Research Methodology for IT-based MCS in Saudi Oil and Petrochemical Industry

This research evaluates the impact of IT-based MCS on managerial teams in Saudi Oil and Petrochemical Industry. The study employs a modified version of UTAUT framework to evaluate the impact of proposed IT-based MCS for three oil and petrochemical companies in Saudi Arabia. The study aims to establish whether the adoption of IT in management control practices can or has improved the companies’ effective management of remote sites.

Running head: RESEARCH METHODOLOGY
Chapter 3: Research Methodology
Name of the Student:
Name of the University:

1 RESEARCH METHODOLOGY
3. RESEARCH METHODOLOGY
3.1 Introduction
This research will evaluate how the implementation of IT-based MCS affects the
overall performance of managerial teams in large Saudi Oil and Petrochemical Industry. To
achieve the research objectives of this study, we have employed a slightly modified version
of UTAUT (Unified Theory of Acceptance and Use of Technology) theoretical framework to
evaluate the impact of proposed IT-based MCS for three oil and petrochemical companies in
Saudi Arabia namely American Halliburton, Saudi SABIC, and Saudi ARAMCO. These three
companies will be used as a benchmark to investigate the behavioural intentions of the
intended users towards adoption and usage of IT-based MCS in Saudi Arabian oil and
petrochemical companies. Moreover, this study also analyzes the implementation and
benefits of IT-based MCS utilized by three large oil and petrochemical companies operating
in the Kingdom of Saudi Arabia (KSA) and it also seeks to establish whether the adoption of
IT in management control practices can or has improved the companies’ effective
management of remote sites. This study will contribute to enhance the understanding of the
latent problems experienced by the companies in the management of remote sites, and
provide a framework to further explore the mitigation strategies that can be implemented
using IT adoption and MCS to help improve the management practices in this sector. In other
words, this study is intended to evaluate technological acceptance in Saudi Arabia. This
research chose the case study approach in order to accommodate the dynamic view of social
reality and allocated greater emphasis on the behavioral intentions to use Information
Technology (IT), in the context of adopting and implementing IT-MCS in the little researched
Saudi Oil and Petrochemical Industry.
It is important to note that a number of IT studies have been conducted using the
UTAUT framework with contrasting research objectives. A wide-variety of research
methodologies have been applied on different and diverse subjects of focus with convergence
on factors that influence the behavioral intentions towards adoption and usage of
organizational IT systems by users across the globe. With gradually expanding literature in
the area, a number of new variables have been plugged into the original UTAUT model to see
the impact on behavioral intentions and use behavior as the two response variables. On
numerous occasions, the author found that UTAUT model has been merged with other
comparable models to incisively study the influencers of technology acceptance. An in-depth

2 RESEARCH METHODOLOGY
examination of several studies further revealed that several researchers have gone to the
extent of re-specifying the operational relationships between the UTAUT model constructs,
the moderator variables and the controls. These developments signal an increasing amount of
attention being allocated to probe the influencers that significantly predict the behavioral
intentions of users’ towards adoption and usage of information technology systems
predominantly in the organizational context. The implications of such research studies could
bring a paradigm shift in the way in which the information technology and information
systems are designed by the IT companies so as to facilitate increased adoption and use by
the intended users primarily the organizational users. This is a similar study that aims to
launch a probe into potentially significant influencers and moderators of IT adoption and use
behavior.
The research questions identified in chapter 1 will be investigated with key focus on
the existing control variables that uniquely influence each of the three companies’ IT-MCS
framework and the extent to which each of these companies have integrated IT practices in
management control. In the previous section, the author examined the past and current
literature available on the adoption and implementation of IT-based MCSs with specific focus
on examining the major predictors of behavioral intentions towards adopting and use
behavior associated with users’ of IT systems in the Oil and Petrochemical sector with in the
Saudi industry. Moreover, the author referred to a large number of contextually relevant and
comparable IT studies to investigate about the types of relationships between the
hypothesized constructs, moderator variables, and the control variables as reported in these
studies. The author also localized attention on the specifics of methodology employed by
each of the studies so referred and their limitations, respectively.
The author also noted that the major difference in every research is the extent of the
controls measured. Another important factor to note is that all research projects are conducted
from a particular perspective and each research tackles a problem from a certain point of
view since reality is a social construct where all the players have diverse perceptions of
circumstances and events. A managerial perspective will be the obvious choice for the current
study since it is focused on the implementation of IT-based MCSs to effectively manage and
control the Oil and Petrochemical industry in the Kingdom of Saudi Arabia which is a
developing economy. Effective managerial control is often seen as a key component to
achieve higher organizational performance. Although, management control functions are
“routinized” and formalized in large organizations, they are rather informal in small

3 RESEARCH METHODOLOGY
organizations. Consequently, MCSs in large organizations are generally acknowledged as
extremely critical for monitoring and regulating organizational activities in accordance to the
laid down plans and procedures. A significant role is played by the governance practices and
prevailing circumstances of every organization in moderating the principles of management
control. This makes it virtually impossible to employ a conclusive or ultimate IT-based MCS
that would fit all companies, especially in the petroleum and petrochemical Industry. It is the
responsibility of the senior management team to establish the goals and decide on the nature
of tasks that are required to achieve these goals. This fundamental control of the management
function assures the effective and efficient implementation of a company’s strategies
including the development of a framework to integrate the IT based MCS in the management.
3.2 Objectives of the Research Methodology
This study analyzes the implementation and benefits of IT-based MCS utilized by
three large oil and petrochemical companies operating in the Kingdom of Saudi Arabia
(KSA) and it also seeks to establish whether the adoption of IT in management control
practices can or has improved the companies’ effective management of remote sites. It is
imperative to note that the evaluation of IT-MCS alone is very insufficient because the
control function is closely tied to organizational planning and goal characteristics. This means
that MCS choices are largely influenced by a company’s goals and planning mechanisms.
Therefore, other organizational functions should be considered in their actual context in order
to justify the findings of this research.
In order to operationalize this research, the researcher objectively viewed the aims,
objectives and purpose of this study, in tandem to, the hypothesized constructs, moderating
and control variables. This research study purports to study the impact of behavioral
intentions of the organizational users of IT-MCS (employees, middle and senior-level
management) on the IT adoption and usage This study will contribute to enhance the
understanding of the latent problems experienced by the companies in the management of
remote sites, and provide a framework to further explore the mitigation strategies that can be
implemented using IT adoption and MCS to help improve the management practices in this
sector.
The behavioral component of organizational IT-MCS users is incorporated into the
four hypothesized constructs of this study viz. performance expectancy, effort expectancy,
social influence and facilitating conditions. The moderating variables which are namely

