Adopsi 01
Adopsi 01
Adopsi 01
Abstract
This study investigates the adoption of the new phenomenon of Geographic Information Systems
(GIS) in an organizational context in an emerging economy, namely Saudi Arabia. It explores the
determinants of employees’ perceptions of GIS, their actual usage and expected outcomes when
they use it. A model was developed for this study based on relevant theories and existing litera-
ture. In total 221 responses were collected from the Ministry of Water and Electricity (MOWE) in
Saudi Arabia using a structured survey questionnaire. Several statistical techniques were applied
to the data collected. Results show that factors having the most significant impact on employees’
perceptions of GIS are managerial support, IT expertise and exposure to GIS. However, some fac-
tors did not have any significant impact on employees’ perceptions, these being GIS training and
incentives. It also emerges that attitude to GIS has an impact on and relationship with the actual
usage of GIS. In the outcomes of GIS adoption, results indicate that its usage influences enhanced
risk management and better customer relationships. However, no significant relationship was
found that led to more efficient decision-making and saving of costs. This paper also highlights
implications and discusses the limitations and suggestions for future research.
Keywords
GIS Training, Managerial Support, Incentives, GIS Adoption, Saudi Arabia, GIS Usage
*
Corresponding author.
How to cite this paper: Alzighaibi, A., Mohammadian, M. and Talukder, M. (2016) Factors Affecting the Adoption of GIS
Systems in the Public Sector in Saudi Arabia and Their Impact on Organizational Performance. Journal of Geographic Infor-
mation System, 8, 396-411. http://dx.doi.org/10.4236/jgis.2016.83034
A. Alzighaibi et al.
1. Introduction
Geographic Information System (GIS) works by integrating hardware, software, and data to capture geographic
data and manage them for analysis, and then displaying the finalized geographic information for users to access.
It helps users to easily and quickly understand the data and make a decision [1]. The term “Geographic Informa-
tion Systems Science” has been used for twenty years internationally. This science has many characteristics and
employs various intellectual and technical strategies [2]. Geographic Information System is an important science
and it is used in three different contexts or classifications as follows: 1) a Geographic Information System “tool”;
2) a Geographic Information System “application”; and 3) a Geographic Information System “science” [2].
There are six components of GIS, the first and most important being the overarching network. The network is
a critical aspect of GIS as data and digital information cannot be shared or communicated except through the
network. The second component of GIS is the user’s hardware, which consists of devices that execute GIS oper-
ations [3]. The third component part of GIS is the software. GIS vendors such as the Environmental Systems
Research Institute provide packages that can be purchased by organizations. These packages offered by different
GIS vendors have varied applications, level of complexity and data size. The fourth component is the database
where all information is saved for a future decision or to solve a problem. The fifth component comprises pro-
cedures that manage the GIS and keep it within budget constraints, and as accurate as possible so it can satisfy
users. The sixth component concerns people, i.e. the users of the GIS and those who provide and update all the
digital data on the GIS database so that it is more efficient and effective [3].
There are many benefits of using GIS. These benefits are for all types of organizations and industries whether
they are small, medium or large. There are five classifications that these benefits come under, namely saving
costs and increasing the organizational efficiency, better decision-making, communication enhancement, more
efficient recordkeeping and management of geographical variables [4]. GIS can be merged with any organiza-
tion’s information system framework [1]. Furthermore, GIS is used to store, control and retrieve datasets. It is
employed in many applications in many different areas. In GIS a dataset is called a layer, which can refer to
roads, seas, buildings, etc. Each layer is stored in a specific location with coordinates in the GIS [5]. Layers that
have the same geographical coordinates are linked to each other in the GIS. This relationship, which is referred
to as spatial joins between datasets, can help in analyzing the data and making decisions. An example of making
decisions can be allocating roads that are close to a certain river. These roads can be allocated by using some
queries in the GIS, and help determine roads that may be affected by floods [5].
Another study was conducted in France to reduce the risks of floods/streaming by presenting a framework to
manage constraints [6]. Another way that GIS queries can help in decision-making is when infectious diseases
can be spread, decisions are vital regarding knowing how long it takes these diseases to reach certain areas and
subsequently avoid them [7]. It can also allocate earthquake emergency shelters, which will reduce the amount
of damage and injury [8]. An analysis done in South Korea presented a model that can help estimate the amount
of forest fire caused by humans. This also can affect the decisions and actions that could be taken to reduce the
spread of fire and reduce damage and injuries [9]. GIS was also utilized in Northern Ireland for allocating areas
with certain population subgroups according to their religion. The government can employ GIS in helping with
development of policies and decisions and manage residential segregation [10]. GIS can also assist in planning
for any future climate conditions in rural areas and how to prepare a strategy or strategies in response [11].
