2 188 5.43 1.104
Scope (complementary) 188 5.18 1.446
Scope (greater 1) 188 5.65 1.293
Scope (greater 2) 188 5.18 2.028
Active Integration of an Organisation
Common knowledge 1 188 4.82 1.432
Common knowledge 2 188 5.38 1.139
Frequency 1 188 5.23 1.044
Frequency 2 188 4.84 .992
Frequency 3 188 4.79 .973
Frequency 4 188 4.97 .994
Active Integration of the Individual
Flexibility 1 188 5.86 .875
Flexibility 2 188 5.69 .873
Flexibility 3 188 5.00 1.241
Flexibility 4 188 5.23 1.054
KI effectiveness
Knowledge integration 1 188 5.16 1.151
Knowledge integration 2 188 5.48 1.037
Knowledge integration 3 188 5.46 .983
Knowledge integration 4 188 5.40 .979
Knowledge integration 5 188 5.56 .993
Knowledge integration 6 188 5.62 .890
Knowledge integration 7 188 4.68 1.285
ES-knowledge base
Knowledge base (system 1) 188 4.78 1.225
Knowledge base (system 2) 188 4.51 1.199
Knowledge base (system 3) 188 5.40 .995
Knowledge base (system 4) 188 2.77 1.178
Knowledge base (business process 1) 188 5.14 1.097
Knowledge base (business process 2) 188 2.94 1.129
ES success
System quality 1 188 5.26 1.152
System quality 2 188 5.37 1.080
System quality 3 188 5.18 1.122
System quality 4 188 4.80 1.348
Information quality 1 188 5.39 .904
Information quality 2 188 5.36 .968
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Information quality 3 188 5.35 1.041
Individual impact 1 188 5.65 .874
Individual impact 2 188 5.52 .973
Individual impact 3 188 5.51 1.037
Organisational impact 1 188 5.15 1.134
Organisational impact 2 188 5.40 1.037
Organisational impact 3 188 5.51 .956
5.4.6 Data Distribution
To determine whether or not the research data is normally distributed, the normal
probability and scatterplot were examined. All points lie in a reasonably straight
diagonal line from the bottom left to top right. This suggests no major deviation from
normality. The scatterplot of standardised residuals also shows the same condition.
5.4.7 Statistical Analyses Overview
SPSS version 16.0 was used to validate the research model. The tests were descriptive
statistics and prediction for numerical outcomes or groups (regression, VIF, Cronbach‟s
alpha and factor analysis). The SmartPLS 2.0, a partial least square technique, was also
used to validate and test the structural model and research hypotheses. SmartPLS is a
recognised software application for path modelling with latent variables. This software
was used to determine relationships between the independent and dependent latent
variables, and to determine both direct and indirect path influences among all the latent
variables in a nomological network. As this study attempts to identify and explain the
antecedents and consequences in the research model, a combination analysis using SPSS
and SmartPLS was an appropriate technique. The following section presents the results
of the research model measurement.
5.5 RESEARCH MODEL MEASUREMENT
Further validation was done by measuring our research model using SmartPLS. This
measurement is used to describe how individual observed constructs load on the
research latent constructs (unobserved).
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5.5.1 Constructs-Measurements Relationships
Measures are known as observable indicators or items that are obtained through
empirical means (Edwards and Bagozzi 2000). Constructs are used to describe a
phenomenon that is observable or unobservable, including outcomes, structures,
behaviours or other aspects of a phenomenon being investigated (Petter et al. 2007).
The measures are used to examine constructs.
Relationships between constructs and measures need to be evaluated in addition to the
structural paths (Edwards and Bagozzi 2000). Because measurement error impacts on
the structural model, misspecification of constructs as formative or reflective affects
theory development and prohibits researchers from meaningfully testing theory due to
improper results (Petter et al. 2007). Formative and reflective indicator relationships
are relevant in a causal model (Hulland 1999). Reflective indicators or measures are
believed to reflect the unobserved, underlying construct, with the construct causing the
observed measures. In contrast, formative measures are defined as the cause of the
construct. Reliability and validity are an appropriate assessment for reflective measures.
However, this is not necessarily true for formative measures. In fact, formative
measures of the same construct can have positive, negative or no correlation with one
another (Bollen 1989; Hulland 1999).
5.5.2 Formative Constructs
Formative constructs are a composite of multiple measures (Petter et al. 2007) where
the changes in the formative measures will cause changes in the underlying construct
(Jarvis et al. 2003). Formative constructs are multidimensional constructs that capture
multiple dimensions. Internal consistency or reliability is unimportant because measures
are examining different facets of the construct. Instead, multicollinearity, which is
desired among measures for reflective constructs, is a problem for measures of
formative constructs (Jarvis et al. 2003). Multicollinearity is avoided by ensuring that the
items do not tap into the same aspects. The measures should not have strong
correlations with one another because this suggests multicollinearity (Petter et al.
2007). According to Jarvis et al. (2003), removing a measure that focuses on a distinct
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aspect of the construct to improve construct validity will adversely affect content
validity. The elimination of an item that is not duplicated elsewhere in the scale could
affect whether the construct is fully represented by the measures because the
construct is a composite of all the indicators (Petter et al. 2007).
5.5.3 Reflective Constructs
Internal consistency is important for reflective constructs. All of the items are
measuring the same phenomenon and if the value for one of the measures changes,
then all of the other values should move in the same direction. Therefore Cronbach‟s
alpha coefficient (Cronbach 1951) and other reliability measures (composite reliability)
are used to ensure the measures are reliable (Petter et al. 2007). Reflective measures
should be unidimensional in that all of the measurement items are measuring the same
aspect of the unobservable construct. Changes in the measures do not cause changes in
the construct; rather, changes in the constructs cause changes in the indicators. Thus,
individual measures can be removed to improve construct validity without affecting
content validity. To test construct validity in this study, factor analysis was conducted
on the data using the Principal Component Analysis (PCA) extraction method with
Varimax rotation.
5.5.4 Construct Validation
According to Bollen (1989), the common way to check the construct validity is to
validate its convergent and discriminant validity. It is critical to identify whether the
constructs that we use accurately measure the intended concepts before any
relationships can be tested. Convergent validity shows that the evaluation relates to
what it should theoretically relate to, and therefore whether the scales relate to the
items that could be correlated. The discriminant validity is the degree to which two or
more measurements designed to measure different theoretical constructs are not
correlated. This test estimates the degree to which a measurement scale reflects only
characteristics from the construct measured and not attributes from other constructs.
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To demonstrate the reliability and validity of the measurement scale, the study
undertook specific analyses using SPSS 16.0 and SmartPLS 2.0. The analyses include
running confirmatory factor analysis for each construct to verify that individual items
represent the same theoretical concept. The study tests the hypotheses of the
estimated model using path coefficient (correlation), effect size and R2, together with
statistical significance level from the bootstrapping procedure.
Construct validity for a formative construct can be tested using discriminant validity,
convergent validity, external validity and nomological validity. The discriminant validity
is used to test the expected possibility to discriminate between different constructs.
The inter-correlations of the model constructs should not be too high (under 0.71)
(Andreev et al. 2009). To establish the nomological validity, the nomological network
was used whereby the constructs were linked with hypothesised antecedents and
consequence constructs. Nomological validity is evidenced if the structural paths
between the latent variables are found to be significantly in the expected causality
directions (Andreev et al. 2009).
The concepts of reliability and construct validity are not meaningful when a formative
model is employed. Besides face and content validity, the validity of formative indicators
can be examined by theoretical explanation and nomological validity (Henseler et al.
2009).
5.5.5 Construct Reliability
The internal consistency of the formative construct was performed by a
multicollinearity test and test of indicator validity (path coefficient significance) (Petter
et al. 2007). Multicollinearity indicates that the specification of indicators was not
accomplished successfully, as high covariance might mean that indicators explain the
same aspect of the domain (Andreev et al. 2009). The magnitude of multicollinearity
can be examined by the variance of inflation factor (VIF) and the tolerance value, which
is reciprocal of the VIF. The value of VIF < 10 shows the absence of multicollinearity.
The significance of the path coefficients was statistically tested using a t-test. A test for
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coefficient significance and calculation of the t-statistic were performed by applying the
bootstrapping procedure.
5.5.6 Model Assessment Overview
The measurement model analyses the relationships between the latent constructs and
their associated items (Chin et al. 2008). In further investigation, the overall research
model was estimated by using the SmartPLS 2.0. SmartPLS 2.0 is a software application
for the modelling of SEM. To evaluate the partial least square (PLS) estimation, the
research follows the suggestions by Chin (1998) and Henseler et al. (2009). The five
research hypotheses (set out in Chapter 3) were tested by examining the magnitude
and significance of the structural paths in the PLS analyses and the percentage of the
variance explained in the constructs. In the research model, four constructs were
modelled as formative and two constructs were operationalised as reflective. The
constructs of Passive Integration of Organisation (PIO), Active Integration of
Organisation (AIO), ES-knowledge base and ES success were modelled as formative,
while Active Integration of Individual (AII) and KI effectiveness were reflective.
The research model was validated using confirmatory factor analysis based on
construct correlations (Gefen and Straub 2005). This technique describes and
summarises the data by grouping together variables that are correlated (Tabachnick
and Fidell 1996). Factor analysis can be used to verify our conceptualisation of a
construct of interest. It is very important to test our conceptualisation since the items
are new. The factor analysis can show if there are few factors as we predicted. One
type of factor analysis is Principal Component Analysis (PCA). The PCA looks at the
total of the variance that the solution generated, and will include as many factors as
there are variables.
Factor loadings can be rotated, to be either orthogonal or oblique. The best
orthogonal analytic rotation and the most widely accepted method is Kaiser‟s Varimax.
Rotated factor loadings are used for naming the obtained factors (components). The
Varimax rotation facilitates the interpretation of factors by increasing their variance and
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information content. The results indicated that all items had loadings above the
acceptable threshold of 0.70 and loaded highly on their theoretically assigned factor
(Gefen and Straub 2005).
5.5.7 Content Validity
Content validity is an important step to ensure the presented indicators capture the
entire scope of the construct as described by the domain of the construct (Andreev et
al. 2009). There is no measurement error for the formative structure, but it is essential
to minimise disturbance terms by identifying a broad set of indicators that cover all
aspects of the construct. Thus, a thorough literature review was conducted related to
the construct domain (Straub et al. 2004).
5.5.8 Multicollinearity Estimation for Formative Constructs
Assessment
As discussed earlier, multicollinearity exists when the independent variables are highly
correlated. The stronger the correlation, the larger the standard estimation error. This
will result in larger confidence intervals and the parameters for the independent
variables are more likely to be insignificant. Multicollinearity exposes the redundancy of
variables and the need to remove variables from the analysis. There are various ways to
obtain the multicollinearity. Some factors might come from improper use of variables
or inclusion of a variable that is computed by other variables in the equation. The
degree of multicollinearity among the formative indicators needs to be assessed by
calculating the variance of inflation factor values or the tolerance values.
The other multicollinearity assessment is the value of tolerance, a measure of
collinearity that is reported by SPSS. A small tolerance value indicates that the variable
under consideration is almost a perfect linear combination of the independent variables
in the equation. Tolerance is an indicator of how much of the variability of the specified
independent variable is not explained by the other independent variables in the
research model. If the value is less than 0.1 (close to zero), it should be investigated
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further. This is because this very small value indicates that the multiple correlation with
other variables is high, which suggests a possibility of multicollinearity (Pallant 2005).
Table 5.6 below shows the VIF values.
Table 5.6: Validity test for formative constructs
The Construct of Passive Integration of an Organisation
Items Tolerance VIF
Organisation structure 1 0.834 1.199
Organisation structure 2 0.866 1.155
Scope complementary 0.854 1.171
Scope greater 1 0.727 1.375
Scope greater 2 0.860 1.163
The Construct of Active Integration of an Organisation
Items Tolerance VIF
Common knowledge 1 0.809 1.236
Common knowledge 2 0.811 1.233
Frequency 1 0.546 1.832
Frequency 2 0.465 2.150
Frequency 3 0.343 2.912
Frequency 4 0.355 2.815
The Construct of ES-knowledge base
Items Tolerance VIF
Knowledge base (system 1) .540 1.851
Knowledge base (system 2) .544 1.838
Knowledge base (system 3) .533 1.877
Knowledge base (system 4) .543 1.842
Knowledge base (business process 1) .626 1.598
Knowledge base (business process 2) .634 1.576
The Construct of ES success
Items Tolerance VIF
System quality 1 .222 4.496
System quality 2 .182 5.485
System quality 3 .245 4.081
System quality 4 .564 1.773
Information quality 1 .450 2.224
Information quality 2 .359 2.783
Information quality 3 .294 3.047
Individual impact 1 .397 2.519
Individual impact 2 .166 6.008
Individual impact 3 .186 5.388
Organisational impact 1 .277 3.612
Organisational impact 2 .153 6.540
Organisational impact 3 .195 5.130
The VIF statistic was used to determine if the formative indicators were too highly
correlated. This is because, if the multicollinearity between the construct indicators is
too high, it can destabilise the research model (Roberts and Thatcher 2009). The
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maximum VIF value for the construct of passive organisation came to 1.375, which is
well below the threshold of 10, as suggested by the traditional rule of thumb. This
indicates that there is no threat to the validity in this construct. All the tolerance values
for this construct were close to 1.0, ranging from 0.727 to 0.866.
The VIF values for the construct of ES-knowledge base ranged from 1.576 to 1.877.
Thus, the measurement items of this formative construct are valid. The tolerance
values also suggest that there is no possibility of multicollinearity threats, as these are
all well situated above the cut-off value of 0.1.
Applying the threshold of 10, no serious collinearity problems can be identified with
regard to the ES success construct. Although the VIF values varied, they were still well
below the cut-off value of 10 with the maximum value being 6.540. For example, the
system quality component shows the VIF values from 1.773 to 5.485. The VIF values for
the information quality ranged between 2.224 to 3.047. The individual impact had a
maximum value of 6.008, while the organisational impact maximum VIF value was
slightly higher at 6.540, as set out above in Table 5.6. The tolerance values for all the
variables in the construct of ES success suggest no possibility of multicollinearity, with
all the values above the threshold of 0.1.
5.5.9 Reliability Test
This research does not analyse the reliability for the constructs of passive organisation,
active organisation, ES-knowledge base and ES success as these formative constructs
are not expected to be internally consistent (Bollen 1989; Roberts and Thatcher 2009).
5.5.10 Construct Validities and Reliabilities for Reflective
Constructs Assessment
The first analysis of the reflective constructs is to demonstrate whether the
measurement items are loaded appropriately on their respective constructs. The items
that show high factor loadings indicate the reliability of the items. Using the loadings
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from the constructs, Cronbach‟s alpha, composite reliabilities and average variance
extracted (AVE) were calculated for these reflective constructs, as set out in Table 5.7.