4 RESEARCH METHODOLOGY
gender, experience, and level of users’ education, geographic location and governmental
policy moderate the influence of behavioral intentions of organizational IT-MCS users on IT
adoption and usage. This study employs non-experimental and quantitative research design.
The proposed research design is non-experimental because the researcher has made no
changes in the behavioral constructs of organization IT-MCS users. According to Leedy &
Ormrod (2010), quantitative research design allows for drawing empirical conclusions based
on statistical testing of quantitative data with valid, consistent and reliable estimates (Leedy
& Ormrod, 2010). In light of that justification, quantitative research design was employed in
context of this study. Appropriate research tools will be applied to achieve the objectives of
this research.
3.3 Sample Selection
Not much work has been done in the direction of exploiting information technology
as a tool to further advance the oil and petrochemical industry in the region. The amount of
past IT literature and research work in this Saudi Arabian region is still sparse. Moreover, not
much secondary data is available on the subject of technology acceptance in the oil and
petrochemical industry of Saudi Arabia. This study is one of the several attempts to explain
the adoption of technology in context of Saudi Arabia.
The study population will be selected equally from all the three study companies to
lend reliability to the research. Key considerations will be placed on the cultural background
and current organizational or business practices in Saudi Arabia and how they affect the
adoption of IT processes in the design of MCS specifically in the oil and petrochemical
industry. The respondents of this study include the employees, supervisors, middle and top-
level managers from three companies viz. Halliburton, Saudi SABIC, and Saudi ARAMCO.
The sample comprises of 384 respondents with 128 respondents from each company. Of
the total respondents, 80 per cent of the respondents will collectively comprise of supervisors,
middle-level managers and employees who either use, or are connected with use of,
information technology and information systems on a routine basis. Remaining 20 per cent
will collectively comprise of top-level managers who are responsible for overseeing the
implementation and use of new information technology and information systems in the
chosen oil and petroleum companies. The reason for choosing a higher proportion of
company supervisors, middle-level managers and employees is, in light of, the plausible
presumption that the exposure, and manpower, of operational and tactical levels in any given

5 RESEARCH METHODOLOGY
organization is higher than the top-level management. The respondents of this study have
been chosen from company headquarters, branch offices and offshore project locations of
each of the three companies. This diversity within the samples is expected to further enhance
the quality and reliability of research findings with respect to the behavioral intention of
industrial IT-users’ towards adoption and usage of information technology and information
users.
In order to decide the sample size, first, the researcher constructed a formula to find n
where the population estimate was not defined. To come up with an appropriate sample
population size, the researcher decided to use a confidence interval of 95% with a margin of
error of 5% for p. The researcher also used ṕ = 0.5 in order to derive a conservative estimate
where the population size was not available, and this led to the following algebraic equation:
n = ( z∗¿
M ¿)2 ṕ (1− ṕ)
Where ṕ = population proportion M = margin of error Z* = z-score
To find the z-score we divide the confidence interval by 2 then look it up on a z-table:
0.95/2 = 0.475 which gives us a z-score value of 1.960
n = ( 1.960
0.05 ) 2 (0.5) (1 − 0.5) = 384.16
By rounding off to the nearest whole number we end up with a sample population size
of at least 384 participants across the three selected companies. This basically means that the
researcher will be required to gather data from approximately 128 employees in each of our
three case study Saudi oil and petrochemical companies.
The second method used the Slovin’s formula to find out a suitable approximation of
the sample from a population size estimated at 35, 000 employees in managerial or
supervisory positions in the three case study oil and petrochemical companies and from all
branches and/or remote sites across Saudi Arabia. The Slovin’s formula is given by:
n =
Where N = estimated population size e = accepted error margin
Therefore, substituting our known values into the equation we get: N = 35000 e = 5% or
0.05 (alpha level) with a confidence interval of 95%.
N
(1 + Ne2)

6 RESEARCH METHODOLOGY
n =
n = = 395.48 which can be rounded off to 396
For the third estimate, the researcher used FlexMR calculator and the Checkmarket
sample size calculator to determine the sample size. The results in both calculators were
identical. With the Chackmate calculator, the researcher used an estimated population size of
35, 000, a confidence level of 95%, an estimated response rate of 50%, and an error margin
of 5%. The results showed that this study needed 380 participants, with an approximated
number of participants to invite being at 760 in order to accommodate the response rate value
of 50%. On the other hand, the FlexMR sample size calculator required fewer inputs but
yielded the same sample size estimation. With a population size of 35, 000, an error margin of
5%, and a confidence level of 95%, the FlexMR calculator also estimated the sample size
needed as 380 participants. To come up with a uniform sample size, the mean sample size
from all three sample estimation tools was calculated to end up with:
n = (380 + 380 + 396 + 386) / 4 = 385
This means that the study needed approximately 128 participants from each case
study company. A survey questionnaires (Appendix A) will be prepared to be distributed to
the n = 384 selected participants.
Specific emphases will be placed on the fact that (1) the Kingdom of Saudi Arabia has
a unique and deeply rooted Islam-driven culture that will greatly influence the results
obtained from this exercise from all socioeconomic aspects, (2) Saudi Arabia’s leadership is
based on a monarchy rule with the King as the head of the royal family which partly
influences company management styles as most of the public sector companies are owned by
the royal family especially the oil industry where the royal family is the sole regulator of
policies and accepted practices, and (3) the researcher will need to travel to remote sites
which may affect the project’s timeline in unforeseen ways. The different types of
assessments used for this study will include statistical evaluation, input and process analysis,
outcome evaluation, sample survey evaluation, and lastly, a subjective analysis. These
35000
(1 + (35000 * 0.052)
35000
(1 + 87.5)