There are some general GIS studies that have been conducted in Saudi Arabia. One of these studies is the
study by Al-Ramadan (1993) whose main focus was to examine the validity of their hypothesis, which included
the highly centralised government in Saudi Arabia may have a much more organized GIS adoption than those
governments that are less centralized [12]. Another study by Abdulaal (2009) has provided a general framework
for enterprise GIS for Saudi municipalities. This framework includes three main factors, which are business
functions, tasks and data requirements [13]. A study by Koshak suggests that during Hajj in Makkah, it is better
to use Web-based GIS to manage traffic plan to facilitate easier mobility [14]. In this study, Koshak has devel-
oped a Web-based GIS for Hajj traffic plan [14].
GIS is also used in water resources since engineers, for example, must understand where their pipes, valves,
pumps, meters, etc., are located. The Ministry of Water and Electricity (MOWE) in Saudi Arabia is currently
using GIS for these same reasons. The location and usage of water and where customers are residing are factors
that need to be known. Engineers, managers, etc., also need to know what projects are under construction and
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the facilities requiring repair [15]. MOWE in Saudi Arabia has adopted GIS and initially in the Riyadh core
area, this process starting in 2003 and then in Dammam core area in 2004 [16]. Following that GIS was inte-
grated in many of the major cities using the Integrated Water Resources Management System [17]. This paper
will examine the adoption of GIS in MOWE and evaluate some determinants and test their impact on MOWE’s
employees’ perceptions of GIS and how these affect the actual usage of GIS. Then the outcomes of this adoption
will be evaluated.
2. Methods
The method used to collect data from employees was the survey. Our survey was divided into four sections,
these consisting of questions about demography, GIS usage, perception of GIS and determinants and benefits of
utilizing GIS. The perceptions of GIS and the determinants and benefits questions have one type of question,
which is on a 7-point Likert-type scale, serving to measure the level of employees’ agreement (1—Strongly dis-
agree, 2—Disagree, 3—Somewhat disagree, 4—Neither agree or disagree, 5—Somewhat agree, 6—Agree, 7—
Strongly agree) [18]. The survey was an online survey sent to MOWE’s employees. The number of people who
participated in this survey was 297 who worked in different branches of MOWE throughout Saudi Arabia.
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3. Theoretical Framework
Several models have been used to study the adoption of GIS technology in MOWE. The theories used in this re-
search were Innovation Diffusion Theory (IDT), Technology Acceptance Model (TAM), TAM2, Theory of
Reasoned Action (TRA) and the Unified Theory of Acceptance and Use of Technology (UTAUT). Innovation
Diffusion Theory (IDT) has been used since the 1950s, but was succeeded by Rogers’ introduction of the most
well-known and commonly used innovation-decision process in 1962 [20]. This theory describes the process of
accepting or rejecting a new innovation. Making a decision about a particular innovation goes through five steps,
which are called the IDT stages [20]. The first stage is when the individual gets to know the innovation and how
a new technology functions, its purpose and the need for it. The second stage occurs when the individual likes or
dislikes this new technology. There are five attributes that encourage an individual to like a new technology and
affect their decision: relative advantage, compatibility, complexity, trialability and observability. After that a de-
cision is made to accept or reject the technology, which is the third stage. The fourth stage is the implementation
of this new technology while the fifth stage confirms the decision made by the individual [21].
The Theory of Reasoned Action (TRA) was developed by Ajzen and Fishbein in 1975 [22] [23]. This theory
can be used in studies referring to people’s attitude-behaviour relationships [24]. In this theory, it is believed that
personal beliefs influence attitude and social norms will in turn shape the individual’s behaviour toward the ac-
tion [22]. There are two main constructs of intention in the theory of reasoned action, these being attitude toward
the behaviour and the behaviour that a person or a decision-maker enacts when social pressure is put on them.
This is known as the subjective norm [22].
The Technology Acceptance Model (TAM) grew out of the TRA model and was devised by Davis [25]. TAM
is the most accepted of all the technology adoption models [23]. TAM consists of three main parts that influence
the behavioural intention and actual usage of a particular technology: perceived usefulness, perceived ease of
use and the users’ attitudes that affect the behavioural intention [26]. This intention is determined by both users’
perceptions and attitudes regarding the technology and its perceived usefulness. Attitude is determined by both
perceived usefulness of the technology and its perceived ease of use [25].
TAM2 was introduced by Venkatesh and Davis [27] in their research paper titled “A Theoretical Extension of
the Technology Acceptance Model: Four Longitudinal Field Studies”. This model represented an extension of
the original Technology Acceptance Model (TAM) and its main purpose was to add more determinants to the
original TAM so that perceived usefulness and the intention to use the technology could be better measured.
These determinants include social influences that emerge in the context of subjective norm, voluntariness and
image. Other determinants comprise job relevance, output quality and result demonstrability which can all affect
perceived usefulness. Another important thing that this extended model wants to understand is how the effects of
social influence change with different experience levels that users have. It also looks at how the intention to use
the technology varies when the usage is voluntary [28].