Table 5.7: Factor loadings, Cronbach‟s alpha, composite reliability and AVE
The Active Integration of the Individual Construct
Component
Cronbach‟s
alpha
Composite reliability AVE
1 2 0.721 0.826 0.545
Flexibility 1 .898
Flexibility 2 .764
Flexibility 3 .901
Flexibility 4 .669
The Knowledge Integration Effectiveness Construct
Knowledge integration 1 .742 0.892 0.918 0.652
Knowledge integration 2 .865
Knowledge integration 3 .862
Knowledge integration 4 .820
Knowledge integration 5 .833
Knowledge integration 6 .689
Extraction method: Principal Component Analysis
Rotation method: Varimax with Kaiser Normalization
Rotation converged in 3 iterations
5.5.11 Factor Analysis
For reflective constructs, factor scores for all measures were generated in SPSS by
performing factor analyses with principal components and Varimax rotations. To test
construct validity, factor analyses were conducted using the Principal Component
Analysis extraction method with Varimax rotation. Reliability was calculated for each
construct using Cronbach‟s alpha coefficient. This analysis can assess the convergent
and discriminant validity (Gudi 2009). All the measurement items with the same
construct should have high loadings on their component (convergent validity) and low
loadings on other factors (discriminant validity). This supports the measures‟ validity as
measurement items should be more highly correlated with their own scales than with
other scales.
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Convergent and discriminant validity were assessed at the individual level. The
measurements were first validated using confirmatory factor analysis. The results
indicated that all items were loaded above the acceptable threshold of .70, an indication
of convergent validity (Nunnally 1978). The results, including item loadings and
construct reliabilities, are summarised by construct in further discussion.
The components of the measurement items of the active individual construct were
verified based on the flexibility explanation by Grant (1996). The initial prediction was
supported and the rotated solution yielded two interpretable factors. Two factors
were rotated as a second order hierarchical structure which involved constructs more
than one dimension (Wetzels et al. 2009). This hierarchical construct is based on
theoretical grounds (Edwards 2001) of KBT (Grant 1996). The two reconfigure
indicators (Flexibility 1 and Flexibility 2) loaded together with loadings of .898 and .764,
while the two extend indicators loaded together with loadings of .901 and .669
(Flexibility 3 and Flexibility 4). For knowledge integration effectiveness, six items were
used to measure this scale: the highest correlation was 0.865 with 0.742 as its minimum
score.
5.5.12 Cronbach’s Alpha
To validate the reliability of the measures indicated for the constructs, Cronbach‟s
alpha technique was used. The purpose of performing the analysis for reliability is to
examine whether the measures consistently represent the construct that is being
measured (Green and Salkind 2005). Reliability was calculated for each group of items
of reflective constructs. Reliability measures the consistency among items for a given
construct. Cronbach‟s alpha coefficient is one of the most commonly used indicators of
internal consistency of a questionnaire, calculated using SPSS. This technique is the
average value obtained by computing the correlation coefficient every possible way, in
which the data set is split into two halves randomly. This is based on the idea of split
half reliability.
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Cronbach‟s alpha is the value of correlation between each item and the total score
from the questionnaire. This value provides the average of the reliability coefficients
one would obtain for all possible combinations of items. This is because the use of
individual items is particularly troubling. Single item reliabilities are generally low, and
without reliable items the validity of the item is poor at best and at worst unknown
(Gliem and Gliem 2003). In social science research, the value of .60 or above is
considered acceptable (Nunnally 1967; Robinson et al. 1991).
5.5.13 Composite Reliability
The composite reliability can be interpreted in the same way as Cronbach‟s alpha. It is a
more accurate internal consistency measure as it avoids the assumption of equal
weighting of items. Table 5.8 shows the reliability of the reflective constructs. For
further analysis, composite reliability analysis was conducted to indicate whether the
measurement items consistently represent the constructs that are being measured
(Green and Salkind 2005). As Cronbach‟s alpha tends to provide severe
underestimation of the internal consistency reliability of latent variables in PLS path
models, it is more appropriate to apply the composite reliability (Henseler et al. 2009).
According to Nunnally and Bernstein (1994), an internal consistency reliability value
above 0.7 is regarded as satisfactory. The results are all above the minimum 0.7, with
0.826 for active individual and 0.918 for knowledge integration effectiveness.
5.5.14 Average Variance Extracted
For the assessment of validity, two validity subtypes (convergent and discriminant) are
usually examined. The average variance extracted statistic is used to assess the
convergent validity. AVE also can be used to determine the discriminant validity
(Hulland 1999), which is the average variance shared among the constructs and
measures. An adequate discriminant validity of a construct should share more variance
with its measures than it shares with other constructs in the research model. An AVE
value of at least 0.5 indicates sufficient convergent validity (Chin 1998; Henseler et al.
2009). All the AVE values are higher than the recommended value of 0.5. This means
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that a latent variable is able to explain more than half of the variance of its indicators
on average. These AVE values provided clear evidence of convergent (Henseler et al.
2009) and discriminant (Hulland 1999) validity for the reflective constructs in the
research model. The results in Table 5.7 above show the validity of the active individual
and knowledge integration effectiveness constructs.
5.6 GRANT’S KBT MODEL EVALUATION
This section discusses the original model evaluation based on the constructs given by
KBT (Grant 1996). As this research uses Grant‟s constructs, Varimax rotation was
chosen to validate the factors that have been used. The resulting rotated factor matrix
will show the factor loadings, which are the correlations between each of the variables
and the factors selected for the rotation. This analysis can also be used to assess the
convergent and discriminant validity of the measurement items and the constructs
(Gudi 2009). The next section presents the results of factor analysis conducted for the
construct of efficiency that involves the components of organisational structure, common
knowledge and frequency.
5.6.1 Multicollinearity Estimation for Formative Constructs
As stated above, in practice, a VIF value that is larger than 10 (equivalently the
tolerance value is lower than 0.1: VIF = 1/tolerance) would indicate a critical level of
multicollinearity. High multicollinearity could mean that the indicator‟s information is
redundant. On the other hand, a very small tolerance value indicates that the multiple
correlation with other variables is high, which suggests a possibility of multicollinearity
(Pallant 2005). Multicollinearity can be an issue if the lower tolerance value is
accompanied by non-significance and large standard errors. Assessments of both
collinearity diagnostic factors are derived from the multiple regression procedure in
SPSS. Table 5.8 below shows the VIF values.
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Table 5.8: Validity test for formative constructs
The Construct of Efficiency
Items Tolerance VIF
Organisation structure 1 .883 1.132
Organisation structure 2 .812 1.232
Common knowledge 1 .788 1.269
Common knowledge 2 .781 1.281
Frequency 1 .529 1.889
Frequency 2 .426 2.165
Frequency 3 .334 2.991
Frequency 4 .354 2.821
The Construct of ES-knowledge base
Items Tolerance VIF
Knowledge base (system 1) .540 1.851
Knowledge base (system 2) .544 1.838
Knowledge base (system 3) .533 1.877
Knowledge base (system 4) .543 1.842
Knowledge base (business process 1) .626 1.598
Knowledge base (business process 2) .634 1.576
The Construct of ES success
Items Tolerance VIF
System quality 1 .222 4.496
System quality 2 .182 5.485
System quality 3 .245 4.081
System quality 4 .564 1.773
Information quality 1 .450 2.224
Information quality 2 .359 2.783
Information quality 3 .294 3.047
Individual impact 1 .397 2.519
Individual impact 2 .166 6.008
Individual impact 3 .186 5.388
Organisational impact 1 .277 3.612
Organisational impact 2 .153 6.540
Organisational impact 3 .195 5.130
As shown in Table 5.8, the maximum VIF value for the construct of efficiency came to
2.991, which is well below the threshold of 10. Thus, we satisfied that there is no
threat to the validity in this construct. All the tolerance values for the efficiency
construct were also well situated above the cut-off value of 0.1, ranging from 0.334 to
0.883.
The VIF values for the construct of ES-knowledge base ranged from 1.576 to 1.877,
which indicates that this formative construct are valid. The tolerance values also
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suggest that there is no possibility of multicollinearity threats, as all the values are close
to 1.
For the ES success construct, the VIF values were varied, but were still well below the
cut-off value of 10 with the maximum value being 6.540. For example, the system
quality component shows the VIF values ranging from 1.773 to 5.485. The VIF values
for the information quality ranged from 2.224 to 3.047. The individual impact had a
maximum value at 6.008, while the organisational impact maximum VIF value was
slightly higher (6.540 as set out in Table 5.8). The tolerance values also suggest no
possibility of multicollinearity, for the construct of ES success.
5.6.2 Reflective Constructs Assessment
Table 5.9 below shows the validity of the reflective constructs including the values of
factor loadings, Cronbach‟s alpha, composite reliability and AVE.
Table 5.9: Factor loadings, Cronbach‟s alpha, composite reliability and AVE
The Scope Construct
Component
Cronbach‟s
alpha
Composite reliability AVE
1 2 .493 0.752 0.513
Scope (greater 1) .881
Scope (greater 2) .936
Scope (complementary) .977
The Flexibility Construct
Flexibility 1 (reconfigure) .898 .705 0.826 0.545
Flexibility 2 (reconfigure) .764
Flexibility 3 (extend) .901
Flexibility 4 (extend) .669
The Knowledge Integration Effectiveness Construct
Knowledge integration 1 .744 .890 0.918 0.652
Knowledge integration 2 .899
Knowledge integration 3 .856
Knowledge integration 4 .819
Knowledge integration 5 .804
Knowledge integration 6 .701
Extraction method: Principal Component Analysis
Rotation method: Varimax with Kaiser Normalization
Rotation converged in 3 iterations
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5.6.3 Factor Analysis
Factor analysis is used to assess the interrelations among a set of measures, and we use
confirmatory factor analysis to test our pre-hypothesised constructs. As this research
uses constructs that are given by KBT (Grant 1996), Varimax rotation was chosen to
validate the factors that have been used. The resulting rotated factor matrix will show
the factor loadings, which are the correlations between each of the variables and the
factors selected for the rotation. This analysis can also be used to assess the
convergent and discriminant validity of the measurement items and the constructs
(Gudi 2009).
For the scope construct, two factors were determined according to the explanation
given in KBT. Two rotated solutions yielded two interpretable factors: greater and
complementary. The two greater indicators loaded together with loadings of .881 and
.936, while the complementary indicator loaded with loadings of .977.
Two factors were rotated for the components of the measurement items of the
flexibility construct. The two reconfigure indicators (Flexibility 1 and Flexibility 2)
loaded together with loadings of .898 and .764. The other two extend indicators also
loaded together with loadings of .901 and .669 (Flexibility 3 and Flexibility 4). The
highest correlation for knowledge integration effectiveness was 0.279 with 0.175 as the
minimum score. The results are summarised above in Table 5.9.
5.6.4 Cronbach’s Alpha
Cronbach‟s alpha is the average value of the reliability coefficients one would obtain for
all possible combinations of items. The scope construct indicated a slightly lower than
acceptable cut-off value (0.493 of 0.60). However, it is considerable due to a strong
theoretical rationale and is still used for this study to maintain consistency with the
theoretical explanation (Chapter 2).
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5.6.5 Composite Reliability
For further analysis, composite reliability analysis was conducted to indicate whether
the measurement items consistently represent the constructs that are being measured
(Green and Salkind 2005). As Cronbach‟s alpha tends to underestimate the internal
consistency reliability of latent variables in PLS path models, it is more appropriate to
apply the composite reliability (Henseler et al. 2009). The composite reliability can be
interpreted in the same way as Cronbach‟s alpha. It is a more accurate internal
consistency measure as it avoids the assumption of equal weighting of items. Table 5.9
above shows the reliability of the reflective constructs. As shown in table, all constructs
including scope meet the satisfactory values for judging the internal consistency
reliability from composite reliability using a threshold value of 0.7.
5.6.6 Average Variance Extracted
The average variance extracted statistic is used to assess the convergent validity. Table
5.9 shows that all the AVE values are higher than the recommended value of 0.5. This
indicates that all the latent variables are able to explain more than half of the variance
of its average indicators.
5.7 HYPOTHESES TESTING
5.7.1 Structural Research Model Assessment
The research model was analysed and interpreted using the PLS technique in two parts.
In the first part, the measurement research model (outer) was tested by performing
both validity and reliability analyses. The test examined (i) the reliability of composite
individual measures, known as composite reliability (CR); and (ii) the convergent
validity of the measures, AVE. The results of both the validity and reliability of the
outer research model are discussed in the previous sections.
In the second part, the structural model (inner) was tested by estimating the paths
between the constructs in the model to determine the significance as well as the
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predictive ability of the model. With the analysis of the measurement model completed,
the structural model of the relationships between the various latent constructs was
analysed. To determine the significance of the paths, the results of the bootstrapping
200 re-sampling technique was run in PLS. All the paths were significant, which
indicates that the research model is empirically confirmed by the data. Figure 5.5
displays the structural model for this research.
The individual path coefficients of the PLS structural model can be interpreted as
standardised beta coefficients of ordinary least square regressions. The structural paths
provide a partial empirical validation of the theoretically assumed relationships between
latent variables (Henseler et al. 2009). To determine the confidence intervals of the
path coefficients and statistical inference, the re-sampling technique of bootstrapping is
used (Tenenhaus et al. 2005). This research used the PLS technique to validate the
structural model and to test the hypothesised relationships as this procedure is able to
model latent construct conditions of small to medium sample sizes (Limayem et al.
2004). The result shows how well the measures relate to each construct and whether
the hypothesised relations as discussed in the previous sections are empirically true. It
also provides more accurate estimates of the paths among constructs that may be
biased when using a multiple regression technique. Tests of significance for all paths
were conducted using the bootstrap re-sampling method.
5.7.2 Bootstrapping Procedure
PLS estimates the path model for each bootstrap sample. The statistical significance of
the parameter estimates were determined by a bootstrapping procedure. The
bootstrap method has been used for assessing the performance of a regression model,
to predict error of the model, and allows assessment of the statistical significance of
the regressors (Austin and Tu 2004). The PLS results for all bootstrap samples provide
the mean value and standard error for each path model coefficient (Henseler et al.
2009). In this study, bootstrapping was used to create 200 sub-samples. T-values that
were obtained from the bootstrapping procedure correspond to various inner and
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outer model paths. The significant values then were calculated using the extracted T-
values.
In statistical hypotheses, the probability value (P-value) is used to decide whether the
research has enough evidence to accept the hypotheses that were supported by the
data. The P-value is a numerical measure of the statistical significance of a hypothesis
test. At all times, a two-tailed P-value was used in this research. A two-tailed P-value of
0.1 would mean that there is a 0.1 (or 10% chance) that the two sets come from the
same group. However, to be cautious, this research follows the tradition in science to
say that a P-value of 0.1 is not significant. The reason is, that if 0.1 was considered
significant, then 10% of all scientific findings would be false. Traditionally, a P-value that
is below 0.05 is accepted. A summary of the result is shown in Figure 5.5. All
significant paths are indicated with an asterisk (*) and straight bold lines.
**Significant at 0.01 level ***Significant at 0.001 level Insignificant path
Figure 5.5: Assessment of research model
In examining the impact of KI effectiveness on ES success, all hypotheses (H1, H2, H3,
H4 and H5) were found to be true. Based on Cohen‟s guidelines, effect size (f2) values
of 0.02 (R2=0.0196), 0.15 (R2=0.13), and 0.35 (R2=0.26) refer to a small, moderate and
large effect size respectively (Petter et al. 2007; Roberts and Thatcher 2009). Founded
on the research model in Figure 5.5, all the R2 values meet the criteria for a small to
KI effectiveness
(R2=0.480)
ES-K Base
(R2=0.436)
ES Success
(R2=0.317)
H4 (0.661***) t=7.035
H5 (0.520**) t=2.993
H1 (0.205***) t=3.243
H3 (0.440***) t=6.918
(0.062) t=0.271
H2 (0.199**) t=2.480
PIO
AIO
AII
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moderate effect size. Table 5.10 shows the details of the path coefficient for each
measure.
