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

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

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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

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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

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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

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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

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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)

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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)

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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