Investigating the factors influencing Business Intelligence adoption A case of Saudi Arabia

Investigating the factors influencing Business Intelligence
adoption: A case of Saudi Arabia
Brunel Business School
MSc in Business Intelligence and Social Media
Academic Year (2014/2015)
MG5510 Dissertation
Name: Osama Al-Barrak
osabbr@gmail.com
Number of words: 12,849
A Dissertation submitted in partial fulfilment of the requirement for the degreeof
Master of Science
Brunel University
Brunel Business School
Uxbridge, Middlesex UB8 3PH
United Kingdom
Tel: +44 (0) 1895 267007
Fax: +44 (0) 1895 269865

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Abstract
Purpose: The modern business environment is complex and dynamic due to the growth of
technology, therefore the importance of business intelligence (BI) has been increasing. This study
investigates factors in the adoption of BI in the context of organisations in Saudi Arabia.
Methodology: Based on the literature review, multiple factors were identified and hypotheses
were developed and tested using quantitative method, conducting a survey questionnaire
distributed to 119 respondents (employees in the IT and BI departments of four governmental
organisations in Saudi Arabia). The research study was based on deductive approach and
purposive sampling technique was used. Descriptive and inferential analysis was performed using
regression and statistical tools to test the hypotheses.
Findings: The findings suggested that six out of thirteen factors (demand, government support,
observability, perception of strategic value, poor quality of data and lack of skilled resources)
affect BI adoption in Saudi Arabia, as supported by the results of the regression and statistical
analysis. Poor quality of data and lack of skilled resources impact negatively, while the other
factors have a positive influence.
Implications: The results of the study can be used both theoretically and practically. The
government of Saudi Arabia and similar countries can improve their rules and regulations to
support BI adoption and companies can target these factors to improve their adoption success.
Originality/value: The study offers great insights about the business intelligence and its adoption
in context of the organisations of Saudi Arabia and the results provide the promising factors that
can contribute to the high adoption of BI.
Key Words: Business Intelligence, Technology Adoption, TOE, Saudi Arabia, governmental
organisations

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Acknowledgements
First and foremost, I would like to express my gratitude to The Almighty Allah for all of our
countless blessings, especially for uniting us and making this special day possible.
Also, I would like to express my gratitude to my supervisor Dr. Kevin Lu for his expertise,
constructive guidance, and patience throughout this process.
Last but not least, I would like to show my sincerest appreciation to my parents, sisters and
brothers for giving me nothing but their sincere supplications.

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Declaration
I confirm that this report is wholly my work. The dissertation does not incorporate without
any proper acknowledgement of the particular author in referencing. To deliver my best in this
research with the knowledge about the research topic, I confirm that the work is not copied
from any previously published materials. The referencing and citation is fully provided in the
text using (Harvard Reference Style) and the full reference list is provided as well. I accept
that on submission of this report this research report becomes the property of Brunel
University who may further use this report for research purposes if required, without my
consent.
Date and Signed: 16/03/2016
I certify that the work presented in the dissertation is my own unlessreferenced.
Signature: Osama Albarrak
Date: 16/03/2016
Signature: Charity De Soe:
09/09/2015

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Contents
Chapter 1: Introduction.............................................................................................................. 1
1.1 Background to the research area ...................................................................................... 1
1.2 Background to the research context................................................................................. 2
Saudi Arabia................................................................................................................. 21.2.1
Saudi Industrial Development Fund (SIDF) ................................................................ 31.2.2
The Ministry of Commerce and Industry (MCI).......................................................... 31.2.3
The Saudi Arabian Monetary Agency (SAMA) .......................................................... 31.2.4
ELM Company............................................................................................................. 41.2.5
1.3 Research Aims, Objectives and Questions ...................................................................... 4
1.4 Research Approach .......................................................................................................... 4
1.5 Dissertation Structure....................................................................................................... 5
Chapter 2: Literature review ...................................................................................................... 6
2.1 Definitions and Concept of Business Intelligence ........................................................... 6
2.2 The Major Components of BI System ............................................................................. 7
Extract-Transformation-Load (ETL) ........................................................................... 72.2.1
Data Warehouse ........................................................................................................... 72.2.2
Online Analytical Processing (OLAP)......................................................................... 72.2.3
Data Mining ................................................................................................................. 82.2.4
2.3 The Benefits of BI............................................................................................................ 8
Tangible Benefits of BI................................................................................................ 82.3.1
Intangible Benefits of BI.............................................................................................. 92.3.2
2.4 Information Evolution Model (IEM) ............................................................................. 10
2.5 The existing IT adoption models and theories............................................................... 10
Technology Acceptance Model (TAM)..................................................................... 112.5.1
Technology-Organization-Environment (TOE) model.............................................. 112.5.2
Diffusion of Innovation (DOI) theory........................................................................ 122.5.3
2.6 Summary ........................................................................................................................ 14
Chapter 3: Research Model...................................................................................................... 15
3.1 The main adopted model................................................................................................ 15
3.2 The proposed Business Intelligence TOE-based model................................................. 15
3.3 Hypotheses Development .............................................................................................. 17
External (Environmental) Factors.............................................................................. 173.3.1

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Technological Factors................................................................................................ 183.3.2
Internal Factors........................................................................................................... 203.3.3
Chapter 4: Research Methodology........................................................................................... 25
4.1 Research Approach ........................................................................................................ 25
4.2 Research Strategy........................................................................................................... 26
4.3 Data Collection............................................................................................................... 26
4.4 Population and Sampling ............................................................................................... 27
4.5 Questionnaire design...................................................................................................... 27
4.6 Pilot Testing ................................................................................................................... 28
4.7 Data Availability and Credibility................................................................................... 28
4.8 Ethical considerations .................................................................................................... 29
4.9 Summary ........................................................................................................................ 29
Chapter 5: Findings and Analysis............................................................................................ 30
5.1 Data collection and response rate................................................................................... 30
5.2 Evaluation of non-response bias .................................................................................... 30
5.3 Data preparation............................................................................................................. 31
Data coding ................................................................................................................ 315.3.1
Data cleaning and screening....................................................................................... 315.3.2
5.4 Reliability analysis of independent variables................................................................. 32
5.5 Descriptive statistics....................................................................................................... 32
Demographic profile of respondents.......................................................................... 335.5.1
Characteristics of responding organisations............................................................... 355.5.2
Proportion of information maturity............................................................................ 375.5.3
5.6 Inferential statistics ........................................................................................................ 38
Multiple linear regression .......................................................................................... 385.6.1
Multiple linear regression equation............................................................................ 385.6.2
Simple linear regression............................................................................................. 405.6.3
5.7 Hypotheses testing ......................................................................................................... 44
External factors .......................................................................................................... 445.7.1
Technological factors................................................................................................. 465.7.2
Internal factors ........................................................................................................... 475.7.3
5.8 Summary ........................................................................................................................ 49
Chapter 6: Conclusions and Recommendations ...................................................................... 50

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6.1 Conclusion ..................................................................................................................... 50
6.2 Meeting the aim, objectives and questions of this dissertation...................................... 51
6.3 Implications.................................................................................................................... 52
6.4 Limitations ..................................................................................................................... 52
6.5 Further recommendations............................................................................................... 53
References List............................................................................................................................... 54
Appendices..................................................................................................................................... 65
Appendix A: Correlations among independent variables .......................................................... 65
Appendix B: Description of the five dimensions....................................................................... 65
Appendix C: Questionnaire........................................................................................................ 66
Appendix E: Approval Letter of Ethics Committee................................................................... 74
List of Figures
Figure 1: Map of Saudi Arabia......................................................................................................... 3
Figure 2: Technology Acceptance Model...................................................................................... 11
Figure 3: TOE model ..................................................................................................................... 12
Figure 4: Innovation decision making theory ................................................................................ 13
Figure 5: Individual innovative model........................................................................................... 13
Figure 6: Adoption rate model....................................................................................................... 14
Figure 7: Conceptual framework of factors affecting BI adoption in Saudi Organisations........... 16
Figure 8: Deductive research approach.......................................................................................... 25
Figure 9: Responses by day............................................................................................................ 31
Figure 10: working experience of respondents .............................................................................. 34
Figure 11: Organisations of respondents........................................................................................ 34
Figure 12: Qualifications of respondents ....................................................................................... 35
Figure 13: Activities supported by computer systems of respondents........................................... 36
Figure 14: Data storage media of respondents............................................................................... 36
Figure 15: The level of analytical tools uses by respondents......................................................... 37