Table 5.10: Summary of measures and path coefficients
PIO
Measure OrgSt1 OrgSt2 ScopeCompl ScopeGreat1 ScopeGreat2
Path coefficient 0.263 0.544 0.045 0.595 -0.005
AIO
Measure CommK1 CommK2 Freq1 Freq2 Freq3 Freq4
Path coefficient 0.331 0.742 0.518 -0.085 0.177 0.898
AII
Measure Flex1 Flex2 Flex3 Flex4
Path coefficient 0.755 0.809 0.635 0.743
KI effectiveness
Measure KI1 KI2 KI3 KI4 KI5 KI6
Path coefficient 0.740 0.864 0.855 0.819 0.839 0.714
ES-knowledge base
Measure KBs1 KBs2 KBs3 KBs4 KBbp1 KBbp2
Path coefficient 0.228 0.147 0.470 -0.032 0.358 -0.244
ES success
Quality
Measure IQ1 IQ2 IQ3 SQ1 SQ2 SQ3 SQ4
Path coefficient -0.193 0.079 0.185 0.174 -0.034 0.235 0.034
Impact
Measure II1 II2 II3 OI1 OI2 OI3
Path coefficient 0.161 0.008 0.118 -0.084 0.612 -0.412
5.7.3 Research Hypotheses Examination
(a) Testing for the Antecedents of Knowledge Integration
Effectiveness
This section reports on the analysis of the antecedents for knowledge integration
effectiveness. As Figure 5.5 above illustrates, the KI effectiveness has three antecedent
constructs: Passive Integration of an Organisation (PIO), Active Integration of an
Organisation (AIO) and Active Integration of the Individual (AII). Detailed analyses of
these three antecedents are reported in this section. When analysing the antecedent
factors, knowledge integration effectiveness yielded R2 of 48%, and the three
antecedents were found to be significantly related with a significant value of less than
0.01. These three antecedents are further discussed in depth.
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Hypothesis 1
Hypothesis 1 suggests that the passive integration of an organisation has a positive
influence on knowledge integration effectiveness. Supporting the hypothesis, the
research model demonstrated a positive and significant influence of passive integration
on KI effectiveness (β=.205, T-value=3.243, p<0.001). The result shows that efficiency
of integration is significantly related to the effectiveness of knowledge integration. It is
consistent with the expectation of H1 (path coefficient=0.205). Hence, the influence of
passive integration does matter.
Hypothesis 2
In hypothesis 2, the active integration of an organisation has a positive influence on
knowledge integration effectiveness was fully supported (β=.199, T-value=2.480,
p<0.01). This result also explains that the active interaction among organisation
members is significantly related to the effectiveness of knowledge integration.
Hypothesis 3
The empirical evidence supports the research hypothesis 3, that the active integration
of individuals has a positive influence on knowledge integration effectiveness (β=.440,
T-value=6.918, p<0.001). The finding suggests that the active individual factor is also
related significantly with knowledge integration effectiveness.
Hypotheses 1, 2 and 3
The results of the PLS path model show that the active individual factor (path
coefficient=0.440) has a much stronger influence on the knowledge integration
effectiveness compared to the other constructs, passive integration (path
coefficient=0.205) and active integration (path coefficient=0.199).
In sum, the results provide evidence that the three antecedents, PIO, AIO and AII,
positively influenced the KI effectiveness. As predicted, all three constructs have a
positive and statistically significant influence on KI effectiveness (p<0.01). The findings
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suggest that the KI effectiveness can be explained by taking into account the influences
of passive and active organisational activities and also individual activities.
(b) Testing for the Consequences of Knowledge Integration
Effectiveness
This section presents the results of consequences for KI effectiveness. There are two
proposed consequences in this research: the KI makes an impact on the goodness of
the ES-knowledge base, which then leads to the success of Enterprise Systems. Using
these two constructs of consequences, the relationships among the constructs were
tested. The path coefficient between KI effectiveness and the goodness of ES-
knowledge base was positively significant at 0.001 (R2=0.436, β=0.661). The relationship
between the ES-knowledge base and the success of ES was also found to be positively
significant, with its path coefficient= 0.520 (R2=0.317, p<0.01). These two consequences
are further discussed in depth.
Hypothesis 4
In hypothesis 4, it was suggested that the KI effectiveness has a positive influence on
the goodness of ES-knowledge base. Empirical evidence supports the hypothesis. The
knowledge integration effectiveness had a highly significant positive influence on the ES-
knowledge base (β=.661, T-value=7.035, p<0.001). The P-value explains that the
effectiveness of knowledge integration is highly significant to the individuals‟ levels of
ES-knowledge base.
Hypothesis 5
Lastly, in hypothesis 5, it was predicted that the goodness of ES-knowledge base has a
positive influence on Enterprise System success. Empirical evidence fully supports the
hypothesis (β=.520, T-value=2.993, p<0.01). In line with the research hypothesis, the
result shows that the goodness of ES-knowledge base and ES success are related with
high significance.
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5.7.4 Direct Impact of KI Effectiveness on ES Success
To investigate whether the knowledge integration effectiveness has a direct impact on
the ES success, a further path coefficient was examined. As the data summarised in
Figure 5.5 shows, the direct impact of KI effectiveness on ES success was not significant
with very low correlation (β=.0.062, T-value=0.271, p=0.787>0.1). Thus, the direct
impact of knowledge integration effectiveness is not valid.
The evidence suggests that the knowledge integration effectiveness had a significant
influence on the ES-knowledge base that leads to ES success (β=.661, T-value=12.590,
p<0.001). However, the direct relationship between knowledge integration
effectiveness and ES success was not significantly supported by empirical evidence
(β=.0.062, T-value=0.293, p>0.1). This indicates that the knowledge integration
effectiveness does not directly influence the success of ES. This finding is consistent
with all the hypotheses of this research model, where all our research hypotheses (1 to
5) have been found to be valid.
Figure 5.6: Model without ES-knowledge base
The candidate then compared the research model with the other possible model,
which is without the ES-knowledge base construct. This model was tested to ascertain
whether the ES-knowledge base construct did possess some explanatory power. To
KI effectiveness
(R2=0.480)
ES Success
(R2=0.208)
(0.456***) t=7.703
(0.204***) t=3.228
(0.442***) t=6.997
(0.197**) t=2.761
PIO
AIO
AII
**Significant at 0.01 level ***Significant at 0.001 level Insignificant path
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test this, the construct of ES-knowledge base was removed from the research model
and the structural model was tested.
Figure 5.6 shows that each independent variable directly affected the final dependent
variable, ES success. The resulting model shows that the paths from the knowledge
integration effectiveness construct and the ES success were significant (β=.456, T-
value=7.703, p<0.001). However, the R square value only explained 20.8% of variance
of the ES success, which is a moderate effect size. In contrast, the predicted research
model (Figure 5.5) achieved a higher value of R square of 31.7%, which is a large effect
size that explains the success of ES. This R square drop in the explanatory power of the
model suggests that KI effectiveness, as well as being positively associated with ES-
knowledge base, has a direct and positive association with ES success. The mediation by
ES-knowledge base provides empirical support of our hypothesis that the ES success
relies on the level of staff‟s knowledge base of business and software knowledge of the
ES. The path coefficient value in this model also indicated a lower value (0.456)
compared to a stronger value (0.520) in the predicted research model by the fifth
hypothesis (β=.520, T-value=2.993, p<0.01).
5.7.5 Choice of the Best Model
The best model was then chosen based on a comparison between the two models. To
test the mediation effect, we use bootstrapping technique. Sobel test has been by far
the most commonly reported to test the mediation effect. However, bootstrapping is
replacing the more conservative Sobel test (Kenny 2011) and is becoming the most
popular method of testing mediation (Preacher and Hayes 2004; Shrout and Bolger
2002). Besides, the Sobel test is very conservative (MacKinnon et al. 1995) and so it has
very low power. As the Sobel test uses a normal approximation which presumes a
symmetric distribution, it falsely presumes symmetry which leads to a conservative test.
In contrast, the bootstrapping technique does not require the normality assumption to
be met and can be effectively utilised with smaller sample sizes.
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The results from the predicted research model (Figure 5.5) were significantly better
than the results obtained from the model without the ES-knowledge base construct
(Figure 5.6). Thus, by comparing the R square values for the dependent construct (ES
success), the research model in Figure 5.5 was chosen. A summary of the results is
shown in Table 5.11.
Table 5.11: Summary of hypotheses test
Hypothesis Hypothesised
effect
Supported Path
coefficient
H1: Passive organisation KI effectiveness + Yes 0.205
H2: Active organisation KI effectiveness + Yes 0.199
H3: Active individual KI effectiveness + Yes 0.440
H4: KI effectiveness ES-knowledge base + Yes 0.661
H5: ES-knowledge base ES success + Yes 0.520
5.7.6 Original Structural Model Assessment
This section discusses the findings by reference to the original explanation of influence
factors for KI effectiveness by Grant (1996). First, this study used PLS path modelling to
assess the hypothesised path model of the prediction research model. Then, the sum of
the direct effect and all indirect effects of one particular latent variable on another (the
total effect: ES success) were evaluated for further interpretation.
Figure 5.7: Original model
*Significant at 0.05 level **Significant at 0.01 level ***Significant at 0.001 level Insignificant path
KI effectiveness
(R2=0.475)
ES-K Base
(R2=0.436)
ES Success
(R2=0.317)
H4 (0.661***) t=13.082
H5 (0.520**) t=3.062
Efficiency H1 (0.266***) t=3.955
Scope
H3 (0.445***) t=6.760
Flexibility
(0.062) t=0.297
H2 (0.120*) t=2.174
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5.7.7 Relationships of the Constructs
The P-values for the correlation between constructs were calculated using a statistics
calculator website7. To run the tests, the T-value for each relationship was obtained
from the bootstrapping procedure that was run for the structural model by using
SmartPLS. The acquired P-value was then used to indicate whether the relationships of
the constructs were significantly or insignificantly correlated.
The efficiency of knowledge integration has a positive influence on knowledge
integration effectiveness construct was fully supported (β=.266, T-value=3.955,
p<0.001). The result shows that efficiency of integration is significantly related to the
effectiveness of knowledge integration.
The sufficient scope of knowledge integration has a positive influence on knowledge
integration effectiveness construct was fully supported (β=.120, T-value=2.174,
p<0.05). This result also explains that the sufficient scope of integration is significantly
related to the effectiveness of KI. It shows that there is a relationship between scope
and the effectiveness of KI, with 97% confidence intervals (P-value is 0.03). This means
that a probability of efficiency does not correlate with the KI effectiveness, as it is only
at 1.5% (1-p/2 with 2-tailed test). The empirical evidence of the flexibility of knowledge
integration has a positive influence on KI effectiveness construct (β=.445, T-
value=6.760, p<0.001) suggests that the flexibility factor is also related significantly with
the KI effectiveness.
It was suggested that the KI effectiveness has a positive influence on the goodness of
ES-knowledge base. Empirical evidence supports the hypothesis. The KI effectiveness
construct had a highly significant positive influence on the ES-knowledge base (β=.661,
T-value=13.082, p<0.001). The P-value explains that the effectiveness of KI is highly
significant regarding individuals‟ levels of ES-knowledge base.
7 http://www.danielsoper.com/statcalc/calc08.aspx
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It was also predicted that the goodness of ES-knowledge base has a positive influence
on the ES success. Empirical evidence fully supports the hypothesis (β=.520, T-
value=3.062, p<0.005). In line with the research hypothesis, the result shows that the
goodness of ES-knowledge base construct and the ES success are related with high
significance.
In line with the knowledge-based theory of the firm, it is indicated that efficiency, scope
and flexibility indicate significantly influenced the KI effectiveness, which leads to good
performance of the ES and ES success. The direct impact of KI effectiveness on the ES
success was not significant and the correlation was very low (β=.0.062, T-value=0.297,
p>0.1). Thus, the hypothesis regarding the direct impact of KI effectiveness is not valid.
The evidence suggests that the KI effectiveness had a significant influence on the ES-
knowledge base that leads to the ES success (β=.661, T-value=12.590, p<0.001).
However, the direct relationship between knowledge integration effectiveness and the
ES success was not significantly supported by the empirical evidence (β=.0.062, T-
value=0.293, p>0.1).
5.7.8 Conclusion
The findings of the research model show that all our hypothesised relationships are
valid. The restructure of the measures into three new relevant antecedents for the ES
context is significant and applicable. This evidence indicates that besides the influence
factors proposed by Grant (1996), our research model is also a useful framework and
of value for further research of KI in the ES context.
5.8 ADDITIONAL FINDINGS
Two phases of analyses were conducted in this study. First, the analysis examined the
overall data set of 188 respondents to test the research model as discussed in the
previous sections. For the second phase of analysis, the data were separated into
groups, containing responses from different employment cohorts and the different
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types of ES that they use. Details of these analyses are presented in the following
sections.
5.8.1 Managerial Group
In the additional analysis, the data were grouped based on the respondents‟
employment cohort. In the first step, responses from the managerial group were
analysed. The model was estimated using SmartPLS, as shown below in Figure 5.8.
Figure 5.8: Estimated paths for the managerial group with PLS
The result of the path coefficient analysis for the managerial group in Figure 5.8
revealed that „active individual‟ had a direct effect on KI effectiveness. With a high
positive influence path of 0.634, the relationship was significantly correlated at the
0.001 level (β=.634, T-value=5.712, p<0.001). The other constructs expressed a non-
significant correlation with KI effectiveness in the managerial group: active organisation
had a negative and insignificant relationship with KI effectiveness (β=-.128, T-
value=0.632, p=0.529>0.1), and passive organisation showed a P-value bigger than 0.1
(β=.050, T-value=0.442, p=0.659>0.1). The outcome of path coefficient analysis also
demonstrates that the KI effectiveness construct had a positive and significant influence
on the goodness of ES-knowledge base with a strong relationship value of 0.561
*Significant at 0.05 level **Significant at 0.01 level ***Significant at 0.001 level Insignificant path
KI effectiveness
(R2=0.539)
ES-K Base
(R2=0.315)
ES Success
(R2=0.436)
H4 (0.561**) t=2.743
H5 (0.418*) t=1.926
H1 (0.050) t=0.442
H3 (0.634***) t=5.712
(0.328) t=1.240
H2 (-0.128) t=0.632
PIO
AIO
AII
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(β=.561, T-value=2.743, p<0.01). The given value of R2 for ES-knowledge base was also
high, at 0.315. Confirming the research hypothesis, the goodness of ES-knowledge base
showed a positive and significant impact on the success of ES with 43.6% of ES success
variance (β=.418, T-value=1.926, p<0.05). As expected, the direct relationship between
KI effectiveness and ES success shows the correlation was insignificant with a level of
value bigger than 0.1 P-value (β=.328, T-value=1.240, p=0.218>0.1).
It is quite surprising that the reported path coefficients do not confirm to some of the
stated hypotheses for ES users in the managerial group. The knowledge integration
effectiveness was only supported by the active individual construct, where active
organisation was negatively and insignificantly related to the effectiveness of knowledge
integration, as stated above. The result also shows that „passive organisation‟ did not
significantly influence the effectiveness of knowledge integration for the managerial
group of ES users. However, the consequences constructs of KI effectiveness, including
the goodness of ES-knowledge base and the success of ES, were empirically supported
by the data.