7 RESEARCH METHODOLOGY
identified measures will focus on different aspects of each of the three companies selected for
examination and comparison in this paper. The amount and type of information gathered
during this research will be quantifiable to enable us to form logical opinions and mull over
the quantitative data format in various ways to provide concentrated information - and
subsequently a lucid picture - from the measured aspects. The categories of measurements
used in this study will include fundamental measurements, derived measurements and fit
measurements.
3.4 Data Collection
The orientation of this study is quantitative. Moreover, a quantitative study is one in
which data so collected is parametric, which is to say that, parameters such as “mean” and
“standard-deviation” are present. Both empirical and theoretical information relevant to this
subject matter will be collected for consideration. Nowduri et al. (2015) conducted a meta-
analysis of relevant research studies undertaken to explore technology acceptance and the
behavior of users towards new information technology and information systems. Of the total
174 studies, 102 studies used quantitative data to explain the phenomenon of technology
adoption in different cultural and geographical contexts (Nowduri, 2015). A quantitative
analysis proved to be more meaningful in testing the complex interrelationship between the
identified research model elements and the three organizations involved in this study. The
collected data will be used to compare the adoption of IT-based MCS between joint venture
companies (here represented by American Halliburton), and Saudi owned companies –
ARAMCO and SABIC. The analysis will also emphasize on the complexity of adopting IT-
based MCSs and how managers use these control systems. This study used a non-
experimental quantitative research design. The study was non-experimental because no
attempt was made to change behavior or conditions; the aim was to establish relationships
among study variables under existing conditions. The quantitative research design was
appropriate for the primary analysis because study variables and covariates were well
defined, numeric, and measurable at a reasonable cost ensuring generalizable results with
high level of validity and reliability (Leedy & Ormrod, 2010). Quantitative data has many
operational advantages over qualitative data. Through application of statistical tools, breadth
of analysis can be performed on quantitative data to explore technology acceptance in context
of Saudi Arabian oil and petrochemical industry. Alike the past studies referred to in the
previous section, this study employs the survey methodology to investigate the significant
predictors of behavioral intentions of industrial users’ towards adoption of IT-MCSs and

8 RESEARCH METHODOLOGY
corresponding use-behavior. The researcher’s personal views and values will also be a key
requirement in the data collection and interpretation process. The neutrality of the researcher
is also of paramount importance especially when drawing conclusions and making
comparisons because the reliability and validity of this research will greatly depend on this
simple factor.
3.5 Research Instrument
Survey methodology will be implemented through survey questionnaires as the
research instruments. A single research instrument has been prepared and questions relevant
to each of the construct: performance expectancy, effort expectancy, social influence and
facilitating conditions have been inserted. The research questionnaire also carries relevant
questions to incorporate the effect of five moderator variables which namely are gender of the
respondents, experience of the respondents, level of user (respondent) education, geographic
location of the respondent, and governmental policy environment. Due care was taken while
framing the questionnaire to ensure that the survey produces desired data. Moreover, all the
questions were tested for criteria validity, content validity and context validity. The research
questionnaire was constructed using 7-point Likert scale with “1” indicating “strongly
disagree”, “4” indicating “neither agree nor disagree” and “7” indicating “strongly agree”
(Venkatesh et al. 2003).
3.6 Assumptions
It was assumed that while some managers made use of informal communication
channels and focused on non-financial measures, some of the managers concentrated on
formal communication channels and focused on financial measures to weigh performance.
These systems use internet-based, satellite or wireless mobile and stationary tools in a
network that helps our case study oil companies in the management of increasingly complex
processes. Therefore, some employees across the different managerial positions will be asked
similar questions which acted as a cross check on the diversified responses or views on the
adoption of IT-based MCS.
3.7 Justification of the Research Methodology
Technology acceptance has been widely researched across the globe. Presently, every
industry is driven by technology. Large-scale oil prospecting and extraction companies
heavily rely on technology for multifarious purposes. In the previous chapter, the researcher

9 RESEARCH METHODOLOGY
conducted a literature review of a large number of past studies on technology acceptance in
both global and local (Saudi Arabia) contexts. However, technology acceptance has been very
thinly researched in context of Saudi Arabia and amount of existing literature is sparse. The
aforementioned research methodology can be utilized to understand the different predictors of
IT adoption and usage. The researcher employs the theoretical framework of “Unified Theory
of Acceptance and Use of Technology” (UTAUT) model which has been empirically found to
explain a substantial portion of variations in the response variable (IT adoption and use). In
other words, the UTAUT model is a robust model that comprehensively explains the
significant determinants (behavioral and other) of technology acceptance. It is also clear from
the literature review chapter that a large number of researchers have historically applied the
UTAUT model to expand their understanding of technology acceptance in different
geographical contexts.
Galliers and Leidner (2014) suggests that organizations may be compelled by external
factors to adopt IT-based MCS, a concept generally known as the legitimizing concept
(Galliers & Leidner, 2014). In contrast, such adoption could also be driven by reactive factors
such as project failures, cost and time overruns, inefficient IT systems to cope-up with the
scale and scope of the projects etc. Broadly, factors influencing the adoption of IT-based
management control systems by large-scale organizations such as oil and petrochemical
companies of Saudi can be categorized into “human-human interaction” and “human-
technology interaction”. This study incorporates the effect of both types of interactions.
Human-technology interactions are incorporated in hypothesized constructs of “performance
expectancy” and “effort expectancy” while human-human interactions are incorporated in the
hypothesized construct of “social influence”.
3.8 Description of Variables
Through our new UTAUT theoretical framework, this study includes four major
constructs drawn from variables presented by Venkatesh et al., in their 2003 study. These four
key elements namely, Effort Expectancy, Performance Expectancy, Social Influence and
Facilitating Conditions, and have an impact on Behavioral Intentions and the eventual
acceptance and use of adopted IT-based MCS. The current research will also evaluate the
impact of four key independent variables used by Venkatesh et al. (2003) on two dependent
variables namely Behavioral Intentions and Usage behavior of the managers and employees
in Saudi SABIC and Saudi ARAMCO, and how they compare to American Halliburton while

10 RESEARCH METHODOLOGY
also analysing how the external constituencies defined by gender, experience, level of
education, governmental Policies and geographic locations (moderating variables) identified
for this study influence IT-based MCS in an emerging economy like Saudi Arabia.
On the issue of Social Influence and Performance Expectancy, it is worth to mention
at this point that our primary focus will not be on the concept of organizational culture but
rather on the social and national culture of Saudi Arabia. For the purpose of this study, the
concept of Social Culture is taken to represent the set of values and norms which are brought
to the workplace by workers and managers, rather than the values and norms developed by
workers and managers in the workplace. According to Chenhall, (2003) the management style
in any organization is influenced by the business type, as well as the organization’s age, and
size. On the other hand, the effects that management styles have on control were analyzed by
Cammann (1976) to examine the need for congruence between management style and IT-
based control systems. Cammann (1976) determined that control systems had some level of
influence on the way employees behaved at the workplace. Secondly, they were able to
establish that the way employees responded to control systems largely depended how
managers used the system in place.
3.9 Operationalizing Study Variables
Table 3.1 summarizes the variable operationalization of the
dependent and independent variables, and potential covariates for this
study.
Table 3.1: Variables, Variable Type, Scales of Measurement and Operationalization
Name of
Variable
Variable Type Scale of
Measurement
Source
Performance
Expectancy
Independent
variable/input variable
Ordinal Survey
Effort
Expectancy
Independent
variable/input variable
Ordinal Survey
Social
Influence
Independent
variable/input variable
Ordinal Survey