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List of Tables
Table 1: Dissertation Structure......................................................................................................... 5
Table 2: Summary of proposed hypotheses ................................................................................... 23
Table 3: Brief Conceptual Definition of Research Factors............................................................ 24
Table 4: Data collection and response rate .................................................................................... 30
Table 5: Reliability analysis........................................................................................................... 32
Table 6: Demographic profile of respondents................................................................................ 33
Table 7: Proportion of information maturity.................................................................................. 37
Table 8 : Regression statistics / R Square...................................................................................... 39
Table 9: Regression statistics / ANOVA ....................................................................................... 39
Table 10: Regression results of BI adoption and the factors ......................................................... 40
Table 11: Simple linear regression / competition .......................................................................... 41
Table 12: Simple linear regression / Demand................................................................................ 41
Table 13: Simple linear regression / Government support............................................................ 41
Table 14: Simple linear regression / Reliability............................................................................ 41
Table 15: Simple linear regression / Complexity.......................................................................... 42
Table 16: Simple linear regression / Observability....................................................................... 42
Table 17: Simple linear regression / PSV ..................................................................................... 42
Table 18: Simple linear regression / Size...................................................................................... 43
Table 19: Simple linear regression / Employees' resistance ......................................................... 43
Table 20: Simple linear regression / Culture.................................................................................. 43
Table 21: Simple linear regression / Managers' innovativeness ................................................... 44
Table 22: Simple linear regression / Data Environment................................................................ 44
Table 23:Simple linear regression / Skilled resources availability................................................ 44
Table 24: Summary of hypotheses testing ..................................................................................... 48

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Chapter 1: Introduction
The growth in the technology in the era of globalization has changed the business
environment and increased its complexity. All companies now consider data to be a
fundamentally important tool in making strategic decisions, to extract the opportunities from the
external environment and to deal with the business challenges. Businesses must function in a
complex environment of ever-changing information technology, market competition, consumer
demands and market conditions. Many firms around the world have started investing in the
adoption of the technology that could play significant role in enhancing efficiency and improving
decision making. In line with this context, many organisations have been attracted towards
business intelligence (BI) technology to develop data-driven decision making.
BI is a very significant approach for firms that helps to make improved decisions, sharing
data and linking the departments across organisations to improve business performance and
processes (Lloyd, 2011). The use of and demand for BI is growing because of its advantages to
companies, such as data analysis, data reporting, data extracting, developing useful information,
forecasting and improving decision making. However, despite its manifest benefits and
significance, many organisations have not adopted BI for various reasons that can be clustered
under multiple factors addressed in this study.
1.1 Background to the research area
Various studies have been done about the factors influencing BI adoption in organisations.
Boonsiritomachai, McGrath and Burgess (2014) studied factors in BI adoption in small- and
medium-sized enterprises (SMEs) in the context of four categories: technological, environmental,
organisational and owner-manager factors. Puklavex, Oliveira and Popovic (2014) also studied
factors affecting BI adoption in the context of SMEs under the three categories of technological,
organisational and environmental. Other researchers have studied BI adoption in different
(relatively advanced) developing countries, such as Malaysia (Hatta et al., 2015) and Poland
(Olszak and Ziemba, 2012), but their focus was generally SMEs. Hartley and Seymour (2011)
developed a framework to study BI adoption in the public sector organisations of South Africa.
The main theoretical models used to understand technology adoption are the Technology
Acceptance Model (TAM) (Davis, 1986), the Technology-Organisation-Environment (TOE)

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model (Tornatzky and Fleischer, 1990) and Rogers’s models (Rogers, 1995, 2005), all of which
discussed various factors that influence the adoption of technology.
The previous and existing studies and theories contribute to the research study about the
factors affecting BI adoption. These studies help in deciding on a clear path for the study to meet
the aims and objectives. Also, the background information of the research area is also provided by
these studies. Although the studies are significant and contribute to the research, they were
conducted in the context of individual industries or countries, and most of these studies have
investigated the factors in the context of SMEs. Despite their great contributions, as discussed in
depth in the literature review, there is a research gap in terms of studying factors in BI adoption in
the context of Saudi Arabia, although some studies have been done on technology adoption in
higher education in Saudi Arabia (Tashkandi and Al-Jabri, 2015), thus this study was conducted
to fill the identified gap in the literature.
1.2 Background to the research context
Saudi Arabia1.2.1
Saudi Arabia is the largest country in the Gulf Region (see Figure 1), with a total
population of over 28.7 million. Arabic is the official and native language of Saudi Arabia, and
the country plays a major political and economic role throughout the Middle East and North
Africa (MENA), mainly based on its religious status as it the land of the two holy mosques and
the heart of Islamic world. Also, its importance as an oil producer (BBC, 2015). The oil and
petroleum sector accounts for 45% of Saudi GDP, causing the country to suffer from some
aspects of the resource curse (e.g. a lack of economic diversification, an over-expanded role of
the state and an underdeveloped private sector), extensive efforts have been made to create a
vibrant and dynamic business environment (Forbes, 2015).

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Figure 1: Map of Saudi Arabia
Source: Country Studies (2016)
Saudi Industrial Development Fund (SIDF)1.2.2
The Saudi Industrial Development Fund (SIDF) was established by Royal Decree with the
aim of supporting the growth and progress of the private sector in 1974. It provides loans for the
development, implementation and growth of the new factories and their upgrading. It also
provides the advice and consultancy to the industrial firms for finance, administration and
marketing in the Kingdom (SIDF, 2016).
The Ministry of Commerce and Industry (MCI)1.2.3
The Ministry of Commerce and Industry (MCI) was established by Royal Decree in 1954
for domestic as well as international trading regulations, growth and development. Various bodies
have joined it interested in commercial issues in the subsequent years. It aims to promote the
commercial and industrial sector in the Kingdom (MCI, 2016).
The Saudi Arabian Monetary Agency (SAMA)1.2.4
The Saudi Arabia Monetary Agency (SAMA) is the central bank of the Kingdom,
established by Royal Decrees in 1952. SAMA is responsible for monetary functions such as
printing the national currency (the Saudi Riyal), managing the Kingdom’s foreign exchange

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reserves and supervising commercial banks and credit and finance companies. It has branches
throughout the Kingdom (SAMA, 2015).
ELM Company1.2.5
ELM Company is owned by Public Investment Fund (PIF) that was established in 1986 as
a research and development company. The major focus of the company was on transfer of
technology and its localisation. In 2004, it has shifted its focus on information technology
services, mainly security. In 2010, it was issued as a Joint Stock Company by Royal Decree
(ELM, 2016).
1.3 Research Aims, Objectives and Questions
Based on the research gap and context explained above, the aim of this study is to explore
the factors that influence the adoption of BI in the governmental agencies of Saudi Arabia. The
research identifies the factors that support organisations to adopt and implement BI technology
and the barriers that prevent them from doing so. To achieve this aim, the following objectives
were established:
 To identify the importance of BI adoption in Saudi Arabia.
 To identify the correlation of BI with influencing factors in Saudi Arabia.
 To determine the positive factors supporting the adoption of BI in Saudi Arabia.
 To determine the negative factors comprising barriers to BI adoption in Saudi Arabia.
The research questions of the research study are as follows:
 What is the importance of the adoption of BI in Saudi Arabia?
 Is there any correlation between BI and the influencing factors?
 What are the positive factors that support the adoption of BI in Saudi organisations?
 What are the negative factors that influence the process of adopting BI in Saudi
organisations?
1.4 Research Approach
To test the hypotheses developed based on literature review and models, quantitative
methods were used. The research employed a deductive approach and utilised purposive sampling
technique. A survey questionnaire was administered to 119 employees from the IT and BI
departments of four governmental organisations in Saudi Arabia: SIDF, MCI, SAMA and ELM.

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The questionnaire comprised 37 questions, mainly close-ended ones, using a Likert-type scale.
MS Excel and SPSS statistical analysis software tools were used to analyse the data.
1.5 Dissertation Structure
Table 1: Dissertation Structure

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Chapter 2: Literature review
The chapter reviews the previous academic literature about business intelligence (BI) to
critically analyse different statements, perspectives and studies. The purpose of the review is to
highlight previously developed models and related theory on individual, group and organisational
technology adoption. The models may support the adoption of BI in the organisation and the
factors affecting its adoption.
Based on the factors identified in the reviewed literature and models, a conceptual
framework and hypotheses are developed to understand the relationship of different factors with
the adoption of BI in the organisation. This chapter reviews the literature of each developed factor
in this study to demonstrate the context of the research model and hypotheses.
2.1 Definitions and Concept of Business Intelligence
BI has been defined in many ways by many researchers (Niu, Lu and Zhang, 2009). For
instance, BI can be defined as the system that includes gathering data, storing data, and
management of knowledge along with using the analytical tools to present the data in the form of
useful information to the decision makers (Rouhani, Asgari and Mirhosseini, 2012). Watson
(2009) defined BI term as the broader group of tools, applications, processes, systems and
technologies for data gathering, integrating, accessing and examining to help management in
making decisions for the business. Another definition of BI is that it is a technological tool for
data gathering and analysis to support the organisational decision making processes and to
enhance performance (Elbashir et al., 2008).
The range of definitions in the pool of literature have consensus that data gathering,
analysing and support for decision making are the key characteristics of BI, but it is unclear
whether it is a tool, system, technology or process. Given that there is no universally agreed
definition of BI (Boonsiritomachai, McGrath and Burgess, 2014), various concepts, definitions
and characteristics of BI are converged to produce the definition used in this paper, that BI is the
process used within the organisation to efficiently gather and analyse data to identify worthy
information, along with capable use of human resources to improve decision making for
improved firm performance.