5.8.2 Operational Group
The results as set out in Figure 5.9 show that all path loadings remained significant and
confirmed all research hypotheses for the operational group of ES users. The passive
organisation construct was found to be positively correlated with KI effectiveness, with
a significant level of value at 0.001 (β=.323, T-value=4.194, p<0.001). For the active
organisation construct, a positive and significant relationship was found with KI
effectiveness where the path coefficient was valued at 0.217 with 0.01 as the significant
level of value (β=.217, T-value=2.558, p<0.01). The active individual construct also had
a high significance and positive influence towards KI effectiveness with a strong path
coefficient of 0.412 (β=.412, T-value=5.383, p<0.001). These three antecedents‟
constructs influenced the effectiveness of knowledge integration for the operational
group of ES users at 57.4% of the effectiveness variance.
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Figure 5.9: Estimated paths for the operational group with PLS
The KI effectiveness construct had a strong relationship with the goodness of ES-
knowledge base with a 0.732 path coefficient value, showing that the effectiveness of KI
provided a positive and significant impact on the goodness of ES-knowledge base in the
operational group of ES users (β=.732, T-value=3.480, p<0.001). The results in Figure
5.9 also demonstrate that the goodness of ES-knowledge base provided a positive
impact on the ES success with 34.6% of ES success variance at 0.05 significance level
(β=.677, T-value=2.050, p<0.05). A direct relationship from KI effectiveness to ES
success was also tested, showing a negative relationship among them and an
insignificant value of impact (β=-.130, T-value=0.351, p>0.1, 0.726).
5.8.3 A Comparison between Managerial and Operational
Groups
Investigation of the similarities and differences between the managerial and operational
groups of ES users provided evidence that there was some inconsistency in the
influence factors regarding the effectiveness of knowledge integration. The findings for
the management group demonstrate that only the active individual construct
significantly influences KI effectiveness, while the operational group confirmed the
KI effectiveness
(R2=0.574) ES-K Base
(R2=0.536)
ES Success
(R2=0.346)
H4 (0.732***) t=3.480
H5 (0.677*) t=2.050
H1 (0.323***) t=4.194
H3 (0.412***) t=5.383
(-0.130) t=0.351
H2 (0.217**) t=2.558
PIO
AIO
AII
*Significant at 0.05 level **Significant at 0.01 level ***Significant at 0.001 level Insignificant path
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research hypotheses that there were three antecedents of KI effectiveness, namely,
passive and active organisation, and active individual.
Although there were differences in the influence factors of KI effectiveness, the findings
from these two groups of employment cohorts show that the KI effectiveness made a
positive and significant impact on the goodness of ES-knowledge base that leads to the
success of the ES. The KI effectiveness from the managerial group perspective
expressed an impact of 43.6% ES success variance, while the operational group showed
a slightly lower impact, with a variance of 34.6% of ES success. No significant direct
impact was reported from these two ES user groups for the relationship between KI
effectiveness and ES success.
5.8.4 SAP Users
Table 5.12 displays the estimated value of the paths for SAP users that was derived
from the data of companies A, B, C and D companies. Using the research model, the
passive organisation construct was found to be insignificantly related to the
effectiveness of KI, although the path coefficient value shows there was some
correlation between them (β=.129, T-value=1.231, p=0.221>0.1). The construct of
active organisation also revealed the same situation, that is, no significant relationship
(β=.227, T-value=1.170, p=0.245>0.1). In contrast, the active individual construct was
significant at p<0.001 (β=.469, T-value=4.386). This means that there was less than
0.1% chance that the relationship between active individual and KI effectiveness was
attributable to random error.
The path coefficient from KI effectiveness to the goodness of ES-knowledge base shows
a high correlation of 0.746 with a significant value at 0.001 (β=.746, T-value=12.497,
p<0.001). The impact from KI effectiveness to the goodness of ES-knowledge base was
also high, giving a value of 55.6% of its variance. The ES-knowledge base construct
influenced the ES success at 38.7% of the success variance, which was also found to be
relatively significant at below 0.1 (β=.534, T-value=1.830, p=0.07). Further investigation
was carried out into a direct relationship from KI effectiveness to the ES success
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construct. As predicted, the correlation was not significant as the path coefficient also
demonstrated a low association between them (β=.112, T-value=0.290, p=0.772>0.1).
Table 5.12 summarises the results.
Table 5.12: Estimated value of paths for SAP users
Construct Path
coefficient
T-value P-value R square
Passive organisationKI effectiveness 0.129 1.231 0.221>0.1 0.505
Active organisationKI effectiveness 0.227 1.170 0.245>0.1
Active individualKI effectiveness 0.469 4.386 <0.001
KI effectivenessES-knowledge base 0.746 12.497 <0.001 0.556
ES-knowledge baseES success 0.534 1.830 0.07<0.1 0.387
KI effectivenessES success 0.112 0.290 0.772>0.1
5.8.5 ES Standard for Government State and Agencies
This section explains the research findings for the state and government agency users
of an ES. The data were derived from SPEKS and SAGA users in state government and
federal agencies.
Table 5.13: Estimated value of paths for government users (SPEKS and SAGA)
Construct Path
coefficient
T-value P-value R square
Passive organisationKI effectiveness 0.305 3.809 <0.001 0.540
Active organisationKI effectiveness 0.249 2.460 <0.05
Active individualKI effectiveness 0.407 4.845 <0.001
KI effectivenessES-knowledge base 0.572 1.726 0.08<0.1 0.327
ES-knowledge baseES success 0.702 2.962 <0.01 0.452
KI effectivenessES success -0.054 0.190 >0.1
The table shows that there was a sign of a large correlation between KI effectiveness
and the goodness of ES-knowledge base for the ES government users. While the
significance level was indicated at only 0.08, which is a slightly higher value than 0.05,
the value was still below 0.1. Considering that the significance level explained that it will
certainly be mistaken in not more than 8% (less than a 10 in 100 chance of the result
being caused by random error), the research hypothesis for this connection was not
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rejected (Fisher 1956, pp. 41-42). Thus, this is considered to be relatively weighty
although the result was not strong enough to show a significant relationship between
these two constructs.
5.8.6 A Comparison between Users of SAP and Standard ES for
Government
The research found that there was some agreement and contradiction between SAP
users (package) and non-SAP users (customised for government use) regarding the
research hypotheses. For the SAP users, the impact of KI effectiveness on the goodness
of ES-knowledge base was quite large, and was found to be achieved at 55.6% of the ES-
knowledge base variance. The goodness of ES-knowledge base construct then caused
38.7% variance of the ES success. In contrast to the SAP users, the users of standard ES
for government state and federal agencies achieved better, with 45.2% of the ES
success variance from the users‟ goodness of ES-knowledge base.
Although the findings support the research hypotheses for the consequences of KI
effectiveness for the goodness of ES-knowledge base and ES success, the results for the
KI effectiveness antecedents show differently. As summarised above in Table 5.12, PIO
and AIO constructs were found to be insignificantly related to the KI effectiveness from
the SAP users‟ perspective, even though the R square of the KI effectiveness was more
than 50% of its variance. The only significant antecedent for this group of users was the
active individual construct, which shows an extremely significant correlation at p<0.001.
By comparison, users of the standard ES for government state and federal agencies
demonstrated significant correlations for all three proposed antecedents for the KI
effectiveness. Among these three constructs, the active individual construct was found
to be most significantly effective with p<0.001 and a path coefficient valued at 0.407. It
was followed by the constructs of passive organisation (β=.305, T-value=3.809,
p<0.001) and active organisation (β=.249, T-value=2.460, p<0.05), respectively.
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5.8.7 Length of Working Experience
This section explains the research findings based on the length of respondents‟ working
experience. The experience was grouped in two categories, with one group for the
respondents who had worked in their jobs for five years and less, and another group
was for those who had worked in their jobs for more than five years (six years and
above). Details of these two groups are discussed in the following sections.
5.8.8 Five Years and Below
For the respondents with experience of five years and less, all research hypotheses
constructs were found to be significant for a two-tailed test, except the construct of
active organisation (p=0.305>0.1). From two antecedents for KI effectiveness, the
active individual construct was found to be extremely significant at p<0.001 with a
strong path coefficient of 0.444. The passive organisation construct was also found to
be significantly related to the KI effectiveness at p<0.05 with 0.194 path coefficient
value. This suggests that the active individual construct is more highly significantly
associated with KI effectiveness rather than the passive organisation construct. In line
with the research hypotheses, the KI effectiveness for users who have a maximum of
five years working experience was found to be extremely significant to their goodness
of ES-knowledge base and gave 25.8% of the ES-knowledge base variance. The path
coefficient between them was also found to be strong with a value of 0.508. The impact
of the goodness of ES-knowledge base on the ES success for this group of users
achieved a strong value of R square at 55.1% of the ES success variance. The
correlation between ES success and the goodness of ES-knowledge base was
established to be extremely significant at p<0.001 with a strong path coefficient of
0.720. However, a direct relationship from KI effectiveness to ES success was found to
be insignificant. The data is summarised in Table 5.14.
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Table 5.14: Estimated value of paths for respondents with work experience of 5
years and less
Construct Path
coefficient
T-value P-value R square
Passive organisationKI effectiveness 0.194 2.488 <0.05 0.438
Active organisationKI effectiveness 0.159 1.032 0.305>0.1
Active individualKI effectiveness 0.444 4.467 <0.001
KI effectivenessES-knowledge base 0.508 4.337 <0.001 0.258
ES-knowledge baseES success 0.720 5.288 <0.001 0.551
KI effectivenessES success 0.041 0.224 0.823>0.1
5.8.9 Six Years and Above
Table 5.15 shows details of the estimated value of paths for respondents who had six
years and more working experience. In line with the research hypotheses, all
constructs were found to be correlated significantly. All antecedent constructs for KI
effectiveness contributed 55.8% of KI effectiveness variance. The strongest path
coefficient was found for the relationship between active individual and KI effectiveness
with a value of 0.416 and extremely significant correlation at p<0.001, followed by the
active organisation and passive organisation constructs. The impact of KI effectiveness
on the goodness of ES-knowledge base for this group of users also produced a high
contribution of more than 50% of the ES-knowledge base variance. The path coefficient
between these two constructs was also found to be extremely significant (p<0.001)
with a strong value of correlation at 0.733. The goodness of ES-knowledge base for this
experienced group of users also made a high impact on the ES success (R2=0.450) with
a significant and strong relationship at 0.873 path coefficient value. Aligned with the
research prediction, a direct relationship between KI effectiveness and ES success was
also found to be insignificantly correlated. Details of the results are shown in Table
5.15.
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Table 5.15: Estimated value of paths for respondents with work experience of 6
years and above
Construct Path
coefficient
T-value P-value R square
Passive organisationKI effectiveness 0.242 2.513 <0.05 0.558
Active organisationKI effectiveness 0.272 2.567 <0.05
Active individualKI effectiveness 0.416 4.719 <0.001
KI effectivenessES-knowledge base 0.733 13.662 <0.001 0.538
ES-knowledge baseES success 0.873 2.423 <0.05 0.450
KI effectivenessES success 0.326 0.752 0.453>0.1
5.8.10 A Comparison between Groups based on Length of
Experience
The findings show that the users who have not had more than five years of experience
had more impact on the success of the ES (55.1% of ES success variance) compared to
the users who had six or more years of experience in their job (45% of ES success
variance). However, the more experienced users made a better contribution to the
goodness of ES-knowledge base (53.8% of ES-knowledge base variance). In contrast, the
lesser experienced group of users contributed only 23.8% of their goodness of ES-
knowledge base in consequence of KI effectiveness. Although the level of ES-knowledge
base goodness was higher in the more experienced user group, the results show that
this group represented a lower percentage of the ES success (45%) compared to the
lesser experienced user group (55.1% of the ES success variance). This may suggest
that the novice users gave more commitment to the ES success compared to the more
experienced users. However, the more experienced users produced a better level of
ES-knowledge base goodness compared to the novice users.
5.9 DISCUSSION OF THE RESEARCH FINDINGS
This section discusses the findings for the research model. Some results of interest
were revealed for the three antecedents of KI effectiveness of demographic groups
based on employment cohorts (managerial and operational employees), types of ES
product according to vendor selected (SAP product and ES product for government:
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SPEKS and SAGA), and experience level (5 years and less, and 6 years and above). This
analysis identifies a few differences among respondents from these groups. Important
differences among these groups of respondents have been identified to form a basis for
future research. Details are discussed in the next sections.
5.9.1 Discussion 1: Overall Research Model Findings
Three analyses were involved in the research model evaluation. First, we examined the
three antecedents of KI effectiveness, consisting of passive integration of an
organisation (PIO), active integration of an organisation (AIO) and active integration of
the individual (AII) constructs. Second, we tested the relationship between KI
effectiveness and the goodness of ES-knowledge base. Lastly, we assessed the
connection between the goodness of ES-knowledge base and the ES success.
The three antecedents of KI effectiveness were tested, involving three hypotheses with
each hypothesis representing each relationship. The first hypothesis posited that
passive integration of organisation is positively correlated with the effectiveness of KI
among employees. The research findings evidenced that this hypothesis is true, as the
relationship between the PIO and the KI effectiveness was found to be associated
positively and significantly. This positive and significant relationship implies that clear
organisational structure of hierarchy in terms of staff position roles and sufficient scope
of knowledge are able to effectively integrate the ES knowledge held by the employees.
The second research hypothesis was also found to be true. The significant correlation
between active integration of organisation and the effectiveness of KI means that the
level of employees‟ common knowledge and the frequency of training, meetings and
feedback regarding the ES among employees are the notable issues that need to be
addressed by organisations to make the KI effective.
The third research hypothesis proposed that the active integration of individuals, which
includes creativity and flexibility among employees in extending and re-configuring their
ES knowledge, makes an important contribution towards more effective KI.
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Among these three antecedents, AII appeared to be the most influential factor for the
KI effectiveness, as evidenced by the strong and statistically significant and positive
coefficient of these constructs‟ interaction. This is then followed by the PIO and AIO
constructs, respectively. It seems reasonable that individuals make more contribution
to the KI practices in organisations as they are the ones who use the ES, and who
exactly know what knowledge they need to utilise the ES at an optimum level.
Organisations also make a contribution to the KI practices for their employees, but
ultimately, it depends on their employees to decide the best way to integrate their ES
knowledge during their work. It is, however, quite surprising that the AIO construct,
which includes the level of common knowledge of employees and the frequency of task
performance by gaining knowledge through repetition of training, discussions, meetings
and feedback, made the least contribution to the effectiveness of KI.
Meanwhile, organisational structure and scope of integration from the PIO construct
made a higher impact on the KI effectiveness. First, by synthesising the length of the ES
application in organisations in the sample with a minimum of three years, it becomes
clear that employees are still developing their understanding of the system. Their level
of common knowledge regarding the use of the ES is still growing. Thus, employees
tend to appreciate a clear organisational structure with clear roles and decision rights
regarding the ES (organisation structure mean score=5.63), and the integrated scope
within and outside their department to develop their common basic ES knowledge
(wider scope mean score=5.42). Second, considering the nature of the working
environment of the sample, employees‟ working culture might be one of the reasons.
Grant (1996) explains that organisational structure relies heavily upon rules and
directives, and these two mechanisms facilitate knowledge integration. We assume that
the respondents in our sample have a tendency to depend more on their superiors to
advise them on what activities should be undertaken.
The result of the level of importance among these three constructs suggests that even
though organisations had organised training courses, meetings, discussions (either
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formal or informal) to give and receive feedback about the system, it was individuals‟
approach towards exploiting ES knowledge that most strongly influenced the
effectiveness of knowledge integration.
We then examined the relationships between KI effectiveness and the goodness of ES-
knowledge base and the ES success. We found that the effective KI practices among
employees generate more than 40% of the employees‟ goodness of ES-knowledge base.