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Facilitating
Conditions
Independent
variable/input variable
Ordinal Survey
Behavioral
Intentions
Output/response
variable
Ordinal Survey
Usage
Behavior
Output/response
variable
Ordinal Survey
Gender Moderator variable Nominal Survey
Experience Moderator variable Interval Survey
Geographic
Location
Moderator variable Nominal Survey
Level of
Education
Moderator variable Ordinal Survey
Government
Policy
Moderator variable Nominal Survey
3.10 The Interrelationship of Research Variables
UTAUT theory is considered as theoretical considerations for understanding the
behavioural intention of the users to implement and benefit IT based MCS which is to be
utilized by three of the large companies such as oil and petrochemical companies operating in
the Kingdom of Saudi Arabia. Increased adoption of IT based MCS has significant
implications for reducing cost as well as operations of the petrochemical companies. The data
suggested that a strong relationships is existed among UTAUT as well as behavioural
intention to implement of IT into the organizations. The aim of this section is to analyzed and
identify the external variables, theories as well as relationship of outer variables with
independent as well as dependent hypotheses of UTAUT. Cheng, Liu and Qian (2008) stated

12 RESEARCH METHODOLOGY
that it is required to understand interrelationship between the variables for drawing of
conclusion from the statistical analysis. In an attempt to bridge gap into the research study,
UTAUT model is adopted as well as utilized to evaluate of linear as well as non-linear
relationships founded on UTAUT model.
Into the UTAUT model, the construct performance expectancy is well-defined by way
of degree to which the distinct is believed that use of IT system will allow the users to attain
into the job performance. This paradigm explains that past to accept of new technology, the
individual project benefits to gain into the event using the technology with respect to job
presentation besides private improvements towards discharge tasks before creating of the last
decisions of purpose to be used. Authors those had adopted this model confirmed this
relationship.
Second construct which is hypothesized into the model is effort expectancy. It
explained to such extent the degree of effortlessness linked with practise of new IT system.
The constructors of this model had planned that influence of this construct is felt at original
stages of learning management system technical innovations, where presentation of new
skills are to be compulsory. Suhendra et al., (2009) confirmed about effects of effort
expectancy on the behavioural intentions.
Apart from this performance and effort expectancy, social influence construct is also
added which has a direct relationship through the behavioural intention. Sundara vej (2009)
stated that social influence is such an extent where the new users are perceived and believed
that they should use of new IT system and technology. ZHOU, Lu and Wang (2010) believed
that while effective and easier use of information system, the end operators are not pleased to
use of system till they all are interested to use it which can influence their attitude besides
behaviour. Thompson and Higgins (1991) emphasized that use of information system is an
optimistic feeling from the users to usage of the system. Belongings of social influence on the
purposes are non-significant in former studies.
The construct facilitating conditions is being described as a degree to which the
discrete are believed an organizational as well as practical infrastructure are existed to
maintenance for usage of information system.
The presentation of new advancements excites certain instabilities along with
anxieties. Keeping in mind the conclusion area to hurdle the psychological instability and
working dissatisfactions, people expect that associations will give the required help to
facilitate the utilization of the framework. In this way, ICT framework made accessible and
solid while institutional approaches exhibit openings and motivating forces for utilization of

13 RESEARCH METHODOLOGY
Performance
expectancy
Effort
expectancy
Social
influence
Facilitating
conditions
Behavioral
intentions Usage
behavior
Gender Experienc
e
Geographic
location
Level of
education
Government
of policy
education
information system innovation impact the utilization by guides. The connection of
significance to these favourable or generally situations creates an immediate impact between
facilitating conditions in addition utilization and not social goal.
In spite of the fact that as not to have a shortest association with the behavioural
intention, Gardner et al., (2000) opined that the underlying impression of understudies on the
accessibility of help administrations and assets to convey portable learning and the other way
around, will to a more prominent degree impact their choice to embrace and in this manner
utilize of information system for business functions. Different creators have hence
demonstrated that earlier learning of encouraging conditions trigger earlier goals to use
instead to use as opposed to the actual usage.
Figure 3.1: Unified theory of acceptance and use of technology
3.11 Measures of Performance
The construction of performance measures is aimed at encouraging appropriate
behavior that ensures the achievement of a company’s goals. There are non-financial
performance measures such as task performance skills, or positive IT adoption practices, and
financial oriented performance measures such as budgeting or profits. This study is primarily
concerned with the evaluation of managers and the adoption of IT-based MCS. It is however

14 RESEARCH METHODOLOGY
important to note that while joint venture companies in Saudi Arabia may link physical
targets with budgets and encourage the achievement of these targets at the minimum possible
cost, Saudi-owned and managed companies rarely use financial measures to gauge the
managers’ performance. On the contrary, instead of using budgets as a management control
device, Saudi companies use budget for expenditure control. Grouping of performance
measure can be simplified to the following categories: (These categories are sometimes
company specific)
 Quality
 Effectiveness – Is the right thing being done?
 Safety
 Efficiency – Are things being done right?
 Productivity
 Timeliness of delivery
3.12 Selection of Data Collection, Method and Research Design
This research will seek to obtain a realistic picture of the subject matter therefore the
primary data will be collected through email questionnaires because the respondents will be
able to give direct and fast responses to the research questions after taking their time to
consider the questions and their answers comprehensively. Conducting survey through email
will also ensure that misquotations are avoided. Expected disadvantages include the lack of
an opportunity to receive immediate responses where cases of unclear information from the
respondents arise. All of the respondents will receive the same questions in order to
standardize the research process and authenticate the variation in submitted answers. A
combination of convenience and selective (purposive) sampling was used in this research for
two major reasons; (1) given that random sampling of the entire population was neither
economical nor practical, the research chose convenience sampling to select the best
representative study population for better control of the research, and (2) members of the
study population were expected to share similar characteristics to standardize the feedback
and results therefore purposive or selective sampling was also found to be equally appropriate
for the research. Lastly, since there was no attempt to change conditions or behavior, this
study used a non-experimental quantitative research design approach with the aim of
establishing the different study variable relationships under the prevailing state of affairs. In
order to generate results that are generalizable but still maintain high levels of reliability and