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2.2 The Major Components of BI System
BI system is a multi-layered software system composed of several duties specialised
software components that function in a systematic order. The major components include extract-
transformation-load (ETL), data warehouse, online analytical processing (OLAP) and data mining
unit (Ranjan, 2005).
Extract-Transformation-Load (ETL)2.2.1
The ETL component provides the mechanism for extracting, transforming, and loading
data from their sources to the warehouse (Olszak and Ziemba 2007). It provides the platform for
deriving meaningful business information from the huge volumes of data available in a business
environment by the three eponymous stages. In the extraction stage, business data is sourced from
transactional systems, business functions, operation processes and the internet. Subsequently, the
data sourced in the extraction stage is transformed so that it is compatible with the data
warehouse system, at the transformation stage. Finally, the data is loaded to the data warehouse
(Lloyd, 2011).
Data Warehouse2.2.2
The data warehouse is the basis of data storage within BI system. Data in the warehouse is
majorly oriented and integrated in accordance with the subject (Ranjan, 2005). The role played by
the data warehouse is crucial in the sense that it aggregates data according to relevance, thus
minimising the chances of confusion in information processing and dissemination. Enterprise
Data Warehouse (EDW) acts as the reception center for all the data received from all units of the
business. The data warehouse may be built in a multidimensional architecture whereby all data
received and kept in this component are regarded as trustworthy and reliable (California
Department of Technology, 2014).
Online Analytical Processing (OLAP)2.2.3
Online analytical processing is a vital component in BI system because it is the
component in which data is analysed, and from which sensible information is drawn (Nedelcu,
2013). Without it, the data that have been gathered remains irrelevant. OLAP had its genesis as an
easy way of analysing huge volumes of complex data (Lloyd, 2011). The complexity associated
with data analysis incurs time and cost inefficiencies that hamper decision making, necessitating a
system that can process the data in the shortest time possible. The online analytical processing

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unit can help make sensible information from such piles of data in real time and without much
difficulty.
Data Mining2.2.4
Data mining is useful in the automatic detection of variations in normal business processes
and transactions. These variations are normally primarily presented in data form and thus need
analysis for interpretation. This component of BI system makes use of statistical techniques to
achieve its functionality, such as classification and clustering. Time-series analysis is another
statistical technique that may be applied in data mining. Because business data collection is
always a continuous process, variations in the business environment may be captured on a
continuous basis. The presence of data mining helps to track such variations from their onset.
Because variations are immediately noted, speedy and corrective decision making is made
possible (Ong, Siew and Wong, 2011).
2.3 The Benefits of BI
All the components of BI system are very vital in the efficient management of business
operations. These components function together to help the management team to present business
information in a more comprehensible manner across the business hierarchical chain (Bălăceanu,
2007). The use of BI is associated with improved openness and use of information within an
organisation that in turn improves business processes, business profitability and detects red flags
whenever they arise (Olszak and Ziemba, 2007). Some of the benefits of BI are that is readily
visible, easily quantified and thus tangible. On the other hand, there are intangible benefits that
cannot be quantified.
Tangible Benefits of BI2.3.1
The three tangible benefits of BI to an enterprise stated in many studies include time
saving, cost saving and return on investment (ROI) (Hočevar and Jaklič, 2010).
2.3.1.1 Time Saving
As an automated system, BI prepares, analyses and processes data in real time, thus
avoiding the time inefficiency and human resources cost that would be incurred by a manual
system. BI thus allows for quick information deduction from available data and an expedited
process of making business decisions. In essence, BI facilitates redirection of personnel time from
data analysis and processing to other vital business functions. Consequently, BI saves time on

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activities such as faster generation of reports that advise in the speedy making of business
corrective and helpful business decisions (Hočevar and Jaklič, 2010).
2.3.1.2 Cost Saving
Use of an automatic system ensures that the costs incurred in the procurement are a one-
off investment that may be inclusive of such needs as staff training in its use, unlike a manual
approach where multiple people are involved in manual data processing and analysis requiring
continuous skills training with emergent technologies as well as checking for human error
(Negash, 2004). BI avoids the resultant direct recurring costs associated with IT infrastructure,
employees or consultants involved with routine manual data processing and analysis. The
deployment of an automated system that is able to multitask also translates into less IT
infrastructure and employees (or more profitable redeployment of personnel). Moreover, BI
allows for early detection of anomalies that would otherwise result in more serious business
losses.
2.3.1.3 Return on Investment (ROI)
The overall benefits of BI include a positive net ROI (Hočevar and Jaklič 2010, p. 95).
The functionality of BI increasing operations efficiency increases profitability. The costs saved on
diminished employee overheads contribute to lower expenditures and hence higher return
margins. The advantage of using BI to generate information on competition through competitive
intelligence enables the business to take advantage of competition weakness (Negash, 2004). As a
result, it greatly contributes to the overall ROI of integrating such a system as part of normal
business functions (Hočevar and Jaklič, 2010).
Intangible Benefits of BI2.3.2
BI can also contribute intangible benefits that can improve the performance and hence
profitability of a business. For example, BI is able to accord to a business the knowledge of its
surrounding environment.
2.3.2.1 Competitive Advantage
Through competition intelligence, BI can easily detect competition trends within the
business environment (Negash, 2004), as a result of which business management executives can
make quick strategic decisions to position the business to take advantage of gaps not filled by the
prevailing competition. Again, competition intelligence merits that executives think ahead of the
competition by devising ingenious competitive strategies.

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2.3.2.2 Efficiency and Effectiveness
BI facilitates the efficiency of roles and functions in organisations, making them more
effective. Considering that data processing, analysis and presentation happen fast, this enhances
the speed of making business decisions and consequently improves the responsiveness of firms to
dynamic business environments. A rapid flow of communication across the different quarters of
the business environment has been made possible by the timely access to such information by
those within the organisation who need it to function effectively (Hočevar and Jaklič, 2010).
2.3.2.3 Single Version of Truth
BI system also acts as a single source of data processing, management and analysis, thus
preventing the possible occurrence of overlap and distortion that would otherwise result from
using several platforms. The centrality of data storage and processing increases data consistency
and contributes to a clear strategic direction for the business (Matei and Bank, 2010).
2.4 Information Evolution Model (IEM)
Maturity status in designing a BI system is a progressive process and not an event. Being a
transformational process, the maturity element of a BI system is guided by such systems that have
already attained maturity (Olszak, 2013). The Information Evolution Model (IEM) is a maturity
model proposed by SAS that is useful to corporations that want to scrutinise how they
strategically handle and use information to make their ventures profitable and function normally.
Business information is a very crucial corporate asset that must be handled with utmost value
because its proper use is able to guide business actions and activities that add value and profit to
the organisation (Leat, 2007). Olszak (2013, p. 954) noted five maturity levels proposed by the
Information Evolution Model: operation, consolidation, integration, optimisation and innovation.
2.5 The existing IT adoption models and theories
There is a range of theories, frameworks and researches that can help in understanding the
process of BI adoption. Here, the adoption can be defined as the implementation of the
technology that must be new within the firm (Hanel and Niosi, 2007). Hatta et al. (2006) studied
the application of the theories to understand firms’ technology adoption, finding that it the latter
is conditioned by the nature and needs of users, the process of designing the information
technology and its quality. It is clear from BI literature that it is a tool or system of information,
therefore using information system theories can assist in understanding its adoption process, such

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as the Technology Acceptance Model (TAM) (Davis, 1986), Technology-Organization-
Environment (TOE) and Diffusion of Innovation (DoI) models (Boonsiritomachai, 2014).
Technology Acceptance Model (TAM)2.5.1
Davis (1986) proposed the TAM to understand user in adopting or refusing a technology
(Ajzen and Fishbein, 1980).
Figure 2: Technology Acceptance Model
Adapted from Davis (1986)
The diagram above shows the TAM presented by Davis. According to the model, the
attitude of the users, perceived usefulness and ease of use, and behavioural intention result in the
actual use of the technology. External factors directly influence the perceived usefulness and
perceived easiness of use (Park, 2009).
Technology-Organization-Environment (TOE) model2.5.2
Based on the multiple models and frameworks, Tornatzky and Fleischer (1990) developed
the TOE model to study the adoption of technology, as shown in Figure 3.

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Figure 3: TOE model
Adapted from Tornatzky and Fleischer (1990)
According to the model, there are three major factors in organisational technology
adoption: technological, organisational and environmental characteristics. It is one of the basic
frameworks to predict and understand the adoption of BI in the organisations. According to
Kauffman and Walden (2001), based on the TOE model, the technological aspect is influenced by
the technology development, and the external environment according to Kowath and Choon
(2001). The role of organisational characteristics was supported by Jeyaraj, Rottman and Lacity
(2006).
Diffusion of Innovation (DOI) theory2.5.3
DOI was first proposed by Rogers (1983) in terms of the process of the communication of
innovation through various channels to the members of the system (either social or
organisational). In subsequent work, Rogers (1995) also identified the characteristics that can
impact the technology adoption, including innovation, adoption, time and system. He proposed
four DOI dimensions, namely innovative decision making, individual innovativeness, adoption
rate and perceived attributes.
2.5.3.1 Innovative Decision Making
Rogers (1995) suggested that there are five levels of innovation decision making:
knowledge, influence, decision, execution and confirmation, as shown in Figure 4. Acceptance or
rejection is decided at this decision level.