It is of interest to see that the research findings revealed a very strong correlation
between KI effectiveness and the goodness of ES-knowledge base. This suggests that by
having more effective KI among employees, the higher level of the goodness of ES-
knowledge base is likely to be gained by employees. The connection between the
goodness of ES-knowledge base and the success of ES was then evaluated, and it was
found that employees‟ level of ES-knowledge base contributed about 32% of the
success of ES overall. The employees‟ goodness of ES-knowledge base was found to be
significantly and highly correlated with the success of ES in organisations.
As expected, we could not find a significant correlation in the direct relationship
between KI effectiveness and the ES success. This could be due to the reason that KI is
a kind of action. The integration activity must provide an effect to employees. Thus,
employees‟ knowledge of the ES increases as a result of the integration process, which
we refer to as their goodness of ES-knowledge base. When their level of ES-knowledge
base improves, they will feel more comfortable with the ES, which then increases the
ES performance. Hence, the results offer an explanation for the essential role of the ES-
knowledge base among employees in order to have better performance of ES in
organisations.
The results shown in Figure 5.5 confirm that there is a positive and significant
correlation between each construct in the research hypotheses. Interestingly, none of
the constructs correlated insignificantly, which supports all of our research hypotheses.
Thus, the research is able to reach the expected conclusion that the three antecedents
are sufficient to determine the effectiveness of KI, and the KI effectiveness is
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competent to produce better levels of ES-knowledge base, and lead to the success of
ES as its consequence.
5.9.2 Discussion 2: Antecedents Only Represent 50% of KI
Effectiveness
The antecedents‟ constructs were found to significantly influence KI effectiveness, with
the antecedents explaining almost 50% of the KI effectiveness variance. However,
another half was not explained well by the theory for the collected data. This could be
due to several reasons.
First, one of the main reasons is that it might be caused by the contributions of
components in the antecedents of KI effectiveness. The KBT (Grant 1996) explains that
greater scope of integration may result in the lower level of common knowledge. This
is because the need for a sufficient level of common knowledge may not be fulfilled
when the scope of integration widens (Huang and Newell 2003). To have an effective
KI on the ES, it is important for employees to have a sufficient level of common
understanding of the system. If the quality of knowledge among employees is poor, KI
may become a barrier for performance of a task (Hustad 2007). Organisations with a
wide range of expertise might have a lower level of common knowledge, which could
impede the KI effectiveness by causing misunderstandings or conflicts. In this situation,
to utilise expertise and learning to an optimal level, frequency of integration ought to
be increased to allow the continuity of interactions among employees. In addition,
Krogh (2009) found that frequency of integration capability should be employed to
increase the scope and flexibility, and this is achieved in his three studied organisations.
This is very significant to organisations with greater scope and lower level of common
knowledge among employees.
Based on our findings, we strongly concur with the KBT explanation and previous
studies, as our mean score for greater scope in the PIO construct shows a value of
5.42, while the level of ES common knowledge mean score is 5.1. Having these mean
scores, we are able to say that the greater scope of integrating various sources of
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expertise among employees caused an obstacle in gaining a sufficient level of common
ES understanding in the AIO construct. Thus, to achieve KI effectiveness, the frequency
of integration must be higher.
By having an adequate frequency of integration among employees, the sufficient level of
common knowledge among them can be achieved. However, in our findings, the mean
score for frequency of integration in the AIO construct only shows the value of 4.96.
The frequency value can be considered low, while the value of scope is higher. Since
the scope of integration is wider, the low frequency of integration among employees
may cause misunderstanding and difficulties in integrating the know-how of ES across
the staff. Given that other components provide satisfactory values (organisation
structure in PIO construct=5.63, complementary knowledge in PIO construct=5.18 and
AII=5.45), we believe that the above factors contribute a plausible reason for our KI
effectiveness result. Therefore, the KI effectiveness only explains 50% of its variance in
our sample.
Second, according to sampling theory from the statistical point of view, the larger the
sample size, the smaller the sampling errors tend to be. Thus, to increase the variance
of KI effectiveness, we suggest that the sample size should be increased for future
work.
Third, we believe that our measurement items could be improved as this is one of the
earliest empirical studies that has looked into the antecedents of knowledge integration
effectiveness and that was founded on KBT. The research has tried to define the
constructs as precisely as possible by drawing on the theoretical explanations and the
relevant literature. The research also closely links the measures to the theoretical
viewpoint through a careful process of creating and amending the measurement items.
It is evidenced from the findings that the research positively and significantly supports
all the research hypotheses. However, the measurement items are far from being fully
perfect in assessing the research constructs. Measures might not completely represent
the respondents‟ real situation. The organisations may have different practices to those
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that were investigated in the questionnaire. For example, measures based on the
practicality of job rotation, training, meetings and feedback mechanisms might not be
well suited to the organisations‟ current processes. Thus, replication of the study might
be a good step to better understanding the issues based on whether this kind of result
is still the same in the future studies.
5.9.3 Discussion 3: Managerial versus Operational Users
To have more understanding on the research model, we include additional analysis by
comparing the results of employees in the managerial group and the operational group.
Detailed findings are discussed as follows.
(a) Managerial Users
Employees from the managerial group revealed that the PIO, AIO and AII influenced
the effectiveness of KI by about 54% of the effective variance. Their view on their
goodness of ES-knowledge base was as high as 32%, to be a result of the impact of KI
effectiveness. The findings on the ES success evidenced that 44% of the system success
was delivered from the goodness of ES-knowledge base according to the managerial
users‟ perspectives.
Empirical evidence showed that the influential factors coming from the organisation
(passive organisation and active organisation) do not correlate significantly with the KI
effectiveness. However, the results evidenced that the active individual construct
influences KI effectiveness with extremely significance at p<0.001, suggesting that
employees who have more flexible ways to integrate ES knowledge in operationalising
the system contribute more to KI effectiveness. The relationship between KI
effectiveness and the goodness of ES-knowledge base also correlated significantly with a
strong path coefficient. The connection between the goodness of ES-knowledge base
and the ES success also gave significant correlation with a sound path coefficient value.
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(b) Operational Users
Compared to the managerial group, empirical evidence from the operational group
showed that all the research hypotheses were correlated significantly. The antecedents
of KI effectiveness contributed almost 60% of the KI effectiveness variance. This
amount of contribution is considered large and is a convincing finding.
The level of ES-knowledge base goodness also demonstrated a high percentage of
contribution to the KI effectiveness, with more than 50% of its variance. As a
consequence, the ES success was found to be impacted at 35% of its variance. This is
followed with a high path coefficient value for relationships from KI effectiveness to the
goodness of ES-knowledge base, and from the goodness of ES-knowledge base to the
ES success. Thus, this research is able to reach the expected conclusion that all the
antecedents‟ constructs of KI effectiveness are valid, and the KI effectiveness is able to
create a greater level of employees‟ ES-knowledge base and the organisations‟ ES
success.
(c) Conclusion
The findings on the KI effectiveness being influenced by the PIO, AIO and AII
constructs showed the same significance in the managerial (54%) and operational
groups (more than 57%). Employees from the operational group delivered better value
for the ES-knowledge base (54%) compared to the managerial group (32%). In contrast,
employees from the managerial group tended to contribute more to ES success
performance (44%), than did respondents from the operational group (35%). The
results show that managers give much more positive value to ES success, possibly for
the reasons that they have been exposed to the system more frequently (Lin and Rohm
2009), and have attended more first-hand training programs from the ES vendor.
Therefore, managers are more satisfied with the ES than are the operational staff.
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5.9.4 Discussion 4: SAP Product versus non-SAP System
(a) SAP Product
Among the three antecedents that were proposed to influence the KI effectiveness,
only one construct, AII, was found to be significant in increasing the effectiveness of KI
at p<0.001. The path coefficient of its interaction was positive and significant. Although
the relationships for both passive organisation and active organisation constructs with
the KI effectiveness were positively signed, their insignificance is deduced as these two
antecedents to the integration effectiveness for SAP users were relatively rigid.
(b) Non-SAP product
All three antecedents, PIO, AIO and AII, were found to be significantly and positively
correlated to the KI effectiveness. This result was analysed in relation to ES users of
the SPEKS and SAGA systems that were designed specifically for government purposes.
We found that the relationship between KI effectiveness and the goodness of ES-
knowledge base shows significance and is positively signed. A similar result was also
found for the relationship between the goodness of ES-knowledge base and the ES
success. Regarding the findings, we conclude that all research hypotheses were
supported by the data collected for non-SAP product users.
(c) Conclusion
The analysis revealed that there were significant differences such as knowledge base
and ES success, between the SAP product and non-SAP system (SPEKS and SAGA).
Responses from SAP users contributed more than 50% of KI effectiveness, which was
slightly lower compared to the SPEKS and SAGA users (54%). However, views on the
impact of KI effectiveness on the goodness of ES-knowledge base were dissimilar
among the users of these two types of ES products. Our analysis shows that the SAP
product produced a better level of ES-knowledge base among their users with 56%,
while the non-SAP product only contributed about 33% of the ES-knowledge base.
Surprisingly, respondents who employed an ES that was designed specifically for
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government use (SPEKS and SAGA) tended to produce better levels of ES success
(more than 45%), than did respondents who used ES provided by a SAP vendor (39%).
The findings provide three issues to be discussed. First, the SAP product produced a
better level of ES-knowledge base among their users compared to the users who use
the non-SAP system although the value of KI effectiveness for SAP users is lower than
for non-SAP users. The main reason is the knowledge of ES provided by the SAP
vendor to the system users after ES implementation. The involvement of professional
experts from SAP in transferring ES knowledge to employees may be considered the
main factor for their goodness of ES-knowledge base. This is supported by our findings
which revealed the contributions of KI effectiveness for SAP users are ranked from AII
as the highest, followed by AIO and PIO respectively.
Besides the importance of having flexibility of integration in the AII construct, training
and meetings in AIO were the second most important factor for them. Hence,
sufficient training and meetings from the SAP experts can offer a better level of
goodness of ES-knowledge base. Comparing the non-SAP system users where the
system is specifically designed for government purposes, the ES knowledge is probably
not as high as the knowledge provided by the SAP vendor.
In addition, besides the contribution from the AII construct, a higher contribution to
the level of KI effectiveness among the government users was made by the PIO
construct rather than the AIO construct (the contributions were ranked from AII as
the highest, followed by PIO and AIO constructs respectively). The result indicates that
the ES users in the government sector relied more on good structure and outline of
roles and decision authority to effectively integrate their ES knowledge rather than on
training and meetings. This is aligned with the study of Pee and Kankanhalli (2008)
which found that public organisations (in this research we refer to the government
sector) have a centralised organisational structure with regard to the arrangement of
superiors and subordinates. Having less concentration on the frequency of integrating
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ES knowledge from trainings, meetings and other communications might be the reason
for the lower level of their ES-knowledge base compared to the SAP users.
Second, SAP users and non-SAP users view the ES success differently. The ES
performance percentage from the SAP users‟ perspective was found to be 39% of ES
success variance, while non-SAP users revealed better profit with more than 45% of ES
success variance. Non-SAP products were analysed from two different systems (SPEKS
and SAGA), both of which were developed to cater to the specific needs and business
processes of the government sector. Therefore, the level of understanding and
familiarity of the system was much better among users. As a result, this contributes to
a higher level of ES success. Non-SAP users‟ comments below may well support the
argument:
“The system is easy to use, but still can be improved.”
“This system is very suitable to use. I suggest that training courses should
be open to all new staffs.”
In contrast, systems that were developed by SAP vendors were found to be too
complicated. A lot of unnecessary forms were included in the package, but some other
functions were not being used by the companies as there was no requirement to use
them. The following opinions were put forward by the users:
“The system is sophisticated but not fulfil users requirement. The required
report cannot be provided by the system.”
“It will be better if the system can be maintained and customised internally,
at least to have control on it.”
“In my opinion, this system is not easy to use to complete my daily tasks.
There are too many procedures to be followed.”
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“Need a lot of time to learn and to understand the system. Skills and
knowledge are gained from repetition use.”
“Limitation on the system access due to rental charge.”
“System should be revised for better user friendly.”
The SAP vendor provides training to the key employees in organisations. In practice,
the „key employees‟ usually refers to management staff. These staff then will transfer
their ES knowledge to other employees through formal training. The transition
knowledge from SAP vendor to management staff and from management staff to other
employees may cause a number of conflicts including misinterpretation,
misunderstanding and a lack of ES knowledge. This might be one of the factors
explaining why the value of SAP users‟ view regarding the level of ES success was lower
than our expectation.
Third, the findings might reflect the length factor of the ES usage in organisations. The
companies that operationalise the SAP solutions have implemented the ES for a
maximum of 6 years and a minimum of 3 years (Companies A and B=5 years, Company
C=6 years and Company D=3 years). In contrast, SPEKS and SAGA have been fully
implemented for the last 8 years, with the implementation of their ES taking place from
1996. The longer duration of ES usage in government bodies E and F provides an
advantage to them in achieving better success in their ES. Consistent with our
assumption, the result shows that the longer the organisations operationalise the ES,
the more familiar the staff become with the ES and the more comfortable they feel with
the system. As a consequence, the ES performance is better.
5.9.5 Discussion 5: Length of Working Experience
Analysis was derived solely on the length of work experience among employees in the
organisations. The aim was to maximise the degree of comparability in performing the
research analysis to stabilise the research model.
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(a) Experience of 5 Years and Less
The antecedents of KI effectiveness were significant and positive only for two
constructs: PIO and AII. Although the interaction between the constructs of AIO and
the KI effectiveness was positive, it did not achieve significance. This suggests that the
factor of AIO was not relatively important to increase the probability of KI
effectiveness.
We then examined the impact of KI effectiveness on the goodness of ES-knowledge
base. The result shows that there was a strong and significant relationship among the
constructs. A highly significant and positive relationship between the goodness of ES-
knowledge base and the ES success was also found for the ES users who have a
maximum five years of experience.
The antecedent constructs contributed 44% of the effective knowledge integration
variance. The KI effectiveness influenced about 26% of the goodness of ES-knowledge
base for this group of employees, and the ES success produced more than 55% of the
success variance.
(b) Experience of 6 Years and Above
All research hypotheses for the constructs and their relationships were found to be
significantly correlated. The path coefficients from KI effectiveness to the ES-knowledge
base appeared to be highly related and the same result was found with regard to the
relationship between ES-knowledge base and the ES success, which were extremely
connected.
The analysis shows that the antecedent constructs produced almost 56% of the KI
effectiveness. The knowledge integration effectiveness has impacted about 54% on the
goodness of ES-knowledge base for this experienced group. The employees contribute
45% of the ES success of variance.
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(c) Conclusion
Compared to the novice workers, the group of more experienced staff contributed
better percentages of KI effectiveness with 55.8%, a higher achievement of the
goodness of ES-knowledge base at 53.8%, but made less contribution to the level of ES
success.
Employees who have less working experience (5 years and below) are more likely to
generate more ES success percentage than did respondents with more experience (6
years and above). We expect that integration practices of ES knowledge are likely to be
less problematic for novice workers. An influential management is capable of pushing
them towards such integration of implementation. Therefore, the KI practices are
expected to be less conflictual.
Experienced workers produce a stable pattern of interactions that contributes to the
implementation of ES knowledge integration due to their better level of ES-knowledge
base compared to the novice workers. They work in an environment where any new
system implementation or new policies from management of organisations are less
likely to affect them. Given the high level of knowledge that they have, this group of
workers are often found to be resistant to any changes in work processes or systems.