15 RESEARCH METHODOLOGY
validity, this research found the quantitative methodology to be more preferable compared to
qualitative research designs. The major reason behind this preference is because all the
identified study covariates and variables are not only measurable at a reasonable cost, but
also numeric and properly defined. The qualitative part of the research focuses on the
researcher’s own direct observation of the respondents’ feedback to reinforce the information
collected through the questionnaires.
To measure the dimensions of BI (Behavioral Intentions), this study will consider the
following subscales; Performance Expectancy, Effort Expectancy, Social Influence, and
Facilitating Conditions. Each construct will be evaluated through a series of questions, table
3.2 gives a breakdown of the four UTAUT constructs and behavioral intentions reworded to
this study.
Table 3.2: UTAUT Survey
Variable Questionnaires Source
Performance
Expectancy H1
The IT-based MCS is useful for the
company
Venkatesh 2003
Using the IT-based MCS enables me to
accomplish goals more quickly
Using the IT-based MCS increases
productivity
Use of the IT-based MCS will increase the
probability of increasing my
compensation
Interaction with the IT-based MCS is clear
and understandable
It is easy for managers to become skilful

16 RESEARCH METHODOLOGY
Effort Expectancy
H2
at using the IT-based MCS Venkatesh 2003
Management find the IT-based MCS easy
to use
Learning to operate the IT-based MCS is
easy
Social Influence H3
People who influence my behavior think
that we should use the IT-based MCS
Venkatesh 2003
People who are important to me think
that we should use the IT-based MCS
Other senior management of the oil
industry will be (are) helpful in the use of
the IT-based MCS
In general, the organization supports the
use of the IT-based MCS
Facilitating
Conditions
H4
The resources necessary to use the IT-
based MCS are available
Venkatesh 2003
The management has the knowledge
necessary to use the IT-based MCS
The IT-based MCS is compatible with
other systems
A specific person (or group) is available

17 RESEARCH METHODOLOGY
for assistance with system difficulties
Behavioral Intention
H5
I plan to use IT-based MCS for the rest of
my employment
Venkatesh 2003
I intend to use IT-based MCS in my
future management activities
I would use IT-based MCS to improve
my efficiency in Management
Source: Venkatesh, 2003
Birth and Irvine (2009) argued that variables of gender moderated relationship among
independent as well as dependent variables. Gender will moderate relationship among
performance expectancy as well as user behavioural intentions. It will also reasonable
relationship among effort expectancy in addition user behavioural intentions. Gender will
reasonable relationship between social behaviour besides user behavior. The effect of gender
moderator variable will be evaluated through a series questions reworded to this study in
table 3.3 (Venkatesh, 2003):
Table 3.3: Gender questionnaires
Gender Questionnaires Source
Performance
Expectancy
H1a
As a male, IT-based MCS enables me to
achieve my necessities more efficiently. Venkatesh
2003, Venkatesh,
and Morris 2000
As a male, I would believe that using IT-
based MCS will enhance learning and
research.
As a female, the IT-based MCS increases
quality of my management knowledge.
As a female, I use IT-based MCS when
learning into my course.
As a female, I use IT-based MCS for
accessing the personal materials.

18 RESEARCH METHODOLOGY
Effort Expectancy
H2a
As a male, learning of IT-based MCS
system is easier.
Venkatesh
2003
As a female, I would find IT-based MCS
implementation flexible for the
organizations.
Social Influence
H3a
The participants into demonstration who
have effect my behaviour to reflect that I
should usage IT-based MCS system.
Vekatesh
2003
The community encourages me to use
IT-based MCS.
People those are important to me
encourage me to use IT-based MCS into
the three organizations.
Experience will moderate relationship among effort expectancy as well as user
behavioural intentions. It will moderate relationship between social behaviour besides user
behavior. Experience will moderate relationship among facilitating conditions as well as user
behavioural intentions. The effect of experience moderator variable will be evaluated through
a series questions reworded to this study in table 3.4 (Venkatesh, 2003):
Table 3.4: Experience questionnaires
Experience Questionnaires Source
Effort Expectancy
H2b
It was easier for me to become skilful
with use of the IT-based MCS system.
Venkatesh
2003
My interaction with the IT-based MCS is
clear as well as understandable.
Using IT-based MCS makes me easier to
become experienced in my management
course.
I have little experience with use of the
system.
This system is flexible for my course.
Social Influence
In general my university supports me to
use the system. Venkatesh

19 RESEARCH METHODOLOGY
H3b 2003Using IT-based MCS enhances my
knowledge about the system
environment.
Facilitating
Conditions
H4b
I have confidence that this system will be
durable. Vekatesh
2003The management and staffs using IT-
based MCS are accommodative.
I experienced of team spirit besides
interested the staffs into the
organizations to use the IT-based MCS
system.
Level of education will moderate relationship among performance expectancy as well
as user behavioural intentions. It will reasonable relationship between effort expectancy and
user behavior. Experience will moderate relationship amongst facilitating conditions as well
as user behavioural intentions. The effect of level of education moderator variable will be
evaluated through a series questions reworded to this study in table 3.5 (source: Venkatesh,
2003):
Table 3.5: Level of Education questionnaires
Level of Education Questionnaires Source
Performance
Expectancy H1c
I find IT-based MCS is useful in my
studies.
Venkatesh
2003
Using IT-based MCS enables me to
achieve the education events more
rapidly.
Using IT-based MCS will increase my
educational outcomes.
The use of IT-based MCS allows me to
have access to the educational
information about my course content.
I find the IT-based MCS helps me to
increase my educational level.
My interaction with the IT-based MCS is Venkatesh

20 RESEARCH METHODOLOGY
Effort Expectancy
H2c
understandable. 2003, Wu, Yu,
and Weng 2012Learning to operate IT-based MCS is
going to easier for me in my education.
Facilitating
Conditions
H4c
I have proper knowledge related to use
of IT-based MCS.
Venkatesh
2003
IT-based MCS are similar to educational
learning tools that I can use.
I have access to the educational
resources required to implement IT-
based MCS.
Geographic location will moderate relationship among social influence as well as user
behavioural intentions. It will moderate relationship among facilitating conditions as well as
user behavioural intentions. The effect of geographic locations moderator variable will be
evaluated through a series questions reworded to this study in table 3.6 (source: Venkatesh,
2003):
Table 3.6: Geographic Location questionnaires
Geographic Location Questionnaires Source
Social Influence H3d
I think that there is high significance of
location into implementation of IT-MCS
practices in the Saudi based Oil
companies.
Venkatesh 2003
There is proper implementation of IT-
MCS practices in Royal Commission of
Jubail.
There is proper implementation of IT-
MCS practices in ARAMCO.
The remote projects are located in
environmentally sensitive regions that
are far from urban areas.
Identified environmental factors such as
human-resource crunch and unforeseen