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Figure 4: Innovation decision making theory
Adapted from Rogers (1995)
2.5.3.2 Individual Innovative
The individual innovative dimension of Rogers (1995) is shown below (Figure 5).
According to the model, there are five levels of technology adoption among individuals, including
innovators, early adaptors, early majority, late majority and laggards. Coklar (2012) found that
the innovators are risk takers while early adaptors are followers, without considering the features
of the technology. Majorities are those adaptors who are careful about the use and uncertainties
and late majority are those who are highly influenced by the resistance, while laggards are
inherently resistant and high pressure is needed to influence them.
Figure 5: Individual innovative model
Source: Rogers (1995)
2.5.3.3 Adoption Rate
Adoption rate is illustrated in Figure 6. This dimension reflects the behavioural patterns
pertaining to individual innovative, with the curve in the diagram showing the rate of technology
adoption by various groups. It can be seen that the innovation/ technology is adopted gradually
during the initial stage (by innovators and some early adopters) until a certain critical mass is
reached, whereupon it enters into a stage of rapid adoption by the majority. Having reached
saturation of adoption the rate declines until only laggards have yet to adopt the technology.
Knowledge Influence Decision Execution Confirmation

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Figure 6: Adoption rate model
Source: Rogers (1995)
2.5.3.4 Perceived Attributes
Rogers (2005) suggested that the rate of technology adoption is slow mainly because of
the difficulties of individuals to perceive the advantageous attributes of that technology. Based on
the proposed theory, Rogers identified the five characteristics of the technology adoption
influencing how its attributes are perceived, including relative advantage, complexity,
compatibility, trial-ability and observability. Innovators and early adopters are likely to adopt the
technology based on relative advantage (i.e. perceived benefits), while for the majority groups
and laggards observability plays a stronger role. Firms with a dominant market position or a
strategy of high investment in research and development, or emerging firms without a legacy
system (which is more expensive to replace) are more likely to be early adopters, whereas more
cautious firms wait and see whether the adoption has been successful among competitors.
2.6 Summary
The literature review chapter discusses various past studies about BI and the factors
affecting its adoption. Different factors were discussed in the pool of literature, mainly based on
the three major categories of external, technological and internal factors. These categories are
identified based on the models discussed and justified. The next chapter shows and justifies the
main adopted model, the developed framework and the generated hypotheses. It also reviews the
literature of each selected category and factor.

15
Chapter 3: Research Model
This chapter presents the research model and the developed theoretical framework for this
study. It is based on the literature on different models that discussed the factors influencing
technology adoption for organisations outlined in the second chapter. Moreover, the chapter
justifies the factor selection process and the main adopted model, followed by the final proposed
framework. Furthermore, the generated hypothesis will be described and linked to the related
literature and previous models. The chapter also criticises the previous models and how the main
adopted model and the selected factors are compatible with the context of this study to achieve its
aim, which is to explore the factors that influence the adoption of BI among government
organisations in Saud Arabia.
3.1 The main adopted model
TAM is widely accepted model mainly applied in the adoption of information systems,
and it has been proven more efficient and dominant than other earlier theories of technology
adoption. Its use has found to be very valuable in understanding the behaviour of use of the
information system and in offering reliable outcomes (Johar and Awalluddin, 2011).
In a similar vein, many researchers have found it limited and recommended its use with some
other enabling factors and components (Wong, 2005).
For Rogers' Theory of diffusion of innovation (DOI), many studies have found that it is
very helpful to conceptualise the acceptance and adoption of the innovation. However, some
researchers have criticised that DOI explains more about the technology adoption stages and
level, while the behaviour of adoption is not focused (Thong, Yap and Raman, 1996).
On the other hand, TOE is widely used model and the most consistent to be used to
understand the adoption of technology at the organisational level. In contrast, both DOI and TAM
lack the focus on the organisational characteristics which has an impact on the acceptance of
innovation and technology. Therefore, TOE is the main adopted model in this research. For this
research context, a combination of different factors of various theories is considered as more
useful, valuable and even popular in the pool of literature (Zhu, Kraemer and Xu, 2003).
3.2 The proposed Business Intelligence TOE-based model
As the TOE model will be used, the selected factors are categorised into three parts
include external factors, technological factors and internal factors. Different studies have used

16
TOE model as the BI is not limited to the external and technological factors but also identified the
opportunities from the internal environment. Hartley and Seymour (2011) have developed the
model to study the factors affecting BI implementation in the public sector organisations of South
Africa and found that the organisational and technological factors as the most affecting to it
utilisation.
In this study, the factors that have been imported from the other models and studies are
grouped into these three categories based on their nature. Puklavec, Oliveira and Popovic (2014)
have also identified the factors in the three categories that include technological, organisational
and environmental. They included the management factor in the organisational context. For the
research at hand, the manager role can be discussed in the context of the organisational (internal)
factors. The following figure is a diagrammatical representation of the proposed framework of the
business intelligence adoption model, followed by corresponding hypotheses.
Figure 7: Conceptual framework of factors affecting BI adoption in Saudi Organisations

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3.3 Hypotheses Development
Based on the business intelligence adoption model, this part reviews the categories and
factors of the model, and then defines the developed hypotheses.
External (Environmental) Factors3.3.1
A range of studies have explored the factors and different characteristics of the external
that drives technology adoption (Zhu, Kraemer and Xu, 2003; Ramdani, Chevers and Williams,
2013), identifying different factors or external influences on BI. Hatta et al. (2015) found that
business partners and competitors are the major factors that influenced the technology adoption.
Other important factors include market demands and governmental support.
3.3.1.1 Competition
Competition in the market persuades the firms to innovate new approaches and ideas to
achieve competitive advantage (Themistocleous et al., 2004). Competition is found as the most
affecting factor in some studies (Boonsiritomachai, 2014; Puklavec, Oliveira and Popovic, 2014).
Waarts et al. (2002) stated that market competitiveness is the major factor that drives the adoption
of technology in the companies. Another study found that the pressure from the competitors
influenced the adoption of technology, whereby the focus of the study was CRM system adoption
in the firms (Alshawi et al., 2011). In line with the above literature, the study will prove the
following hypothesis:
H1: Competition positively affects BI adoption in Saudi organisations
3.3.1.2 Demand
With the growing use of technology and information systems, demand for BI is also rising
(Anderson-Lehman et al., 2004). Based on the demand and its rise, many studies have predicted
that the growth will be continued further in future. The expected growth is directly linked with the
solutions and support of BI to the decision making and data analysis (Lachlan, 2013). Changing
market demands induce company approaches to seek dynamic approaches such as BI
(Ramanigopal, Palaniappan and Mani, 2012). Hatta et al. (2015) also identified demand as an
external factor affecting BI adoption. Based on the literature debate, the research will measure the
following hypothesis:
H2: Demand positively affects BI adoption in Saudi organisations

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3.3.1.3 Government Support
Government supports was also found in the list of environmental factors influencing the
technology adoption. Stewart (2010) studied the impacts of regulations on the innovation and
concluded that government regulations directly stifle innovation, thus the effects of this might be
reduced if the firms are able to evade these regulations (e.g. operating in more lax legislative
environments). Tornatzky and Fleischer (1990) also found that government regulations are a
barrier to technology adoption. In contrast, some studies have found that the government
regulations can drive technology adoption if governments support the growth of business from an
economic perspective (Chen, 2006; Esselaar et al., 2007; Madrid-Guijarro et al., 2009). In other
words, government regulation has the potential to affect BI adoption either positively or
negatively. This review led to prove the hypothesis as follows:
H3: Government Support positively affects BI adoption in Saudi organisations
Technological Factors3.3.2
Rogers (2005) identified a number of technological characteristics and factors that
influence the diffusion of innovation in the organisation, notably relative advantage, complexity,
compatibility, trial-ability and observability. A number of studies have explored the relationships
between technology adoption and these characteristics (Hua, Rajesh and Theng, 2009; Ramdani,
Chevers and Williams, 2013). The literature supports the impacts of technological factors on
adoption, and it can be said that the characteristics of BI can either drive or resist its adoption in
enterprises (Puklavec, Oliveira and Popovic, 2014). Based on the multiple theoretical
perspectives, there are four major technological factors used to study the impacts on adoption,
including reliability, complexity (Sahey and Ranjan, 2008), observability (Roger, 1995) and
perception of strategic value (Jang et al., 2009).
3.3.2.1 Reliability
Ramamurthy et al. (2008) studied factors in the information and data process of
organisations. Reliability has been identified as one of the most influential factors. Dumitrita
(2011) argued that BI is significant to generate and access steady information. Buttler and
Sellbom (2002) also found reliability to be a factor in technology adoption and stated that it is one
of the barriers to the adoption of technology. Many other studies discussed reliability as an
important factor in adoption (Abadi, 2009; Benlian and Hess, 2011; Bhattacherjee and Park,
2013). With the support of research, the following is hypothesised:

19
H4: Reliability of BI positively affects its adoption in Saudi organisations
3.3.2.2 Complexity
Chen (2006) supported the concept of complexity impacting technology adoption. BI is
inherently very complex, which causes firms to attach very high importance to hiring and
contracting with the most efficient vendors if they decide to adopt the technology (Folinas, 2007;
Sahey and Ranjan, 2008). This means that the organisational decision to adopt BI is influenced by
its own complexity.
H5: Complexity of BI negatively affects its adoption in Saudi organisations
3.3.2.3 Observability
Rogers (2005) claimed that the adoption of the technology is influenced being observing
others already using it. The visibility and physical presentation of the technology affects the
perceptions of organisations and the individuals within them (Lundblad, 2003). However, Grover
(1993) found observability to be a less significant factor in technology adoption, subsequently
corroborated by another IT study (Premkumar and Ramamurthy, 1995). Therefore, this research
will explore and identify the impacts of BI observability on its adoption.
H6: Observability of BI positively affects its adoption in Saudi organisations.
3.3.2.4 Perception of Strategic Value
Many studies have already stated the importance of the perception of the strategic value of
any new adopted technology (Teece, Pisano and Shuen, 1997). In line with these outcomes, the
researchers have found that the technology is adopted when the company perceives it to be useful
in adding strategic value. Jang (2010) claimed that perception of values has significant impacts on
the technology adoption. Grandon and Pearson (2004) found that the implementation of the
technology in the firm is mainly based on the strategic benefits of that technology in terms of
productivity, operations and performance. The literature of BI supports that it is very significant
for the business process and performance because it directly helps in decision making. Therefore,
the strategic benefits of BI (perceived or actual) can be expected to affect its adoption.
H7: Perception of Strategic Value positively affects BI adoption in Saudi organisations.