This may result in more conflict, in terms of knowledge interactions, limited ES
knowledge integration, which thus reduces the integration effectiveness. While novice
workers tend to adopt new ES knowledge, the experienced workers remain satisfied
with their current work activities. Consequently, experienced workers provide a more
stable ES-knowledge base that is gained from their skill in their work, while
simultaneously decreasing the level of ES success.
5.9.6 Discussion 6: Multi-industry Sample
Regarding the generalisation of the results of this study, the finding is robust because
the data were collected from a multi-industry sample. However, this research only
used a sample of Malaysian organisations for data analysis, and as such, caution should
be taken in generalising the results. No specific reason points to the fact that nationality
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might bias the results in a predictable direction. However, to prove this assumption and
generalise the findings, this research should be applied to other countries. This is
because the limited data source may weaken the generalisability of the research findings
in geographical settings.
5.9.7 Discussion 7: Multiple Cohorts’ Sample
The research respondents were obtained from managerial and operational groups of
employment. From the percentage that was calculated, the sample distribution between
these two groups was almost similar, at around 40% from the managerial group and
53% from the operational group. As the sample consists of an approximately balanced
number of these two cohorts, there is less potential for problems relating to bias.
From the research point of view of ES utilisation, the sample has answered the
research questions about the impact of KI effectiveness on the goodness of ES-
knowledge base by employees, and how much the goodness of ES-knowledge base
influences the ES success.
Due to the research focus, data did not cover the strategic group of staff in an
organisation. The strategic group was assumed to not be frequently using the ES
compared to the managerial and operational employees. As the research focus is on
the ES utilisation, it is believed to be sufficient for the research data to be gathered
from these two groups only.
However, the data sample may miss some important issues that are not captured from
the research. We may have limited ability to identify problems that may arise from the
impact of the strategic group on the ES success from the KI effectiveness perspective.
In addition, to understand how the KI impacts on the ES performance, the research
pilot case study is subject to managers‟ views only. It did not capture the operational
employees‟ perspective in ES utilisation. Thus, further investigation needs to be
undertaken to generalise the implications of KI effectiveness from the viewpoint of
strategic, managerial and operational employment cohorts.
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5.9.8 Discussion 8: Experienced Sample
All respondents were employees who have at least six months of working experience,
with the maximum years of experience reported being 32 years. Based on the sample,
55% of the responses were gathered from employees who have more than five years of
experience (six years and above). More accurately, 28.2% of the respondents were
employees with over sixteen years of familiarity with their work. This gives evidence
that the research data is sufficient and appropriate as it is sourced from experienced
respondents who have adequate knowledge to answer the questionnaires.
5.10 SUMMARY
Three antecedents, namely, passive integration of an organisation (PIO), active
integration of an organisation (AIO) and active integration of the individual (AII), were
found to significantly influence the effectiveness of KI. The KI effectiveness brought
significant impacts to the goodness of individuals‟ ES-knowledge base and the success of
the ES. The three antecedents of the KI effectiveness tested in the research model
explained almost 50% of the KI effectiveness variance. The model also reveals that
more than 40% of the goodness of ES-knowledge base was explained by the KI
effectiveness. In addition, over 30% of the variance in ES success was found to be
impacted by the ES-knowledge base.
All the research hypotheses were supported positively and significantly by the research
findings. Regarding the first hypothesis, the evidence demonstrated that the PIO is
positively and significantly correlated to KI effectiveness. In line with KBT, this result
proved that organisational structure and scope of integration elements do influence the
effectiveness of KI among employees. Our second hypothesis was answered by findings
that show the AIO construct is positively related to effective KI with a significant
connection. This construct is represented by the level of common knowledge of
employees and the frequency of integration among them. The third hypothesis referring
to the flexibility of integration in the AII construct was also supported by results
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showing a positive, significant and important relation to KI effectiveness. The
consequences of KI also empirically supported through the fourth and fifth hypotheses.
This gives evidence that all constructs of antecedents and consequences for KI were
relevant and valid. This chapter outlined aspects of the findings that open up new
questions, and discussed these aspects in detail. The contributions and implications of
the research and future works are discussed in the next chapter.
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CHAPTER 6:
RELATED WORKS,
CONTRIBUTIONS,
LIMITATIONS AND
FUTURE WORKS
This chapter discusses the research related works, contributions, limitations, future
works and conclusion. The research identifies the significant impact of knowledge
integration (KI) on the success of an Enterprise System (ES), with a particular focus on
employees‟ knowledge in operationalising the ES in the post-implementation phase. The
research can be categorised into two main parts: the antecedents of KI effectiveness,
and the consequences of KI effectiveness.
The research addresses two main questions regarding the KI. First, what are the salient
antecedents of KI effectiveness in the ES context? Second, what are the consequences
of KI effectiveness for the ES success? To answer these questions, we consider the
phenomenon of ES post-implementation through the lens of the knowledge-based
theory of the firm (KBT) by Grant (1996) and its core concept of KI.
By proposing the significance of KI in ES operationalisation, our main argument is that
the ES success is highly related to ES users‟ ability to combine others‟ knowledge about
how to operationalise the ES. In light of that, we developed a research model that
captures three antecedents and two consequences of KI effectiveness.
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More precisely, this research provides a logical link between the KI perspectives
proposed by Grant and the improved ES performance that is identified from the IS-
impact measurement model (Gable et al. 2008). Since knowledge is argued to be critical
and is an important foundation for the success of an ES (Gable 2005; Lengnick-Hall and
Lengnick-Hall 2006; Sedera and Gable 2010; Wang et al. 2007; Vandaie 2008), the main
objective of this research is to complement previous studies by emphasising the impact
of KI effectiveness on ES success. Extending the previous studies, the research
proposes that KI among employees in relation to operationalisation of the ES is an
issue that should be addressed by organisations for the better performance of their
systems.
6.1 RELATED WORKS
For more than a decade, the concept of knowledge has received substantial attention in
both research and industry sectors. Previous studies indicated a 90% ES failure rate
(Momoh et al. 2010; Zabjek et al. 2009), with the high failure rate of the ES becoming a
major concern of organisations (Dey et al. 2010; Scott and Vessey 2002; Strong and
Volkoff 2010) given that large investments have been made. In parallel with the huge
number of ES failure studies, many researchers have suggested critical success factors
for ES implementation (Al-Mashari et al. 2003; Dey et al. 2010, Mandal and
Gunasekaran 2003; Umble et al. 2003). A wealth of research suggests knowledge
management is a critical success factor for Enterprise Systems (Lee and Lee 2000; Pan
et al. 2007; Volkoff et al. 2004). While many prior research studies focus on KM as a
critical success factor for the ES (Sedera and Gable 2010), much work has focused on
the pre-implementation phase of the ES, and very little has been concerned with post-
implementation even though this phase is crucial for ES success (Scott 2005).
Our research can be seen as a continuation of recent work in the last few years. There
are some connections and differences between our work and previous studies. Prior
research identifies the „knowledge gap‟ between stakeholders as one of the key reasons
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for the lack of ES benefits in organisations (Soh et al. 2000; Pan et al. 2007). It has also
been suggested that this gap of knowledge can be minimised (or eliminated) by
„integrating‟ the knowledge held by employees in an organisation (Huang and Newell
2003; Newell et al. 2004). The KBT of Grant (1996) argues that an organisation‟s
performance is related to how effectively the organisation integrates knowledge. Revilla
and Curry (2008) concur that one of the major barriers to achieving effective
integration is the way an organisation integrates knowledge. In agreement with Grant,
and in response to the gap evident in other studies, we examined the impact of KI
effectiveness on ES success (Gable et al. 2008) in ES post-implementation in an
approach that employed the KBT explanation.
While prior research focuses on the antecedent of KI from the RBV perspective
(Tiwana 2004; Tiwana and McLean 2005), this research concentrates on the
antecedents of KI effectiveness from the KBT point of view. Adaptation of the KBT has
been done in a few studies examining KI in their particular research context. For
example, Hustad (2007) built a framework upon Grant‟s theory of efficiency, flexibility
and scope and investigated KI in distributed networks through an illustrative case study.
Huang and Newell (2003) also applied the case study approach to increase their
understanding of KI in cross-functional projects. These prior research studies employed
qualitative observation to understand the KI effectiveness. In recent work, Caya (2008)
used the core concept of KI through the lens of KBT (Grant 1996), and concentrates
on the KI effectiveness impacts through quantitative examination but leaves many
important key factors of KI effectiveness unexplored. Although some research studies
have operationalised the KI (Caya 2008; Mehta 2006; Tiwana 2004; Tiwana and McLean
2005), the use of KBT in empirically examining the entire factors of KI effectiveness in
an ES has never been operationalised before. We therefore sought to fill these gaps in
previous research.
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6.2 CONTRIBUTIONS
We discuss the contribution of this research in two parts: firstly, by explaining our
contribution in the context of theory, and secondly, by discussing and justifying our
contribution to practice. Details of these contributions are provided in the following
sub-sections.
6.2.1 Contributions to Theory
(a) Contribution 1: Adding value to the theoretical concept
Many organisations struggle to maximise the return of their ES investments. One of the
ES critical success factors, the gap of knowledge has important practical consequences
because employees are often obliged to employ an ES without a solid understanding of
the ES goals, benefits and weaknesses.
Proposing KI effectiveness as one of the critical factors for ES success, this research
aimed for a better understanding of the relationship between KI effectiveness and ES
success. The KI effectiveness measurement rests on a foundation of previous theory
proposed by Grant (1996). First, this research makes a contribution by shifting the KI
paradigm in the KBT viewpoint from a pure theoretical level to a more operationally
oriented and empirically testable ground by deriving a set of specific measures that can
be used to quantify the constructs of KI effectiveness antecedents in ES context.
We note that previous researchers (Huang and Newell 2003; Hustad 2007) studied the
influence factors of KI by providing case study evidence, while Caya (2008) empirically
introduced one aspect of common knowledge for KI effectiveness. Others have
adopted different approaches to study KI (Mehta 2006; Tiwana 2004; Tiwana and
McLean 2005) in different contexts such as e-business and project teams. Therefore,
this research offers the first empirical assessment to operationalise the entire influence
factors proposed by the KBT of Grant in the context of ES. The research results
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further clarify and emphasise the important role of KI effectiveness in relation to the
development of the ES-knowledge base goodness and the ES success.
Second, this research is a complement to the prominent theoretical exposition of KBT
by Grant and widens other studies (Caya 2008; Huang and Newell 2003; Hustad 2007;
Tiwana 2001) that cover the influence factors of effective KI. Following the theoretical
explanation of Grant, we propose our research model (refer to Chapter 3) to capture
the most critical influence factors to explore KI effectiveness in the ES post-
implementation phase. The KBT is particularly relevant to operation ES (Bendoly and
Jacobs 2005), but it is largely ignored by previous studies. In order to extend the
concept of KBT in ES, we identify three salient antecedents for KI effectiveness in the
research context, namely, passive integration of organisation (PIO), active integration of
organisation (AIO), and active integration of individual (AII).
Indeed, the findings suggest that the three antecedents of KI effectiveness are valid in
shaping the efficiency of integration practice among employees in relation to
operationalisation of the ES. This promising analysis is not unexpected, as integration
activities naturally take place inside organisational and individual frameworks (Aladwani
2001; Munkvold 2008; Okhuysen and Eisenhardt 2002), in the form of passive and
active elements.
Third, the research proposes two important impacts of KI effectiveness in the ES post-
implementation phase. Prior studies have investigated the role of KI in other stages of
the ES implementation process. For instance, previous researchers have focused on e-
business teams (Tiwana 2001), distributed networks (Hustad 2007), and virtual teams
(Caya 2008). In addition, in ES research, the extant literature shows that KI assessment
has mainly focused on the implementation stage (such as the ES project implementation
phase) (Huang and Newell 2003; Pan et al. 2007) but neglect the post-implementation
stage and how successful the ES usage. However, numerous studies report ES failures
(Scott and Vessey 2002; Zabjek et al. 2009), with businesses continuing to lose billions
of dollars annually (Zhang et al. 2005) after implementing an ES. Organisational benefits
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from the ES continue to be unpredictable (Strong and Volkoff 2010), without doubt
caused by a lack of eligible skills and ES-related knowledge among employees (Zabjek et
al. 2009) as a result of which a number of issues have arisen such as erroneous data
input, poor use of the ES and employee resistance (Momoh et al. 2010).
We provide an insight into the ways benefits for an organisation can be increased after
the ES implementation. According to our findings, we argue that the consequences of
KI effectiveness in the ES go-live stage are twofold: 1) boosting the goodness level of
individuals‟ ES-knowledge base, and 2) improving the level of ES success in
organisations. Importantly, the suggested consequences of KI effectiveness were
strongly supported by our empirical evidence. The findings show the ES success and the
goodness of individuals‟ ES-knowledge base were generated accordingly from the
effectiveness of employees in integrating their ES knowledge. Therefore, these two
constructs are concluded to be the important impact factors, so that organisations
should be more focused on the issue of integrating ES knowledge among their
employees. The evidence of the empirical analysis for these two constructs enabled the
research to answer the main research question of the KI effectiveness impact on ES
success, as discussed in previous chapters.
(b) Contribution 2: Proposing understandable antecedents for KI
effectiveness for ES
This research complements the prominent theoretical background of KI. The factors of
KI effectiveness were originally proposed by Grant (1996) as a way of understanding
and explaining the dynamic capability of KI in organisations. However, besides the
original structure of Grant‟s factors for KI effectiveness of efficiency, scope and
flexibility (as discussed in Chapters 2 and 5), we believe that there is a better way to
represent the antecedents of KI effectiveness in the ES context. The arrangement of
influence factors for KI effectiveness is not a straightforward representation of the ES
context. Even if they can be understood, we believe they are potentially difficult to
interpret. This makes it difficult for the non-expert reader to understand. For example,
the efficiency construct represents an organisational structure, common knowledge of
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employees and the frequency of integration. Thus, to assist the non-expert reader to
easily understand Grant‟s theory of KI specifically in the ES context, this research
proposes new and understandable constructs, namely, passive integration of an
organisation (PIO), active integration of an organisation (AIO) and active integration of
the individual (AII).
Our exploration of the nature of ES operationalisation in an organisation‟s working
environment brings a new understandable structure of the constructs. This involves
proposing new constructs and re-organising the measures that belong to the newly
named constructs of PIO, AIO and AII. The restructuring of measures into new
constructs for KI effectiveness antecedents offers a new contribution to the research
area. As our purpose is to assist non-experts to understand the research model more
easily, we introduce rigorous constructs and re-arrange the measure components of
Grant (1996) in necessary places to achieve a better design.
Our research model offers two new main perspectives in identifying the antecedents of
KI effectiveness in an ES: individual and organisational knowledge, and passive and active
KI practices. The two main perspectives are then grouped into three constructs
(passive integration of organisation; active integration of organisation; and active
integration of individual) that we represent as the antecedents of KI effectiveness. We
assess the validity of the KBT of Grant‟s theoretical view by carefully applying all the
factors in the new constructs. Each unique measure created from the factors of
influence was then directed to these three constructs as antecedents for KI
effectiveness in a more readable and understandable form. We found that all
constructs were relevant, as the given constructs were found to be correlated
significantly to the KI effectiveness (as set out in Chapter 5).
The advantage of grouping the KI antecedents by the three factors is that it is easier to
understand the issues associated with integrating knowledge for ES by approaching it
from the perspective of passive and active individuals and organisations. We believe
that our new constructs present a more readable research model, and also consider
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that this is a useful approach as it reflects the reality of knowledge in the ES context (as
described in Chapter 3).