21 RESEARCH METHODOLOGY
delays in resolving queries and issues
associated with remote projects.
Facilitating
Conditions H4d
I have confidence that implementation of
new IT systems becomes a successful
project in Saudi Arabia oil companies.
Venkatesh 2003
I believed that environmental factors
such as existing digital-divide and lack
of IT awareness could still obstruct
organization-wide adoption and diffusion
of new IT systems.
Government of policy will moderate relationship with the behavioural intentions. The
effect of Government Policy moderator variable will be evaluated through a series questions
reworded to this study in table 3.7 (Keen 1993; Issa-Salwe et al., 2010):
Table 3.7: Government Policy questionnaires
Government Policy Questionnaires Source
Behavioral Intentions
H5
I believed that a proper government
policy is required to implement IT-MCS
practices in the Saudi based Oil
companies.
Author; Keen
1993; Issa 2010
Government policy shapes the
behavioral intentions of the managers to
implement the IT-MCS.
I will prove that government policy
influences a positive behavioral
intentions for improved efficiency
measures through the implementation of
IT-MCS.
Government policies support the
adoption and acceptance of competitive

22 RESEARCH METHODOLOGY
corporate technology to meet high
performance expectations.
In general, the regulations from
government will help to improve
corporate governance by use of IT-
MCSs.
Government policies ensure effective
control of actual organizational
activities.
Favourable Government policies
encourage the adoption of IT- MCS due
to the companies’ intentions to conform
to the government policies
Government policies in a growing
economy strongly influence the user
behavior for the adoption and increased
use of IT in oil companies.
3.12 Criteria of Selection Companies
It is important to highlight the criteria used in the selection of case study companies
used for this research. The main aim of this research was to choose matched case companies
such that the evaluation had controlled factors and variables. Therefore, the following criteria
were used in selecting the three case study oil and petrochemical companies:
a) In order to reflect the general pattern of practice in adopting IT-based MCS, three was
a sufficient number of companies – two Saudi owned and one joint venture.
b) The companies had to be dispersed geographically across Saudi Arabia for us to test
different competencies and experiences of Saudi Arabian managers in the
petrochemical industry.
c) The researcher also chose case study companies that had some economic relevance in
the context of the Saudi Arabian economy because this indicates the importance
attached to IT-based MCS in Saudi Arabia.

23 RESEARCH METHODOLOGY
d) The selected companies operate in the same industry and share the same technological
complexities. This minimized the differences on the effect of IT adoption in MCSs
and management practices.
e) The researcher was also careful to select organizations that are almost the same size to
avoid huge differences in IT-based management control practices that are likely to
arise from factors such as company size. This study chose to examine large companies
as opposed to small companies since these large organizations have a need for
sophisticated IT-based MCSs.
3.13 Research Participants and Ethics
The researcher decided it would serve the study’s purpose if the following employees
from the three companies participate in this study:
1. The CEO (Chief Executive Officer)
2. Top managers
3. Middle level managers responsible for IT systems
4. Lower-Level managers in charge of IT-based MCS implementation
5. Employees in the IT department
6. Top managers in charge of marketing
7. Top managers in charge of production
All the respondents were categorically made aware of the strict confidentiality to be
observed when handling the divulged information and that the purpose of this study was
entirely academic. All respondents were also made aware of their rights to refuse to answer
questions that would otherwise need them to part with secretive or sensitive information.
Given the constraints of cost and time, the primary concern during the collection of data was
deciding how to best address the highlighted research questions, as well as the need to show
that the collected data was properly acquired, and the analysis was both appropriate and
sufficiently insightful. The research model variables will be measured using instruments and
principles validated by previous studies (Khandwalla, 1977; Keen, 1993; Overby, 2006;
Ahuja et al., 2009; Salarzehi & Kord, 2010; Kestle, 2014). Top managers, middle-level
managers, and low level managers selected as respondents for this study will receive similar
questions that will mainly deal with managerial utilization of IT-based MCS in decision
making and management processes. These respondents will answer the structured

24 RESEARCH METHODOLOGY
questionnaires including questions which will primarily focus on the adoption of IT-based
MCS techniques and identified control moderator variables.
3.14 Criticism of Research Data Sources
To analyze the validity of research data obtained through questionnaires, the
researcher will consider several uncertain factors such as the respondents may suffer limited
or selective memory thereby presenting certain issues inaccurately, this is largely because of
reactivity problems. Biasness in social desirability is also a possibility where respondents
tend to use social desirable means when answering research questions through questionnaires.
Evidence has shown that the perception of respondents can be affected by social desirability
to answer in a certain way to specific research questions. These considerations may affect
validity levels of the research findings. On matters of reliability, it is expected that the
respondents will give the same responses regardless of who asks the questions. The potential
issues relating to social desirability bias are likely to present problems of exaggeration or
hiding of shortcomings. Other factors that are expected to affect the collection of research
data include people’s reactions to information, the organizations’ internal structure and
functioning, and the design of IT-based MCSs that affect the behaviors of employees in each
considered organization. Another important point to note is that if the top level managers in
the case study companies have a risk-averse culture in place, there may be conflict and
possible confusion if lower level managers consist of risk takers. This is because with a risk-
averse attitude, top managers may choose to stick to what they know for a fact as a result of
prior experience, knowledge, and stability. Such organizations tend to be highly reactive
especially when dealing with potential drawbacks. Lastly, it is important to consider that most
employees fear the shame culture therefore they may lack initiative in their responses because
errors and mistakes are likely to be personalized. MCSs are the nexus of any large
organization and they function to ensure the alignment of a company’s objectives and
strategies with the activities and processes. Nevertheless, as mentioned in previous chapters,
the description or definition of MCSs either varies greatly or overlaps significantly such that
the research on MCSs focuses on seemingly unrelated elements which may result to
contradictory conclusions.
3.15 Analysis Plan