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Internal Factors3.3.3
The internal organisational factors were explored by many studies (Yu and Zhang, 2010).
There is a range of literature that debates the impacts of organisational factors as the issue in the
technology adoption. The researchers have found that the inside ability of the organisation and its
resources have noteworthy impacts on the adoption of technology (Zahra and George, 2002) The
studies mentioned in the literature that have studied the factors affecting BI adoption found some
common internal factors that impacts on the adoption of BI, including size of firm, culture, role of
managers, role of employee, skilled resources availability and data environment
(Boonsiritomachai, McGrath and Burgess, 2014; Boonsiritomachai, 2015; Hatta et al., 2015).
3.3.3.1 BI Adoption in Large Enterprises
Flavia (2014) found in his study discussing the internal factors affecting BI adoption that
larger firms who adopt BI technology mainly do so because of operational and technical
perceptions. Large firms are more concerned to improve their performance in the market to
maintain and increase their market share, therefore their decision making process constantly
considers new approaches. Puklavec, Oliveira and Popovic (2014) also focused on the large
organisations for the adoption of BI and have stated that size of the firm has huge impacts on the
technological adoption.
3.3.3.2 BI Adoption in SMEs
Though the majority of researchers considered BI technology to be more associated with
larger organisations, many studies discussed the use of BI in SMEs (Lee and Cheung, 2012).
Abzaltynova and Williams (2013) supported the idea and stated that both small and medium sized
firms are concerned about the use and adoption of BI for the business performance; while it
represents a comparatively more risky investment for them than it does for large firms, they often
have no legacy system and thus are prone to adopt newer technologies when needed. A number of
studies have found that the size of firm influences the adoption of the technology (Ramamurthy et
al., 2008; Jang et al., 2009). Therefore, the next hypothesis of the research as follow:
H8: Organisation size positively affects BI adoption in Saudi organisations.
3.3.3.3 The Role of Employees
Employee impacts on BI adoption have mainly been explored in terms of resistance to the
innovation, which can prevent the implementation of the new technology (Economist Intelligence
Unit, 2007). Studies have argued that weak knowledge about the technology among employees

21
prevents them to using it, and they latently prefer to use traditional and familiar ways of
conducting operations and processes. Here, the compatibility factor of Rogers (2005) theory can
be linked that suggested level of technology compatibility with the existing values and norms and
practices impacts the adoption. This means that resistance from the employees to follow their
traditional practices might be included in the factors that impact BI adoption.
H9: Employees Resistance negatively affects BI adoption in Saudi organisations.
3.3.3.4 Culture
Puklavec et al. (2014) found that the organisational culture is another influencing factor in
technology adoption. Leidner and Kayworth (2006) argued that the culture can impact on the
technology adoption at any level. Davis, Miller and Russell (2006) found that the social,
behavioural and moral culture of the firm is directly linked with the information flow within the
organisation, particularly as the BI system deals with providing information within the
organisation.
H10: Organisation Culture positively affects BI adoption in Saudi organisations.
3.3.3.5 The Role of Managers in BI Adoption
Different studies have supported the influence of managers in the technology adoption
(Chang and Tsia, 2006; Ghobakhlooet al., 2011; Hung et al., 2011; Nguyen and Waring, 2013).
Innovativeness, technological background and experience of the managers influence their
decisions to decide either to adopt new technology or not (Gorver, 1993). Hatta et al. (2015) also
found that innovativeness of management is a considerable factor that directly impacts the
adoption of BI. The majority of studies have found significant impacts of managers on the
technology adoption, and that will support the following hypothesis:
H11: Innovativeness of Managers positively affects BI adoption in Saudi organisations.
3.3.3.6 Organisational Data Environment
Data are the backbone of any analytical software including BI. High data quality must be
available to provide high quality report then support decision making. Shen, Hsu and Peng (2012)
studied the impacts of data environment within the firms in relation to the impacts on BI
adoption. The organisational data environment has been found to affect BI adoption in SMEs in
particular (Puklavec, Oliveira and Popovic, 2014). The adoption of BI is based on the required

22
data quality within the company, which means that the level of data quality of the Saudi
organisations might influence BI adoption.
H12: Poor Data Quality negatively affects BI adoption in Saudi organisations.
3.3.3.7 Skilled Resources Availability
For the adoption of any new technology appropriately skilled resources are important.
Karkoviata (2001) argued that unskilled personnel hamper the IT adoption, including intrinsic
lack of skills among employees or conservative behaviours by management (Wade and Hulland,
2004). Hwang et al. (2004) claimed that the skills of the team directly impact the decision of the
management to adopt the technology. Dunne and Troske (2004) also studied the relationship of
technology adoption with workforce skills and found that a more skilled workplace is required for
the adoption of advanced technology.
H13: Lack of Skilled Resources negatively affects BI adoption in Saudi organisations.

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Table 2: Summary of proposed hypotheses

24
Table 3: Brief Conceptual Definition of Research Factors

25
Chapter 4: Research Methodology
This chapter aims to describe and justify the chosen research methods, strategy, and data
collection process and credibility, to discuss the questionnaire design and the sampling technique
used, and to outline the pilot testing and ethical considerations. Research methodology can be
defined as the systematic process of solving a research problem. It is a field of study that focuses
on the methods for research to obtain knowledge (Kumar and Phrommathed, 2005). It is very
important to devise a methodology for solving the chosen problem of this research, which is to
investigate the factors influencing business intelligence adoption in Saudi Arabia.
4.1 Research Approach
The researcher chose a research approach according to the goals and objectives of the
research and the type of study (Saunder, Lewis andThornhill, 2009; Yin, 2013). Therefore, the
deductive approach is more appropriate for the conducting the research. The deductive strategy
can be defined as a strategy that gets its support from an already developed theoretical framework
(Zikmund, 2003). The deductive approach works from the more general to the more specific and
can also be defined as the "top to down" research approach. It can be more clearly understood
from the process mentioned in the diagram below:
Figure 8: Deductive research approach
Source: Gill, Johnson and Clark (2010)
As this research aims to test theories and hypotheses developed based on the previous
literature and models, a deductive approach will be used.
Theory
Hypothesis
Observation
Conformation

26
4.2 Research Strategy
After selecting a research approach, the next step is to select, carefully, a research strategy
for conducting the research (Saunders et al., 2011). There are two main categories of research
methods: quantitative and qualitative (Cherry, 2000; Ritchie et al., 2013). Here, the researcher
used a quantitative methodological strategy to increase the validity and authenticity of the
research. As a deductive approach was used, therefore, a quantitative strategy was more aligned
with the approach (Soiferman, 2010). An empirical and statistical study was required to test the
hypotheses about the factors that influence BI adoption in Saudi Arabia. Empirical data can be
collected by experimentation, surveys, etc., which are widely used techniques in quantitative
methodologies.
For this quantitative research, data was collected through a survey in which the
participants responded to an online questionnaire. By analysing the responses to the
questionnaire, the researcher was able to assess the research results on a quantitative basis.
4.3 Data Collection
The data collection process is considered one of the most important processes in
conducting research. Primary data sources can be defined as first-hand data that is gathered
through direct sources, including surveys, questionnaires, interviews, focus groups, observations,
experiments, and so on (HoxandBoeijie, 2005). In this research, primary data was research in a
well-defined manner. It was also necessary to utilize appropriate and proper data collection
sources for achieving the desired objectives. Thus, researcher utilised the primary source of data
collection.
Primary data was collected by utilising survey and questionnaire techniques to collect
analysable data that supports the research aim as well as to facilitate the determining factors, and
explaining the correlations and relationship among the variables. Close-ended questions were
used in the questionnaire for the clarity of the responses. The questionnaire was based on 37
questions that were mainly associated with the factors influencing BI adoption that were
identified in the literature review. Flexibility to answer the questions in the survey is mainly
considered as a non-standardised questionnaire. However, to ensure the interaction, the Likert
scale was used to provide a level for the respondents to express their opinions and responses.