(c) Contribution 3: Restructuring the antecedents of KI measures for
ES
The restructured measurement of the new constructs rests on a foundation of the
theory proposed by Grant (1996). It is anchored to a main theoretical perspective of
dynamic capabilities from KBT regarding the influence factors for KI effectiveness. To
be more understandable, the measurement components were re-organised so that the
new constructs consist of specific content that covers all the components proposed by
the KBT.
First, we identify that organisational structure and the integration scope in
organisations belong to the PIO construct. In this construct, we keep the component of
organisational structure, and move the frequency component to another construct. We
include the measure of scope component in this PIO construct which was previously
referred to by its own name. Given the components explained by Grant (1996) about
the efficiency and scope constructs, we believe that it is best to categorise scope by
reference to the passive and active elements of organisations. With this new
classification, the measures were linked with relevant constructs. For example, the
passive elements of an organisation consist of organisational structure in support of
roles and decision rights, and the scope of integration to understand whether the KI
among employees is happening in a complementary way or with a greater capacity.
These organisational structure and scope of integration components can be grouped as
the passive element of an organisation whereby these components are static, decided
by the organisation and should bind the employees.
Second, we propose the AIO construct to represent the component measures of
employees‟ common knowledge and the frequency of integration. Previously, the
common knowledge and frequency components were identified in the efficiency
construct, together with the organisational structure factor. We see that the frequency
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of integration involves myriad activities in receiving and giving knowledge among
employees. The common knowledge also needs many interactions and actions to
create a common understanding on certain topics among employees. Lastly, we classify
the flexibility measures in an AII construct and retain the nature of its elements.
Ideally, the constructs become easier to understand with these re-structured
measurements. In general, we provide a simpler model to be understood by
researchers and non-expert readers alike. We believe that the tighter the fit between
components of measures and the constructs, the better the model. The new research
model seems to provide a very natural way of representing the measures of
antecedents for the effectiveness of KI for the ES. When we analyse the findings, we
find this method to be successful through positive and significant empirical evidence.
Our findings show that all empirical evidence worked well with the research
hypotheses.
(d) Contribution 4: Operationalising the theoretical constructs
We provide a substantial contribution to the research area. One implication of our
research is that it is able to shape the understanding of KI in the ES research area. This
can be seen in two aspects.
First, we introduce measures for the influence factors of KI effectiveness by applying
the measurement components given by the knowledge-based theory of the firm by
Grant (1996). An overview of Grant‟s theory had not yet been fully empirically tested
(Caya 2008), and needed further exploration. Previous research examined the factors
through case study observations (Huang and Newell 2003; Hustad 2007; Pan et al.
2007). Thus, this research is the first study in which the entire measurements for
antecedents of KI effectiveness are empirically operationalised by offering the
measures. We obtain sufficient findings that are proficient to answer the first three of
our hypotheses.
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Second, we offer the measures of ES-knowledge base to identify how much the ES-
knowledge base is affected by the KI effectiveness, and how much it impacts on ES
success. This is tested empirically for the first time to study the critical knowledge
factors for the ES performance. In constructing the measures, we apply Davenport‟s
(1998) explanation for ES knowledge, including the elements of system knowledge,
business process knowledge and organisation knowledge.
By scoping our research focus, we only analyse the system knowledge and business
process knowledge components (refer to Chapter 1). We found that the measures
contribute to our fourth hypothesis by providing adequate findings to show that the
goodness of ES-knowledge base is influenced by the knowledge integration
effectiveness. Further, the measures help us to understand how much the goodness of
ES-knowledge base contributes to the ES success. The new measures facilitate an
understanding of the consequences of knowledge integration effectiveness towards the
goodness of ES-knowledge base and the ES success. Thus, our fourth and fifth
hypotheses were answered.
(e) Contribution 5: Validating KI effectiveness antecedents
empirically
This research presents the first empirically validated antecedents for KI effectiveness.
The previous development of KI application has focused on qualitative research. Thus,
this research makes an important contribution by making quantitative measurement of
KI antecedents feasible. Even though our research problem domain is the ES post-
implementation phase, the method may appropriate to be applied in any information
systems. This means that the research model may be tested by other researchers in a
different context. Hence, the work is potentially offers benefit to the research
community.
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(f) Contribution 6: Applying new theoretical perspective to study
the ES success
This research is among the first to empirically examine the impact of KI effectiveness
on ES success as a complete nomological net in the ES post-implementation phase.
Previously, most research in KI was focused on the group or project team
performance, but few have considered the ES success context (Tiwana 2001; Hustad
2007).
We propose that ES success is the consequence of KI effectiveness. In this proposal,
we take a new approach to the KBT of Grant (1996). The KBT theoretical explanation
was applied to test the impact of KI effectiveness on ES performance. This differs from
previous works, most of which refer to KI from the resource-based view (RBV)
perspective. Here, we study the KI impact on the ES success that is relevant to the ES
post-implementation in support of organisations‟ returns on their ES investments. We
adopt the ES post-implementation segment of ES utilisation to explore whether the KI
approach is causally linked to ES success. By doing so, this research also highlights the
fact that ES success is not only one of the causal consequences of KI effectiveness, but
that it also positively influences the individuals‟ ES-knowledge base.
The research model was tested in six large organisations in multi-industries in Malaysia
including the private and public sectors. The survey was used to gather information
from managerial and operational employees spread across those six organisations. The
results show that all the relationships in the research model were positively associated.
Consistent with the KBT explanation, we found that KI effectiveness was valid and
significantly related to the outcome of ES performance, which we refer to as ES
success.
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6.2.2 Contributions to Practice
(a) Contribution 1: Offers guidelines on how to make KI practice
effective in organisations
The research indicates the antecedents of KI effectiveness that need to be
comprehensively addressed by organisations in order to have an effective KI. Grant
(1996) concludes that organisational structure, level of common knowledge, frequency
and scope of integration, and the flexibility of integration are the important factors
affecting KI effectiveness. Departing from Grant‟s framework, this research discusses
the theoretical focus on the passive and active organisational practices, and the active
individual. To understand the issues, analysis was first undertaken to look into the
effective levels achieved by the proposed three antecedents - PIO, AIO and AII. With
these three constructs, the research interpreted the proposed antecedents for KI
effectiveness. Emerging from the study is a key message to organisations that making
huge investments in ES without taking care of KI among their employees will not secure
the success of the ES.
This research presents a model of antecedents and consequences of KI effectiveness
that can be used by practitioners to identify KI practices in their organisations and
predict the potential degree of the ES success. The study offers a guideline on how to
make KI practice effective in organisations through evidence of the importance of the
organisation‟s passive and active practices and also the activeness of individuals. Given
that the KI effectiveness is one of the critical factors for ES success, in return,
organisations that have better KI practices are more likely to have more ES benefits. ES
benefits are almost certainly rendered by less conflict towards the system given the
advantage of integrated knowledge of ES operationalisation among employees. In other
words, effective KI among ES users will likely reduce the degree of uncertainty in ES
performance.
Organisational influence constructs, including passive and active factors, must also be
part of an organisation‟s plan to make the ES implementation more effective and
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efficient. As passive integration elements in an organisation require a clear hierarchical
structure and sufficient scope for knowledge to be integrated among employees,
organisations must make a strong commitment to frame these two factors in a better
way. Some degree of variation can be expected according to the depth of the
integration‟s scope. A low scope of ES KI will definitely result in low integration
effectiveness. Excessive scope of integration is likely to affect the integration success
too. In other words, the scope of integration must be sufficient enough to be applied by
the organisation‟s employees. Organisational structure is likely to be the most stable
kind of passive organisational practice. A clearly structured organisational hierarchy
carries the potential for greater effectiveness in the ES KI.
Active KI practices in organisation entail issues including the level of common ES
knowledge among employees and how frequently the employees receive ES training,
receive and give feedback, or participate in meetings regarding the system in either
formal or informal ways. The level and frequency of these activities make a significant
impact on the effectiveness of KI for employees in organisations. Thus, organisations
should be more aware, more responsive, and give more consideration to the
employees‟ needs on these matters.
The research findings show that active integration practices by individuals contribute a
significant and high influence factor in every single analysis. In the findings of this
research, the active individual construct consistently reveal a strong influence on the
effectiveness of KI where this construct is the most powerful determinant of the KI
effectiveness compared to the passive and active organisation constructs. This
individual aspect should be addressed as an important issue for organisations in order
to have better ES performance. Organisations must be more concerned about
individual flexibility of integration, and encourage every employee to integrate their ES
knowledge in any way that suits the organisation‟s intent.
Organisations should put significant effort into these antecedents appropriately.
Accordingly, the KI among employees in the organisation will turn out to be more
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effective. This then generates a better level of employee ES-knowledge base goodness,
which then influences the ES performance by making a positive impact on the success
of the system.
(b) Contribution 2: Proposes the goodness of ES-knowledge base as
an influence factor for ES success
The theory of KI discussed here not only explains the influence factors, but also the
consequences of KI effectiveness. In turn, as impacts of KI, the goodness of the
individual ES-knowledge base was examined and the ES success level was investigated
to answer the research hypotheses 4 and 5 (refer to Chapters 3 and 5).
The level of ES success was examined as the final impact from the KI effectiveness and
the goodness of ES-knowledge base. Judgement was made about the goodness level of
the ES-knowledge base from the individual perspective. Notably, the goodness of
individuals‟ ES-knowledge base achieved as an outcome of KI effectiveness suggests that
organisations should consider that KI practices will sufficiently increase their
employees‟ level of ES-knowledge base. Ignoring the goodness of ES-knowledge base
issue among employees will not guarantee the organisation‟s ES success, as the findings
evidenced that ES success is generated from the goodness of ES-knowledge base and
not directly brought about by the KI effectiveness construct.
The research findings support and show how the links between KI effectiveness
positively and significantly impact on the individuals‟ goodness of ES-knowledge base
and the ES success. The result contributes to the understanding of the way in which the
ES-knowledge base can be influenced by the effective KI and its impact on the ES
success as a consequence.
KI is an important practice for employees to increase the level of their ES-knowledge
base. During the integration process, employees learn to add new knowledge, link with
others‟ understanding, benefit from others‟ skills, and sort and evaluate others‟ talents
and limitations through interactions. Effective KI helps them to increase their goodness
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of ES-knowledge base. The more effective the KI is, the better their ES-knowledge base
levels are.
Consequently, employees found that they are able to take less time to accomplish their
particular task, able to understand the mechanisms that facilitate performance of the
task, and can quickly carry out their task without constantly having to refer to the user
manual.
The research identifies individuals‟ perceptions of ES success along four dimensions,
namely, information quality, system quality, individual impact and organisational impact.
The findings suggest that employees consider the ES that they are using is overall
successful, as a consequence of having a good level of ES knowledge that resulted from
effective KI practices among them.
(c) Contribution 3: Alerts managers and employees to the issues to
generate ES success
Managers and employees who are continually faced with the challenge of utilising the
complex and large ES can benefit from the research in three ways: 1) to understand
whether they appropriately play their parts in having effective KI; 2) to understand
what factors they should consider in order to have better levels of ES-knowledge base;
and 3) to plan effective ways to have better performance of ES.
This research provides a guide to best practices for employees or ES users through
understanding the antecedents of KI effectiveness and factors to apply to increase their
ES-knowledge base, and to plan for the future. Managers and employees may benefit
from this research by adapting the findings to their policy and management approaches
to ensure they play their optimum role in integrating ES knowledge to successfully
operationalise the ES. Managers might use the research findings to revise their existing
KI practices in order to achieve better ES performance. For example, the results
suggest that they ought to be concerned with the potential antecedents of having an
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effective KI, and should take appropriate actions to harness the consequences of KI to
achieve ES success by having awareness of the employees‟ ES-knowledge base.
(d) Contribution 4: Offers guidelines of KI practices for ES success
for Malaysian organisations
The importance of KI is not being aware in most developing countries (Wright and
Wright 2002) including Malaysia. The major barriers for organisations in developing
countries to reap benefits from ES investment are lack of expertise of business process
knowledge (Huang and Palvia 2001) and ES knowledge disintegration (Nah and Degaldo
2006). The previous reports of problem on the operation of ES should led
organisations to acknowledge the importance of integrating the ES knowledge to
maximise the usage of the system. Practicing KI as a basic management practice
throughout processes in organisations allows them to improve ES operations and
become highly productive (Gartner 2006). Since the data were gathered in Malaysian
companies, the key findings of this research on KI effectiveness would be of value to
the staff as ES end-users and management of organisations in Malaysia when taking
decisions regarding the operation of the ES in terms of best practices, the nature of
working environment and society values. Clearly, findings of this study confirm that KI
effectiveness is important for an operational ES success among Malaysian organisations.
6.3 LIMITATIONS
Despite the contributions to theory, research and practice discussed above, there are
some limitations in the research. The limitations fall into two categories: limitations in
the questionnaire deployment and limitations in the research findings.
6.3.1 Limitations in the Questionnaire Deployment
The research limitations in the questionnaire deployment can be understood in relation
to four main points. First, although we referred carefully to the literature in
constructing our survey questionnaire, the pure theoretical discussion provides a
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limitation to our research. There were no prior instruments that could be used for our
research. All the survey items for the KI antecedent constructs were derived from the
theory justification and case study report from previous works. Further, all the
measures for the goodness of ES-knowledge base construct were generated from
literature. We found that the limited sources made it relatively difficult for the
constructs to be operationalised. However, we believe that addressing this limitation
made significant contributions to the research.
Second, in building the survey instrument, we employed the measures of Gable et al.
(2008) to examine the ES success by using the IS-impact measurement model. As the
survey was conducted in Malaysia, the questionnaire was developed in Malay language.
In doing so, we made some changes to the measures such as combining a few questions
of ES success measures to take into account differences in language and interpretation
of terms. We assume that these changes may have affected the nature of some
questions.
Next, the survey was conducted by handing the questionnaires personally to the
targeted respondents. We identified representatives in the organisations who would
collect the questionnaires back. Even though this method caused promising responses,
there were not as many returned forms as we expected. This limitation is
acknowledged and decisions were made about it in managing the survey. In addition, we
are aware that there are other proficient methods, such as web-based surveys, that can
facilitate high response rates.
And lastly, due to the research focus, data were not collected for the strategic group of
employees. It was assumed that the strategic group would not be using the ES as
frequently as the managerial and operational employees. As the research focus is on the
ES utilisation, it is believed to be more appropriate that the research data are gathered
from these two groups only. However, the data sample may miss some important
issues that are not captured from the research. We may have limited ability to identify
problems that may arise from the impacts of the strategic group on ES success from
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the KI effectiveness perspective. In addition, to understand how the KI impacts on the
ES performance, the research pilot case study is based on managers‟ views only. It does
not capture the operational employees‟ perspective in ES utilisation. Thus, further
investigation needs to be taken to generalise the implications of KI effectiveness from
the viewpoint of strategic, managerial and operational employment cohorts.
6.3.2 Limitations in the Research Findings
The collected sample was limited to the ES users in information technology divisions
and financial departments. From these divisions and departments, we were solely
focused on the managerial and operational cohorts‟ viewpoints. This limitation was a
result of attempting to manage the scope of our research.
First, we decided to gather our sample from the IT and financial sections only. We
recognised that these two divisions depend heavily on the ES in their general business
processes. According to the requirement to recruit respondents who were
knowledgeable about the ES, we assume that this decision is acceptable. However, due
to the increasing application of ES in other sections, our research findings are restricted
to the chosen sample. It is possible that the results will not be enough to be
generalised, as sectors that fall outside of these IT and financial operations might
produce different outcomes.