25 RESEARCH METHODOLOGY
According to Nowduri et al. (2015), an analysis plan is created prior to analysis of
collected data. It consists of description of research question as well as different steps used
into analysis. In this particular study, five research questions are being analysed such as:
1. What is the relationship between Performance Expectancy (PE)
and Behavioural Intentions to use IT-based Management Control
Systems in Saudi Arabia-based Oil and petrochemical Industry?
2. What is the relationship between Effort Expectancy (EE) and
Behavioural Intention to use IT-based Management Control
Systems in Saudi Arabia-based Oil and petrochemical Industry?
3. What is the relationship between Social Influence (SI) and
Behavioural Intention to use IT-based Management Control
Systems in Saudi Arabia-based Oil and petrochemical Industry?
4. What is the relationship between Facilitating Conditions (FC) and
Usage Behaviour of IT-based Management Control Systems in
Saudi Arabia-based Oil and petrochemical Industry?
5. What is the relationship between Behavioural Intentions and
Usage Behaviour of IT-based Management Control Systems in
Saudi Arabia-based Oil and Petrochemical Industry?
Above mentioned questions are analyzed with statistical software
tools like SPSS (Statistical Package for the Social Sciences) followed by
regression analysis. The researcher had used of regression analysis which
was used in UTAUT based studies. Regression analysis (Pearson), ANOVA, and
correlation analysis will be used in this research to substantiate the appropriate hypotheses,
and SPSS software will be used for the analysis of this research. In order to examine selected
research questions, regression analysis is conducted for investigating whether or not
independent variable forecasts the dependent variable. Leek, Maddock and Foxall (1998)
proposed that it is proper analysis method when the goal of research is to assess extent of

26 RESEARCH METHODOLOGY
relationship among independent variable besides dependent variable. The regression model of
the hypothesis can be stated as the following:
Dependent variable of hypothesis = A + B* x
Where, Where, A= Intercept of the Model; B= regression coefficient; x = independent
variable of hypothesis
On the other hand, an analysis of the findings of the regression method indicates that the
value of the Significant F, as obtained through regression analysis, is found to be less than
0.05, the null hypothesis has to be disallowed essentially. Participant demographic data will
be characterized using descriptive statistics (frequencies and percentages). SPSS analysis is
provided with basic statistical functions which is included of frequencies, cross tabulation as
well as bivariate statistics. For each hypothesis, Table (3.5) summarizes study variables, and
statistical procedures employed:
Hypothesis 1: Performance Expectancy (PE) will have a positive influence on
Behavioural Intentions to use IT-based MCSs.
Hypothesis 2: Effort Expectancy (EE) will have a positive influence Behavioural
Intention to use IT-based MCSs.
Hypothesis 3: Social Influence (SI) will positively influence Behavioural Intention to use
IT-based MCSs.
Hypothesis 4: Facilitating Conditions (FC) will have a significant influence on Usage
Behaviour of IT-based MCSs.
Hypothesis 5: Behavioral intention or acceptance of an information system by Saudi
managers predict its actual usage.
Those hypothesises will be tested by use of regression analysis which helps to
measure relationship among performance expectancy besides behavioral intentions for H1
then relationship between Effort Expectancy and Behavioral Intentions in H2 . It measures
the relationship between Social Influence and Behavioral Intentions in H3, measures the
direct relationship between Facilitating Conditions and Usage Behaviour in H4 and measures
the direct relationship between Behavioral Intentions and Usage Behaviour. Here,

27 RESEARCH METHODOLOGY
performance expectancy, effort expectancy, social influence, facilitating conditions are
independent variables and behavioral intentions and usage behaviour are dependent variables.
Based on foregoing literature, following statements are hypothesized to investigate
the role of gender differences on the influence of performance expectancy over behavioral
intentions and corresponding usage of technology:
H1a: Gender will have a moderating influence on the relationship between
performance expectancy and behavioral intentions towards adoption and usage of IT-based
MCSs.
H1c: Level of user education will positively mediate the effect of performance expectancy
on behavioural intentions and use behaviour of IT-based MCS in Saudi-Arabia.
Both the hypothesis will be tested by use of Pearson’s correlation for measuring gender
and level of education affects the relationship between Performance Expectancy and
Behavioral Intentions. This statistical analysis method is used to measure how strong there is
relationship between two variables. It returns a value between -1 and 1, where 1 indicates that
there is stronger relationship, -1 indicates that there is strong negative relationship and 0
indicates that there is no relationship at all.
H2a: Gender will have a moderating impact on the influence of effort expectancy on
behavioral intentions towards adoption and corresponding usage of IT-based MCSs.
H2b: Experience will have a moderating influence on the relationship between effort
expectancy and behavioral intentions towards adoption and usage of IT-based MCSs.
H2c: Level of user education will positively mediate the effect of effort expectancy on
behavioural intentions and use behaviour of IT-based MCS in Saudi-Arabia.
The hypothesis will be tested by use of Pearson’s correlation for measuring how gender,
experience, in addition education level affect the relationship among Effort Expectancy and
Behavioral Intentions. Correlation with coefficient of 1 means that each positive increases
into one variable, then there is positive rise of the fixed proposition in others. On other hand,
correlation with coefficient of -1 means that each positive increases into one variable, then
there is negative rise of the fixed proposition in others.
H3a: Gender will moderate the relationship between social influence and behavioral
intention of users towards adoption and usage of IT-based MCSs.

28 RESEARCH METHODOLOGY
H3b: Experience will have a moderating impact on the influence of social influence on
behavioral intentions towards adoption and corresponding usage of IT-based MCSs.
H3c: Geographic location will moderate the effect of social influence on the behavioural
intention to adopt and use IT-based MCSs.
The hypothesis will be tested by use of Pearson’s correlation for measuring how gender,
experience, and geographic location affect the relationship between Social Influence and
Behavioral Intentions.
H4b: Experience will have a moderating role on the influence of facilitating conditions
and usage of IT-based MCSs.
H4c: Level of user education will positively mediate the effect of facilitating conditions on
use behaviour of IT-based MCS in Saudi-Arabia.
H4d: Geographic location will moderate the influence of facilitating conditions on
adoption and usage of IT-based MCSs.
The hypothesis will be tested by use of Pearson’s correlation for measuring how
experience, education level, and geographic location affect the relationship between
Facilitating Conditions and Behavioral Intentions.
H5a: Government policy will positively influence behavioural intentions to adopt and
use IT-based MCSs in Saudi-Arabia.
The hypothesis will be tested by use of ANOVA to measure the influence of
government policy on Behavioral Intentions. ANOVA test is one way analysis of variance
which is used for determining whether there is any statistical between independent variable.
Table 3.5: Statistical Tests Plan for Hypotheses
Hypothesis Variables Statistics
Hypothesis 1
H1a
H1c
Performance Expectancy and Behavioral
Intent.
Gender, Performance Expectancy and
Behavioral Intentions.
R. Analysis
Pearson’s
Correlation
Pearson’s