27
4.4 Population and Sampling
The population of the research was based on the total number of people that were included
in the research study (Saunder, Lewis and Thornhill, 2009). In this research, the population of the
study includes four large Saudi organisations. These four organisations are as follows:
 (SIDF) Saudi Industrial Development Fund.
 (MCI) Ministry of commerce and industry.
 (SAMA) Saudi Arabian Monetary Agency, which is the central bank of Saudi Arabia.
 (ELM Company) which is a joint-stock company owned by the Saudi Public Investment
Fund (PIF).
The research sample was chosen by utilising the purposive sampling technique. Purposive
sampling can be defined as a sampling technique that is used when the researcher needs to access
a particular group of people (Palys, 2008). In the following research, purposive sampling was
used because the researcher needed access to BI and IT employees in these organisations.
The sample size, chosen for the questionnaire survey, was 119 employees, with the
consideration of the lack of BI specialists, overall, in Saudi organisations. Therefore, the
questionnaire was sent to all the available specialists in BI and IT. The questionnaires were
distributed to the participants through emails and LinkedIn as the least time-consuming survey
method. It is also important to offer the respondents an adequate amount of time to think about
the questions and make proper replies.
4.5 Questionnaire design
As it has already been mentioned, the questionnaire consisted of 37 questions. Here, the
design and structure of the questionnaire need to be discussed.
Out of a total of 37 questions, three questions were demographic; five were
psychographic, and the remaining questions were about the factors that influence BI adoption.
The demographic questions were used to obtain profiles of the respondents. The five questions in
the second section were asked to identify the level and the intensity of technology and software in
order to determine the maturity level of information technology in the selected organisations. The
questions in the third section were designed to test the effect of each factor in each category
identified in the literature and hypotheses that influence BI adoption in Saudi organisations.

28
Questions 9 and 10 aimed to determine the effect of competition on BI adoption. Question
11 aimed to identify the impacts of demand, and questions 12, 13, 14, and 15 were designed to
understand the impacts of government support. All of the questions were placed under the
external factors category. Questions 16 to 24 fell into the category of technological factors.
Questions 16, 17, and 18 addressed the reliability of BI implementation; question 19 was linked
with the complexity; questions 20 and 21 addressed observability; and questions 22, 23, and 24
were concerned with the perception of strategic value affecting BI adoption. Questions 25 to 35
were linked with internal factors.
Questions 25 and 26 were meant to determine the impact of size, questions 27 and 28
addressed employee resistance, questions 29 and 30 related to culture, question 31 was about
managers’ innovativeness, questions 32 and 33 were about the data environment, and questions
34 and 35 addressed skilled resources availability impacts. The last two questions were asked to
obtain the recommendations of the employees for the organisations to adopt BI. To measure the
intensity of the answers, the five-level Likert scale was used, assorting from strongly agree to
strongly disagree.
4.6 Pilot Testing
As the questionnaires were distributed through emails and LinkedIn, therefore, pilot
testing was done to check the level of participants to understand the questions in the
questionnaire. Pilot or pre-testing has been found to be very important in conducting survey
research by many researchers (Hunt, Sparkman and Wilcox, 1982). For the pilot test of the
questionnaire of the following research, the researcher had used the questionnaire for the pre-
survey of five respondents. The pre-testing conducted to measure the outcomes as desired and
then the final questionnaire for the survey was revised based on the outcomes of pilot testing.
4.7 Data Availability and Credibility
After the data collection process, the next step involved data analysis; data analysis
process requires the researcher to arrange raw data in an organised form for conducting data
analysis. The data analysis tools are selected on the basis of research methods employed in the
study (Bendat and Piersol, 2011). For the quantitative data analysis, it was done by using
statistical tools, such as the SPSS software, and MS excel. The results were interpreted on the
basis of the research hypotheses.

29
4.8 Ethical considerations
It is important for researchers to pay attention to the ethical standards and values of the
research and consider them as the highest priority of conducting the research (Creswell, 2013). To
meet the standards of data collection and get accurate and authentic information from the research
participants, all ethical implications were considered that aligned with the standards of the
College of Business, Arts and Social Sciences Research Ethics Committee of Brunel University.
The participants were aware of the research objectives and were required to submit a consent
form to participate in the research on a voluntary basis. They were aware of the standards of
confidentiality and anonymity being followed and were ensured that the information collected for
research would not be used for other purposes. The participants received a copy of the Participant
Information Sheet, which was reviewed and approved by Research Ethics Committee. Thus, this
research was conducted in an ethical manner. The Participant Information Sheet and The
Approval Letter of Ethics Committee can be found in Appendix D and E.
4.9 Summary
The research methodology chapter provides a complete overview of the scientific research
methods used for conducting research. The chapter explained all the essential components of
conducting research. Moreover, the chapter also discussed the ethical considerations of the
research.

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Chapter 5: Findings and Analysis
This chapter reports the results of the data collected based on the analysis. First of all, the
process of administering the questionnaires is provided and then the preparation for data process
is explained based on data coding and cleaning. For the presentation of the data, the statistical
tools and techniques are applied mainly in two categories: descriptive and inferential statistics. In
the statistical presentation, the regression and correlation model is applied that is presented using
multiple charts, tables, graphs and figures. The end of the chapter summarises the results and
findings.
5.1 Data collection and response rate
In the previous chapter of methodology, the approach for the data collection and sampling
was discussed based on the purposive sampling. The response rate can be found seen the table
below.
Table 4: Data collection and response rate
5.2 Evaluation of non-response bias
As the response rate was 84.9%, it was necessary to evaluate non-response bias. Out of a
total of 119 respondents, 84.9% respondents replied completely at different times of survey,
however 15.1% respondents did not fully respond and were subsequently excluded. The
responses at different times were as follows:

31
Figure 9: Responses by day
5.3 Data preparation
It is essential to convert the data collected from the respondents in the field into useful
information to generate results. From the converted results, the research can then identify the
most relevant and important data to test the hypotheses and answer the research questions. For the
data preparation, preparatory procedures were used that included data coding, cleaning and
screening (Aaker, Kumar and George, 2004).
Data coding5.3.1
The first step in data preparation is the coding of data, which includes providing
significant codes for each response of each question in the questionnaire. Numbering codes were
provided to the responses during the data coding process. The coding was actually done during
the designing and developing of the questionnaire, whereby the researchers assigned variable
names, however because of the complexity in the questionnaire construction, reverse coding was
applied to some variables. As Likert-type scales were used for complex variables, reverse coding
was applied such that strongly agree was transposed from value 5 to value 1 of strongly disagree,
and value 4 was transposed to value 2.
Data cleaning and screening5.3.2
As all the coding was done from manual to computer system, the data cleaning and
screening was done to crosscheck the errors and incorrect coding. The process helps in detecting
errors, inconsistencies in responses and missing data prior to conducting analysis.

32
Statistical tools were applied to the data to ensure a complete data set was used in the analysis.
The responses were missing in eight questionnaires for different questions. Based on the
incomplete responses, these questionnaires were excluded from the data analysis to prevent the
errors and impacts on the results, which would undermine generalization.
5.4 Reliability analysis of independent variables
The measurements were subject to reliability testing using on Cronbach’s alpha
coefficient scoring. The rule of thumb is that the Cronbach’s alpha value is only accepted when it
is higher than 0.6 (Hair et al., 2006).
For some variables, the researcher applied the sum of more than one question for
regression and correlation. Therefore, it was very important to test the reliability of the
combination of the responses for single variables. The combination used for the factors included
competition, reliability, perception of strategic value, employee resistance, culture and dependent
variable (adoption of BI). The table below shows that the values of variables range from 0.738 to
0.894, which is greater than 0.6. This means that the values and results generated from them are
acceptable based on the Cronbach’s alpha rule of thumb.
Table 5: Reliability analysis
5.5 Descriptive statistics
During the study and survey, some descriptive features were used that must be explained
in terms of data analysis. Here, the descriptive statistics include respondents’ profile, the
organisational characteristics and the adoption of the BI, as explained in the following sections.

33
Demographic profile of respondents5.5.1
The demographic profile of the respondents can be seen in the table below, where only
three demographic factors were included: work experience, organisation and educational
qualification. Demographic profile of respondents
Table 6: Demographic profile of respondents
Out of the total respondents, 50% had under 1-5 years of working experience with their
organisation category. However, 25% of respondents were under 6-10 years of working
experience category. 21% respondents have more than 10 years’ experience in their organisations,
while only 4% of respondents fell under the less than 1 year category. The graph below shows the
results for working experience.

34
Figure 10: working experience of respondents
Under the category of organisation of the respondents, 36% (the largest group) were from
SIDF, 34% from ELM, 16% from SAMA and 14% from MCI. The graph below shows the results
for organisations.
Figure 11: Organisations of respondents
Under the education qualification category, 47.5% of the respondents possessed a master’s
degree, 47.5% held a bachelor’s degree, 3% had diplomas, 1% had high school graduation, 1%

35
held doctorates and 0% had other education qualifications. Thus the majority of the participants
had a bachelor’s or master’s degree. The graph below shows the results of educational
qualifications.
Figure 12: Qualifications of respondents
Characteristics of responding organisations5.5.2
The information and data found in the characteristics of the organisations from
psychographic questions was further used to differentiate in five level of BI adoption. The most
pertinent characteristics of the organisations concerned how they use computer systems across
different operations and functions.