Second, we scope our respondents to be employees who carry out management and
operation tasks. These two groups were identified since they are the employees who
typically use the ES very frequently. This was done in order to have respondents who
were satisfactorily knowledgeable about the ES. Although the data were collected from
six large organisations, having 196 responses out of 300 respondents may rather a small
sample. Besides, it may be difficult for the findings to be generalised since the results
were not analysed for the other cohort, the strategic group. Thus, the data may need
to be expanded to the strategic group as well, as discussed in the section on future
studies.
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Third, this research only used a sample of Malaysian organisations for data analysis, and
as such, caution should be exercised in generalising the results. No specific reason
points to the fact that nationality might bias the results in a predictable direction.
However, to prove this assumption and generalise the findings, one may apply this
research to other countries. This is because the limited data source may weaken the
generalisability of the research findings in geographical settings.
6.4 FUTURE STUDIES
The research has significant potential for further extension. First, replication studies
should apply the investigation to another situation. We suggest that more research
should be conducted in future in order to identify other possible factors that influence
and cause KI effectiveness in the ES context. For example, future work could be
undertaken to investigate the ES that are being implemented in higher education or
healthcare institutes. As this research has already examined the issues by analysing KI
practices in private companies and the government sector, the same study could be
conducted in the higher education sector to understand how the theory can be applied
and generalised in that setting.
Second, the study can be extended to gather useful data. A case study could be carried
out to better understand the relationships between the antecedents and consequences
constructs together with the questionnaire approach. For example, it would be
beneficial to have a longitudinal study where the researcher observes the practices in
organisations to identify the KI practices among employees in utilising the ES. However,
this would require high levels of commitment from the participating organisations and
researcher.
Third, data may need to be collected using other methods. For example, collecting a
sample of data in a web-based version may gather more robust results with multiple
response rates. This is because the method is cost-effective, less time intensive and
crosses over geographical boundaries. According to sampling theory from the statistical
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point of view, the larger the sample size, the smaller the sampling errors tend to be.
Thus, to increase the robustness of the research findings, we suggest that sample size
should be increased in future work.
Fourth, the scope of this research is restricted to the KI effectiveness among
employees in the ES post-implementation stage as it specifically focuses on ES
operationalisation. Thus, the analysis of the goodness of ES-knowledge base and the ES
success as two consequences was limited to the knowledge about operationalising the
ES among employees. For further research, it may be necessary to collect data related
to other aspects of ES post-implementation such as ES maintenance or upgrading
(Sathish 2006). ES maintenance also suffers from another fundamental problem, which
is the loss of knowledge. In ES post-implementation, much ES knowledge is, typically,
either lacking, or only encountered in the source code. For instance, the business
model and requirements specification may have been lost, or never properly
documented, or the software engineers who participated in the initial ES configuration
are long gone. Future study that focuses on other stages can therefore be used to
generalise the result for the entire ES post-implementation phase.
Fifth, further investigation is required for the KI effectiveness for different levels of
cohorts in organisations. For example, the approach taken in this research could be
extended to investigate how KI among the strategic group of employees impacts on the
level of their ES-knowledge base, and how this leads to the success of ES. Therefore, it
would be productive for future work to verify whether the research findings can be
generalised to employees in these other groups.
Sixth, the research model has been tested by examining organisations that implemented
two particular products: ES product by SAP vendor and non-SAP product specifically
designed for government. This research suggests that further work could usefully be
carried out to verify whether the research findings can be generalised to ES products
from other ES vendors.
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Finally, the measurement items used in this study could be further improved as this is
one of the earliest empirical studies on the antecedents of KI effectiveness founded on
KBT. The measures might not completely represent the respondents‟ real situations.
The practices within the organisations may differ to the practices covered in the
questionnaire. For example, the practicality of job rotation, training, meetings and
feedback mechanisms might not suit the organisations‟ current processes. Thus,
replication study may be worthwhile to examine whether the results remain the same
or are further supported in future studies.
6.5 CONCLUSION
The purpose of this research is to examine how KBT can be operationalised regarding
the contribution of influence factors to KI effectiveness to enhance the levels of ES-
knowledge base that cause ES success. This research aims to position the importance
of KI effectiveness to achieve ES success in the ES post-implementation phase. To do
so, we have provided a theoretical justification and established empirically that there
are substantial connections between KI effectiveness and the ES success. The research
model was examined using responses from 188 ES users in six large Malaysian
companies in which the ES was in the post-implementation phase.
The results obtained from the analyses suggested that the research model
demonstrated sufficient and adequate fit in general to the data. We found that all five
research hypotheses specified by the research were supported by empirical evidence,
suggesting that all the hypotheses were valid. Therefore, we assume that the findings
were capable of providing a reasonable explanation of the knowledge integration
effectiveness in relation to its antecedents and consequences. Our additional findings in
a smaller sample based on classifications of employment cohorts, ES products and
working experience levels were consistent in most categories. Thus, the results
provide an even stronger support for our research hypotheses and further stabilise our
research model.
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The research findings offer an empirical explanation that KI and ES researchers can use
to motivate and guide future studies. In addition to a discussion of future works, we
explained some novel contributions to the aspects of theory, research and practice, as
well as our research limitations.
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APPENDIX 1: Example of ES Modules in Respondent’s
Organisation (from Interview Session)
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APPENDIX 2: Survey Instrument
Knowledge Integration for Enterprise System Success
a survey conducted by the
Faculty of Science and Technology
General Instructions for Completion
Introduction: This research is conducted to better understand the impact of knowledge integration on Enterprise Systems (ES). Mrs. Nor Hidayati Zakaria, a PhD student is leading the research in collaboration with Dr. Darshana Sedera and Prof. Guy Gable from the Queensland University of Technology (QUT). Purpose of the Survey: The purpose of this survey is to identify the impacts of knowledge integration on the success of ES. We seek to learn from your experience with the ES in your organisation. Insights into your experiences with the ES will be valuable in highlighting the areas where researchers, practitioners and others should be focusing their attention, today and in the future. An analysis from this study will provide a more comprehensive understanding of the crucial impact of knowledge on the success of an Enterprise System. Conduct of the Survey – The survey will be conducted during August 2009. Our team member, Mrs. Nor Hidayati Zakaria, will visit your organisation to distribute the survey questionnaires within the timeframe. Confidentiality - Detailed results of the survey will be confidential and findings will never be attributed to any individual. Only aggregated results are reported. Neither QUT nor any agency will receive a copy of the study database. If you have any concerns regarding the ethical conduct of this research, you can contact the Secretary of the Human Research Ethics Committee at the Queensland University of Technology on (+617) 3138 2091. General Instructions for Completing and Returning the Questionnaire – It will take you approximately 5 minutes to complete this questionnaire. Please answer all questions and return the completed questionnaire to our representative. If you have any questions concerning the questionnaire, please do not hesitate to contact us. Dr. Darshana Sedera Senior Lecturer School of IT Faculty of Science and Technology QUT, GPO Box 2434, Brisbane QLD 4001 AUSTRALIA e-mail: [email protected]
Prof. Guy G. Gable Research Director School of IT Faculty of Science and Technology QUT, GPO Box 2434, Brisbane QLD 4001 AUSTRALIA e-mail: [email protected]
Mrs. Nor Hidayati Zakaria Researcher School of IT Faculty of Science and Technology QUT, GPO Box 2434, Brisbane QLD 4001 AUSTRALIA e-mail: [email protected]
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General information about you:
Department: Position: Main task/s: Number of staff in your department: Number of staff in your organisation: Length of experience in this organisation: ______ years ______ months Length of experience in your current position: ______ years ______ months How often do you use an Enterprise System? A few times a day A few times a week A few times a month Only when necessary
Please provide your honest opinion in response to each statement or question. For statements 1-10, your choices of response are: 1) Strongly disagree; 2) Disagree; 3) Slightly disagree; 4) Neutral ;5) Slightly agree; 6) Agree; 7) Strongly agree 1. In my view, a clear structure of roles and staff
positions has provided an easy way for information about the system to be accessed and shared by the department.
2. In my view, staff absenteeism is not really affects the job to be done, as someone is able to make decisions or approvals on behalf of the absent staff member in my department.
3. My department rotates job functions when necessary so staff knowledge of the system improves in general.
4. I often obtain recent information about the system, my tasks and the organisation’s operations through communicating with other staff.
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5. My job is heavily dependent on many others within my department.
6. My job requires a lot of cooperation from others: a. Within the department b. Across other departments.
7. I like to experiment with new knowledge while performing my job through the well-maintained and updated system.
8. To solve a problem related to the system that is the same as or similar to a previously experienced problem, I prefer to use and enhance an existing method.
9. A standard operation procedure is only a general guideline, so when I use the system I use other better methods if necessary.
10. I always creatively use my existing knowledge of
the system in order to make my job easier (and
this may differ from standard process).
For questions 11-14, your choices of response are: 1) A few times a year; 2) Once a month; 3) A few times a month; 4) Once a week; 5) A few times a week; 6) Once a day; 7) A few times a day 11. How frequently do you participate in informal
system usage discussions among staff in your department?
12. How frequently do you attend formal meetings/ discussions to update system knowledge and solutions in your department?
13. How frequently do you receive training or guidance (formal or informal) on how to perform your job using the system?
14. How frequently do you receive new information or feedback about how you are expected to use the system to perform your job?
For statements 15-29, your choices of response are: 1) Strongly disagree; 2) Disagree; 3) Slightly disagree; 4) Neutral; 5) Slightly agree; 6) Agree; 7) Strongly agree 15. I competently combine what I already know with
new system knowledge from other staff.
16. I competently share my system expertise among staff.
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17. I competently combine my system expertise to jointly solve related problems among staff.
18. I know how my job fits with others to increase system knowledge among staff.
19. I can clearly see how my colleagues fit their diverse knowledge with the knowledge of other staff in my department to increase system knowledge among them.
20. I often manage to find new ways of solving problems related to the system.
21. I have a satisfactory knowledge of system-related skills.
22. I understand well the planned goals and objectives of the system.
23. There are some aspects of the system for which I do not feel competent.
24. I understand well the procedures, policies and system module functions which are related to my job.
25. I possess the necessary skills to work with the system based on shared experience among staff.
26. There are some aspects of the overall system modules which I do not understand.
27. I understand well the procedures, policies and system modules which are being used in my department.
28. I know well which individuals possess which area of system expertise in my department.
29. Most of the solutions to the system’s problems in my department reflect the views of the majority of staff.
For statements 30-42, your choices of response are: 1) Strongly disagree; 2) Disagree; 3) Slightly disagree; 4) Neutral; 5) Slightly agree; 6) Agree; 7) Strongly agree 30. The system is easy to learn.
31. The system is easy to use.
32. The system meets my requirements.
33. The system can be easily modified, corrected and improved.
34. Information from the system is easy to understand.
35. Information from the system is concise.
36. Information needed from the system is always available, readable and usable.
37. I have learnt much through the presence of the system.
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38. The system enhances my effectiveness in the job.
39. The system increases my productivity.
40. The system is cost-effective.
41. The system has resulted in overall productivity improvement.
42. The system has resulted in improved outcomes or outputs.
Thank you for your cooperation. Please let us know if you have any comments:
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APPENDIX 3: The Pool of 27 IS-Impact Measures
CONSTRUCTS ITEMS
Individual Impact
1. I have learnt much through the presence of (the
IS) 2. (the IS) enhances my awareness and recall of job
related information 3. (the IS) enhances my effectiveness in the job 4. (the IS) increases my productivity
Organisational Impact
5. (the IS) is cost effective 6. (the IS) has resulted in reduced staff costs 7. (the IS) has resulted in cost reductions 8. (the IS) has resulted in overall productivity
improvement 9. (the IS) has resulted in improved outcomes or
outputs 10. (the IS) has resulted in an increased capacity to
manage a growing volume of activity 11. (the IS) has resulted in improved business
processes 12. (the IS) has resulted in better positioning for e-
Government/Business
Information Quality
13. (the IS) provides output that seems to be exactly
what is needed 14. information needed from (the IS) is always
available 15. information from (the IS) is in a form that is
readily usable 16. information from (the IS) is easy to understand 17. information from (the IS) appears readable, clear
and well formatted 18. information from (the IS) is concise
System Quality
19. (the IS) is easy to use 20. (the IS) is easy to learn 21. (the IS) meets (the unit’s) requirements 22. (the IS) includes necessary features and functions 23. (the IS) always does what it should 24. (the IS) user interface can be easily adapted to
one’s personal approach 25. (the IS) requires only the minimum number of
fields and screens to achieve a task 26. All data within (the IS) is fully integrated and
consistent 27. (the IS) can be easily modified, corrected or
improved
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APPENDIX 4: The Letter of Participation
SEEKING FOR PARTICIPATING IN SURVEY
We are writing to seek your participation in a research of Knowledge Integration on Enterprise
System Success. Mrs. Nor Hidayati Zakaria is leading the research in collaborating with Dr.
Darshana Sedera and Prof. Guy Gable from Queensland University of Technology (QUT).
The purpose of this survey is to identify the impacts of knowledge integration on the
success of Enterprise Systems (ES). We seek to learn from your experience with the ES in your
organisation. The targeted participants are needed from employees who use the system in their
daily basis tasks. Insights into your experiences with the ES will be valuable in highlighting
where researchers, practitioners and others should be focusing their attention, today and in
future. An analysis from this study will provide a more comprehensive understanding of the
crucial impact of knowledge to the success of an Enterprise System.
The survey will be conducted during August 2009. Mrs. Nor Hidayati Zakaria will visit
your organisation to distribute the survey questionnaires within the time frame. Detailed results
of the survey will be confidential and findings are never attributed to any individual. Only
aggregated results are reported. Neither QUT nor any agency will receive a copy of the study
database. If you have any concerns regarding the ethical conduct of this research, you can
contact the Secretary of the Queensland University of Technology’s Human Research Ethics
Committee on (07) 3138 2091.
Your participation to this survey is crucial in providing the necessary information for
this research. Please feel free to include any additional comments you deem necessary or
relevant to improving the program. We very much appreciate your support in our effort
Sincerely,
Dr. Darshana Sedera
School of Information
Technology
Faculty of Science and
Technology
QUT, Brisbane AUSTRALIA
Professor Guy G. Gable
Research Director
School of Information
Technology
Faculty of Science and
Technology
QUT, Brisbane AUSTRALIA
Mrs. Nor Hidayati Zakaria
School of Information
Technology
Faculty of Science and
Technology
QUT, Brisbane AUSTRALIA
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Thank you for agreeing to participate in our study to better understand the impact of
knowledge integration on Enterprise Systems in your organisation. This study is conducted by
the IT Professional Services team of Queensland University of Technology, Australia, leading by
Mrs. Nor Hidayati Zakaria, an academic from Universiti Teknologi Malaysia (UTM).
For the above research purpose, the team seeks to distribute survey questionnaires with
you during August 2009. The goal of the survey is to get your views towards the knowledge of
Enterprise System you are using in your organisation. Your responses will be kept strictly
confidential and the aggregated findings will be reported only in academic purposes.
At your convenience, please indicate your date and time preference with a reply mail to:
Date:
Specific time:
Contact person (if any):
Other preference (if any):
Should you prefer another medium of communication, eg. Skype, please let us know as well.
Thank you once again for your participation and we look forward to meeting you.