29 RESEARCH METHODOLOGY
Level of Education, Performance Expectancy
and Behavioral Intention.
Correlation
Hypothesis 2
H2a
H2b
H2c
Effort Expectancy and Behavioral Intentions.
Gender, Effort Expectancy and Behavioral
Intentions.
Experience, Effort Expectancy and Behavioral
Intentions.
Education Level, Effort Expectancy and
Behavioral Intentions.
R. Analysis
Pearson’s
Correlation
Pearson’s
Correlation
Pearson's
Correlation
Hypothesis 3
H3a
H3b
H4d
Social Influence and Behavioral Intentions.
Gender, Social Influence and Behavioral
Intentions.
Experience, Social Influence and Behavioral
Intentions.
Geographic Location, Social Influence and
Behavioral Intentions.
R. Analysis
Pearson’s
Correlation
Pearson’s
Correlation
Pearson’s
Correlation
Hypothesis 4
H4b
H4c
Facilitating Conditions and Usage Behavior.
Experience, Facilitating Conditions and Usage
Behavior.
Education Level, Facilitating Conditions and
Usage Behavior.
R. Analysis
Pearson’s
Correlation
Pearson’s
Correlation

30 RESEARCH METHODOLOGY
Hypothesis 5
H5a
Behavioral Intentions and Usage Behavior.
Government policy and Behavioral Intentions.
R. Analysis
ANOVA test
Assumption of regression analysis is that there should be a linear relationship between
the outcome variables as well as independent variable.
Assumptions for Pearson coefficient correlation is as follows: level of measurement,
absence of outliners, linearity, related pairs as well as normality of variables. Level of
measurement is referred to each variables. Linked pairs is referred to sets of variables where
each applicant have some pair of standards. Therefore, when correlation is between weight as
well as height, then each observations have weight along with height values. Absence of
outliners are referred to have no outliners on either variables. An outlier is defined a value
that 3.29 standard deviations from the mean, and there is also standardized value less than +/-
3.29. Linearity is referred to figure of ideals by scatterplot.
Assumptions of ANOVA test is that each group of examples are drawn from dispersed
populations, and all the populations have mutual variance. The samples are being drawn self-
reliantly of each other. With each sample, the explanations are being tested casually along
with self-reliantly of each other’s.
3.16 Research Constraints
The scope of this study will be limited to the three selected oil and petrochemical
companies; Saudi ARAMCO, Saudi SABIC and American Halliburton and a sample study
population of 384 employees from all three companies due to time and cost constraints. As
mentioned earlier, large Saudi oil companies such as Saudi ARAMCO have a number of
remote sites in underdeveloped and environmentally sensitive locations away from
concentrated urban areas. These companies therefore face countless cumbersome problems in
access that may lead to increased costs and significant delays, as well as have negative effects
on research quality. This research will also consider the principles of individual motivation
and social identity in team settings, and how both affect and relate to the design and

31 RESEARCH METHODOLOGY
implementation of IT-based MCSs. It is also important to note that some of the data collected
in this research such as ideas, perceptions, and concepts cannot be quantified.
3.17 Project Timeline
This study employs survey methodology to collect data through questionnaires.
Collection of primary data is inherently a time-consuming and lengthy process that involves
sending/mailing the questionnaires to respondents, a reasonable amount of time given to the
respondents to fill the questionnaire, time overruns due to contingencies and finally, time
taken by the respondents to return back the filled questionnaires. Consequently, planning the
course of data collection process is highly important since any delay in data collection may
jeopardize the success of this study. Owing to these reasons, the author has suggested an
estimate timeline as per the figure below:
Data Collection
Data Analysis
Reporting the findings
Interpretation of Results
Completion of the report
8/1/2018
8/11/2018
8/21/2018
8/31/2018
9/10/2018
9/20/2018
9/30/2018
10/10/2018
10/20/2018
10/30/2018
Project Timeline
Start Date # of days to complete End Date

32 RESEARCH METHODOLOGY
Data Collection
Data Analysis
Reporting the findings
Interpretation of Results
Completion of the report
8/1/2018
8/11/2018
8/21/2018
8/31/2018
9/10/2018
9/20/2018
9/30/2018
10/10/2018
10/20/2018
10/30/2018
Gantt Chart
Start Date # of days to complete End Date
3.18 Summary of the Chapter
This study has selected three Saudi oil and petrochemical companies: Saudi
ARAMCO, Saudi SABIC and American Halliburton as the target population to evaluate how
the implementation of the IT based management Control Systems MCS in these companies
affected the overall performance of the managerial teams in these companies. In order to
achieve the defined objectives of this research as mentioned in the previous sections the
principals from the UTAUT (Unified Theory of Acceptance and Use of Technology)
theoretical framework was applied. The three companies serve as a benchmark for examining
the IT based use of MCSs in Saudi Arabia and the results will also be compared between the
three companies to establish the differences in the effectiveness of application of such
systems in the socio- cultural context of the developing Saudi economy in contrast to the
developed western American oil companies having a high level of technological acceptance.
The research thus, follows a case study approach by selecting the sample population of the
respondents from these three companies including the employees from the remote
subsidiaries and branches of these companies. The data collected from each of these

33 RESEARCH METHODOLOGY
companies will be distinctly used to compare the results to accommodate the dynamic view
of social reality while emphasising the behavioural intentions to use IT in context of the
Saudi Industrial Sector.
A quantitative survey through a close-ended questionnaire will be conducted among a
sample population of 384 respondents (128 from each company). The questionnaire is based
on a seven point Likert’s scale to measure the responses from Strong Disagree=1, Neutral= 4
to Strongly Agree=7. Regression Analysis is used to test the hypothesis developed for this
study while Pearson’s correlation test will establish the correlation of the moderating
variables and significance of the impact of moderating variables on the dependent and
independent variables’ relationship. The study follows the conceptual model developed by
Venkatesh et al. (2003) using Performance Expectancy (PE), Effort Expectancy (EE), Social
Influence (SI) and Facilitating Conditions (FC) as the independent variables and Behavioural
Intentions (BI) and Usage Behaviour (UB) as the dependent variables. The researcher
included gender, experience, level of education, geographical location and government policy
as the moderating variables to better understand the adoption of IT based MCS in the current
scenario and prevailing market and political conditions in Saudi Arabia which is primarily an
Islamic nation with a monarchical government owning most of the public sector and oil and
petrochemical companies in the country.
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