36
Figure 13: Activities supported by computer systems of respondents
Information Storage System is another characteristic of the organisation that can be seen
in the graph below, where 52% of respondents said that their organisations are using a shared
central database system.
Figure 14: Data storage media of respondents
Application System in the organisations can be seen in the graph below, where 44% of
respondents mentioned that their organisations are using advanced software to detect data
relationships.

37
Figure 15: The level of analytical tools uses by respondents
Proportion of information maturity5.5.3
The table below shows the five levels of information maturity. Different questions were
used to generate the results for five levels in line with the five dimensions. The description of the
five level and dimensions can be found in Appendix B.
Table 7: Proportion of information maturity
It was found that the organisations have a mixture of BI levels of adoption based on the
systems for information, knowledge process, different applications and culture used across their
companies. For instance, some respondents marked that their organisations use computer systems
for marketing as well as for daily operations, while some mentioned that their organisations use

38
manual as well as computer systems for information across the firm. Therefore, it was difficult to
identify the single BI level of each individual organisation.
5.6 Inferential statistics
Inferential statistics, other than demographic statistics, were used to develop the generalised
results, predictions and findings about the responses, attitudes and nature of the entire population
based on the sample responses. For the study at hand, the inferential statistics were mainly based
on the two approaches of simple regression and multiple linear regression. Firstly, multiple linear
regressions were applied to appropriately test the hypotheses in the complex construction model.
Secondly, simple regression was employed for testing the hypotheses developed in the previous
sections to identify the impacts of each factor on the BI adoption, as different independent
variables might have relationship impacts on each other’s results.
Multiple linear regression5.6.1
Multiple linear regression is used to test hypotheses in which the relationship of a
dependent variable is measured with multiple independent variables. The hypotheses of the
following research are tested based on multiple linear regressions in general, although the
correlations of independent variables sometimes impact on the relationship with the dependent
variable, thus the test is conducted using simple linear regression.
Multiple linear regression equation5.6.2
The formula used for simple logistic regression was as follows:
𝐷𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡 (𝐴𝑑𝑜𝑝𝑡𝑖𝑜𝑛)
= 𝛼 + 𝛽(𝐶𝑜𝑚) + 𝛽(𝐷𝑀) + 𝛽(𝐺𝑆) + 𝛽(𝑅𝐸𝐿) + 𝛽(𝐶𝐿𝑋) + 𝛽(𝑂𝐵𝑆) + 𝛽(𝑃𝑆𝑉)
+ 𝛽(𝑆𝑍) + 𝛽(𝐸𝑅) + 𝛽(𝐶𝐿) + 𝛽(𝑀𝐼) + 𝛽(𝐷𝐸) + 𝛽(𝑆𝑅𝐴) + ℮
Where,
𝛼 = 𝑖𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡
β = Regression coefficient for respective variables
*COM= Competition, DM= Demand, GS= Government Support, REL= Reliability, CLX= Complexity, OBS=
Observability, PSV= Perception of Strategic Value, SZ= Size, ER= Employees’ Resistance, CL= Culture, MI=
Managers Innovativeness, DE= Data Environment, SRA= Skilled Resources Availability
*℮ = other factors that might not be discussed or included in the following discussion, but which have an impact on
BI adoption.

39
The table below shows the regression statistics, where adjusted R Square is 0.893, which
means that the model of the study is statistically significant. Further, the significant value of
regression is 0.000, which means the model is significant.
Table 8 : Regression statistics / R Square
Table 9: Regression statistics / ANOVA
The dependent variable in the study was the adoption of BI; therefore, Q8 and Q36 in
combination were used to generate the results with independent factors. The table below shows
the results of regression of BI adoption with the factors. The p-value of each factor in the table
below shows that each independent variable factor has a significant relationship with the
dependent variable; however, the coefficient shows the positive and negative impacts. The
coefficient of competition is -0.097, reliability is -0.060, complexity is -0.118, employees’
resistance is -0.036, managers’ innovativeness is -0.055 and data environment is -0.149. All these
factors have shown negative relationships with BI adoption, while the remaining factors show a
positive relationship.

40
Table 10: Regression results of BI adoption and the factors
Simple linear regression5.6.3
Because of the complexity of the model construct in the research, there are chances of the
impacts of one independent variable on the other independent variables. Therefore, simple linear
regression is applied to evaluate the impacts of each factor (independent) individually on the BI
adoption (dependent). For some factors, the results are different from the multiple regressions, as
explained below.
5.6.3.1 External factors
 Competition
P-value of competition in multiple regressions is 0.332, which means there is no
significant relationship of competition with BI adoption. However, P-value is significant in
simple linear regression; however, the coefficient is 0.802, which shows the positive relationship
of the competition with BI adoption. This means that the impacts and relationships of other
factors impact on the result of competition in the multiple regressions.

41
Table 11: Simple linear regression / competition
 Demand
The model is significant for the individual relationship of demand with BI adoption, as p-
value is 0.02. The simple linear regression has also found the same significant relationship of
demand with BI adoption. However, the coefficient has found a less impact on the demand, as
both regressions show a positive relationship.
Table 12: Simple linear regression / Demand
 Government Support
The p-value of government support is 0.000 in both regressions, which means it is
significant and the coefficient is also positive. This means that the impacts of other factors are
low on government support.
Table 13: Simple linear regression / Government support
5.6.3.2 Technological factors
 Reliability
The table below shows the significant relationship of reliability with BI adoption as the p-
value is 0.000. Furthermore, the coefficient is 0.690 that shows the positive relationship, however
the multiple regressions show no relationship with p-value of 0.300. This means that reliability is
affected by the relationship of other factors.
Table 14: Simple linear regression / Reliability
Coefficients Standard Error t Stat P-value
Intercept 1.00727001 0.200768798 5.017064 0.0000
Competition 0.802214975 0.045633473 17.57953 0.0000
Intercept 1.194767442 0.189730808 6.297172 0.00
Demand 0.750177978 0.042541086 17.6342 0.00
Intercept 0.737541033 0.208886674 3.530818971 0.000631
Government Support 0.847209788 0.04662776 18.16964393 0.00
Intercept 1.574094 0.248130608 6.343812 0.00
Reliability 0.690712 0.057941699 11.92081 0.00

42
 Complexity
The model is significant because of the p-value of 0.00, whereby the relationship of
complexity with other factors was found to highly impact on its relationship, as no relationship
was found in the multiple regression with the p-value of 0.119.
Table 15: Simple linear regression / Complexity
 Observability
The effects were found to be significant as the p-value is 0.000 in the simple regression,
however a positive coefficient value of observability was found in the multiple regression with p-
value of 0.04. This means that the relationship of observability has the least impact on its
relationship with BI adoption.
Table 16: Simple linear regression / Observability
 Perception of Strategic Value
Again, P-value of perception of strategic value is 0.00, which means the model is
significant for the factor, whereby the relationship of other factors has less influence on the
perception of strategic value, as the coefficient is found to be positive with a significant
relationship in multiple regressions, as well with the significance level of 0.001.
Table 17: Simple linear regression / PSV
5.6.3.3 Internal Factors
 Size
The model is significant for size of the organisation with the p-value of 0.00, while the
multiple regressions with significance level of 0.850 indicate no relationship of size with BI
Intercept 5.727831546 0.086355923 66.32818 0.00
Complexity -0.77445723 0.046928834 -16.5028 0.00
Intercept 1.559697533 0.25576201 6.098238 0.00
Observability 0.69169541 0.05955117 11.61514 0.00
Intercept 1.681669059 0.311569715 5.397409 0.00
Perception of Strategic Value 0.662189871 0.072690715 9.10969 0.00

43
adoption. This means the relationship of other factors has a great influence on the magnitude of
the relationship with BI.
Table 18: Simple linear regression / Size
 Employees’ Resistance
The p-value of employees’ resistance is 0.246 (greater than 0.05), which shows that the
model is not as significant as in multiple regression model, with p-value of 0.359. This means that
the relationship of employees’ resistance with other factors is little affected by its relationship
with BI adoption.
Table 19: Simple linear regression / Employees' resistance
 Culture
The model is significant for the culture as its p-value is 0.000. The coefficient value is
0.785, which shows the positive relationship between culture and BI adoption, however different
results were found in the multiple regressions with p-value of 0.120. This means that the
relationship of culture has high impacts on its relation with adoption.
Table 20: Simple linear regression / Culture
 Managers’ Innovativeness
The model is again not significant for managers’ innovativeness as its p-value is 0.052.
The multiple regressions also found no significant effects of managers’ innovativeness on BI
adoption, with a significance level of 0.364. This means that the relationship of managers’
innovativeness has limited impacts on its relation with adoption.
Intercept 1.42403525 0.302308802 4.710532 0.00
Size 0.69925555 0.068243243 10.24652 0.00
Intercept 4.064798764 0.356733483 11.3945 0.00
Employees’ Resistance 0.119752412 0.102791788 1.165 0.246819
Intercept 1.055281848 0.190561075 5.537762 0.00
Culture 0.785686864 0.042979359 18.28056 0.00

44
Table 21: Simple linear regression / Managers' innovativeness
 Data Environment
The p-value for data environment is 0.000, which means the model is significant. The
coefficient value in the table below is -0.679, which shows the negative relationship of the data
environment of the organisation with BI adoption. The same results were found in the multiple
regressions with p-value of 0.001, with little difference in the coefficient value.
Table 22: Simple linear regression / Data Environment
 Skilled Resources availability
For the last factor also, p-value is less than 0.5, which means that the model is significant
for the skilled resources variable, and the relationship was found to be positive in both
regressions. This means that the relationship of skilled resources availability with other individual
variables has less influence on its relationship with BI adoption.
Table 23:Simple linear regression / Skilled resources availability
A table of the relationships between independent variables can be seen in Appendix A.
5.7 Hypotheses testing
The significance of the BI adoption effecting factors is analysed based on the findings and
results of the ratios and values found in the multiple regression model. The hypotheses were
tested based on three major categories of the factors in the theoretical research framework.
External factors5.7.1
 H1: Competition positively affects BI adoption in Saudi organisations
The significant value of 0.332 in the regression of competition indicates no impacts of
competition on the BI adoption. Further, the negative value of coefficient in the simple linear
Intercept 1.585725769 0.232588029 6.817745 0.00
Managers Innovativeness 0.666685806 0.052570815 12.68167 0.00
Intercept 5.579838916 0.108325611 51.50988 0.00
Data Environment -0.67917412 0.058442273 -11.6213 0.00
Intercept 1.21377749 0.318289196 3.813442 0.00
Skilled Resources Availability 0.72606727 0.069919445 10.38434 0.00

45
regression did not substantiate hypothesis 1. However, this was not tested across the
organisations, therefore the results can be generalized as the competition in the market has no
impacts on the adoption of BI in government agencies in Saudi Arabia.
Although hypothesis 1 is not supported, Boonsiritomachai, McGrath and Burgess (2014)
found that competitive pressure in the market has a direct impact on the BI adoption, however
their study was conducted in context of SMEs in Thailand. Numerous studies have found that
competition in the market leads organisations to look for new technologies and ways of
conducting business (Ifinedo, 2011). Similarly, Waarts, Everdingen and Hillegerberg (2002)
found the high impacts of competition on the adoption of the technology. No study has found yet
that competition has no significant impact on BI adoption; however, no study has yet conducted
to find the factors affecting BI adoption in Saudi Arabia. Therefore, it can be said that Saudi
government organisations do not consider competition to be important in BI adoption.
 H2: Demand positively affects BI adoption in Saudi organisations
Based on the likelihood test, demand was found to be a significant factor (0.021). The
hypothesis was to test the positive effect on BI adoption, which is supported as the coefficient
value is 0.178, which means hypothesis 2 is accepted, and demand for BI in the market affects its
adoption.
Boonsiritomachai, McGrath and Burgess (2014) also found that demand for BI is
increasing both in large and small organisations. This means that the demand is a positive factor
for the adoption of BI. Another study stated that the decision for technology adoption is mainly
based on the demand side (Hall and Khan, 2003). The results of this study thus corroborate the
findings of previous researchers.
 H3: Government Support positively affects BI adoption in Saudi organisations
The regression likelihood test found that government support has a significant relationship
with BI adoption, based on the significant value of 0.000 that is less than 0.05. The research was
aimed to test the positive effect, and the coefficient value in the regression is 0.313, which means
that government support has a positive impact on BI adoption in Saudi organisation and
Hypothesis 3 is supported.
Coad et al. (2014) found that government support is very important for the adoption of
new technology as it supports firms in terms of training, regulations, technical support and policy

46
intervention. On the other hand, Puklavec, Oiliveria and Popovic (2014) found that government
support has no such impact on BI adoption. It is possible that impacts might be different across
industries, but it can be said that government support has a positive role in BI adoption in Saudi
Arabia.
Technological factors5.7.2
 H4: Reliability of BI positively affects its adoption in Saudi organisations
The likelihood test of found that BI reliability has no significant relationship with its
adoption, as the significance level was 0.300, which is greater than 0.05. Thus hypothesis 4 is not
supported. The coefficient relationship is negative, with -0.06, confirming the findings of Buttler
and Sellbom (2002).
 H5: Complexity of BI negatively affects its adoption in Saudi organisations
The linear regression test found a significance level of 0.119 for this item, indicating that
there is no significant relationship between BI complexity and its adoption. Hypothesis 5 is
supported as the relationship is either positive or negative, once the relationship has been
accepted as significant. The same conclusion was reached by Boonsiritomachai, McGrath and
Burgess (2014).
 H6: Observability of BI positively affects its adoption in Saudi organisations
The likelihood test found a significance level of 0.046, indicating the significant
relationship between observability of technology and BI adoption. Furthermore, the relationship
was found to be positive as the coefficient value is 0.104, therefore hypothesis 6 is supported.
Boonsiritomachai, McGrath and Burgess (2014) also found the same results.
 H7: Perception of Strategic Value positively affects BI adoption in Saudi organisations
The regression likelihood test found that perception of strategic value has a significant
relationship with BI adoption, based on the significance value of 0.0013, which is less than 0.05.
The coefficient value in the regression is 0.143, which means that the perception of strategic
value has a positive impact on BI adoption in Saudi organisation and Hypothesis 7 is supported,
supporting the findings of Puklavec, Oiliveria and Popovic (2014).

47
Internal factors5.7.3
 H8: Organisation’s size positively affects BI adoption in Saudi organisations
The likelihood test of the factor has found that the size has no significant relationship with
reliability as the significant level is 0.850. The hypothesis 8 is not supported by the test. In simple
linear regression it was found to have a significant relationship (0.00), which means that in effect
with other factors, the relationship of size with BI adoption is being influenced. Kumar,
Maheshwari and Kumar (2002) found that size is an important factor for affecting technology
adoption.
 H9: Employees’ Resistance negatively affects BI adoption in Saudi organisations
The significant value of 0.359 in the regression of competition shows that there is no
relation of competition with BI adoption and hypothesis 9 is not substantiated, although in simple
linear regression it was found that it has a significant relationship (0.00), this means in effect with
other factors, the relationship of employees’ resistance with BI adoption is influenced.
MacGregor, Waugh and Bunker (1996) found a relationship between impacts of employee
resistance and technology adoption.
 H10: Organisation’s Culture positively affects BI adoption in Saudi organisations
The significant value of 0.120 in the regression of culture shows no impacts of culture of
organisation on the BI adoption and not substantiated hypothesis 10. Although it is not tested
across the organisations, the results can be generalised as the culture has no impacts on the
adoption of BI in the Saudi organisations. Puklavec, Oiliveria and Popovic (2014) found that
culture impacts the technology adoption, and Fink (1998) also found the factor to be impactful.
 H11: Innovativeness of Managers positively affects BI adoption in Saudi organisations
The likelihood test of the factor has found that the innovativeness of managers has a
significant relationship with reliability (0.364), thus hypothesis 11 is not supported and
innovativeness of managers has no relationship and impacts on BI adoption in Saudi
organisations, in contrast to previous literature which found the manager’s role to be very
important (Ghobakhloo, Arias-Aranda and Benitez-Amado, 2011).
 H12: Poor Data Quality negatively affects BI adoption in Saudi organisations
The regression likelihood test found that perception of data quality has a significant
relationship with BI adoption, based on the significant value of 0.001, less than 0.05. The

48
coefficient value in the regression is -.0149, which means that poor data quality has a negative
impact on BI adoption in Saudi organisation and hypothesis 12 is thus supported. This confirms
the findings of Chaudhuri, Dayal and Narasayya (2011).
 H13: Lack of Skilled Resources negatively affects BI adoption in Saudi organisations
The significant value of 0.043 in the regression of lack of skilled resources shows it has
impacts on the BI adoption. However, the positive value of coefficient 0.104 in the simple linear
regression shows that the availability of skilled resources is important for successful adoption of
BI. This means that the lack of skilled resources negatively impacts on BI adoption in Saudi
organisation. Hence, the hypothesis 13 is supported. However, Boonsiritomachai, McGrath and
Burgess (2014) stated that skilled human capital is an important factor for BI adoption, which
means the literature supports this result.
Table 24: Summary of hypotheses testing

49
5.8 Summary
The chapter analysed results from the data to generate findings using the methods and
techniques described in the previous chapters. The chapter began by processing the data from the
questionnaires to change into useful information for testing hypotheses.
The results were divided into two sections related to demographics and inferential
statistics, discussing demographic and psychographic questions and the regression models are
used to test the relationship of the factors affecting BI adoption (dependent variable),
respectively. Based on the findings, it was confirmed that six out of 13 factors have a significant
relationship, as developed in the hypotheses, including demand, government support,
observability, perception of strategic value, poor data quality and lack of skilled resources. While
for the remaining factors the study has found no relationship with BI adoption. The next section
concludes the results.

Investigating the factors influencing Business Intelligence adoption A case of Saudi Arabia

Investigating the factors influencing Business Intelligence adoption A case of Saudi Arabia

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