The Unified Theory of Acceptance and Use of Technology (UTAUT) was developed by Venkatesh et al.
(2003) [28]. It is a theory consisting of four main determinants that directly affect the intention to use new tech-
nologies: performance expectancy, effort expectancy, social influence and facilitating conditions. This theory
also comprises four moderators that can affect the direct four core determinants, i.e. gender, age, experience and
voluntariness of use [28].
4. Research Model
The research model was constructed based on the existing theories mentioned previously. The research model
looks at three parts and tests their impact on each other. The first part of this model consists of five determinants
that may affect the perception of GIS. These determinants are GIS training, incentives, managerial support and
exposure to GIS. These determinants’ impact on people’s perceptions of GIS that are going to be tested will be
the most significant. The model’s second part looks at the impact of perception of GIS on the actual usage of GIS,
which will also be tested. The third part tests the actual usage and adoption of GIS. Four outcomes will be tested:
efficient decision-making, cost saving, enhanced risk management and improved customer satisfaction. The con-
text being investigated will be the Ministry of Water and Electricity (MOWE) in Saudi Arabia (see Figure 1).
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Efficient decision-
GIS training
making
Incentives
Cost saving
PERCEPTION OF ADOPTION OF
Managerial GIS GIS
support Enhanced risk
management
IT expertise Improved
customer
satisfaction
Exposure to GIS
technology Demographics Factors
Gender
Age
Tenure
Academic qualifications
on Earth. Some examples of these are the climate in different parts of the planet and how climate change can af-
fect the distribution and spread of diseases. It can also help in detecting the distribution of crimes in various
areas, distribution of plants and animals, etc. There are many applications of GIS that can help humans such as
Global Positioning System (GPS) in finding locations and managing services and resources [29]. All activities
such as building, digging ditches, burying pipelines and cables, finding oil and many other activities, can be do-
cumented using GIS [3]. Geographic Information Systems (GIS) keep track of such activities and where they
occurred or left their mark. According to Longley et al. (2011, p.4), almost everything that happens, happens
somewhere. Knowing where something happens can be critically important [3].
We use GIS in our daily lives, for example asking for a direction to get somewhere. We also use GIS to solve
problems in many areas such as health care, such as deciding where to build a new hospital in a particular area
and why. Delivery companies send things using different routes every day so the decisions they have to make
concerning which routes their vehicles should take will solve geographical problems. Some people cannot de-
cide whether their specific problem is geographical or not. There are three variables that can help in deciding if
this is the case. The first is that the problem has a question of scale. The second is the purpose of the problem
and whether they use geographic data to analyze the issue. The third is the time scale of the problem and wheth-
er geographic problems have persisted for a specific period of time [3]. In this way, “GIS does a better job of
sharing data and information than knowledge, which is more difficult to detach from the knower” [3].
There are three relationships in the research model that need to be tested and looked at in detail. The first rela-
tionship is between determinants and perception of GIS. Determinants consist of five factors that can affect
MOWE’s employees’ opinions concerning GIS. These five determinants’ impacts will be tested in the form of
hypotheses as described in more detail below.
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five questions were constructed according to Al-Gahtani and King (1999), Talukder (2014) and Gosh and Glott
(2005) [31] [33] [34]. The hypothesis that will be tested is:
H1: GIS training impacts on employees’ perceptions of GIS.
5.2. Incentives
Incentive can be defined as what a person thinks and perceives the benefits to be of introducing a new technol-
ogy into the firm [35]-[37]. A person’s perception of an innovation is very important as it might change people’s
behaviour regarding whether they accept this innovation [38]. Adoption of new technologies is expected to ena-
ble institutions meet their goals and benefits more efficiently. In order to reinforce employees’ perception of the
advantage of adopting new technologies, management should give them individual behavioural motivators that
encourage them to adopt new technologies [39]. In this research incentives are employees’ motivations and be-
liefs about the consequences of using GIS in MOWE. Participants were asked to rate their level of agreement or
disagreement concerning four items developed by Kurnia et al. (2006) and Talukder (2014) [34] [40]. The
second hypothesis that will be tested is:
H2: Incentives impact on employees’ perceptions of GIS.
5.4. IT Expertise
People’s prior experience refers to their skills and what they know about an innovation beforehand [43] [44]. In
this case employees’ IT expertise means their level of skills and experience about Information Technology (IT).
IT expertise was measured according to workers’ prior experience and skills and from where they were acquired.
Participants were asked to rate their level of agreement or disagreement on four items that were constructed ac-
cording to Talukder (2014) and Al-Gahtani and King (1999) [31] [34]. The hypothesis to be tested is as follows:
H4: IT expertise impacts on employees’ perceptions of GIS.
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an organization, in this case the Ministry of Water and Electricity (MOWE) in Saudi Arabia. The hypotheses in
this relationship are explained in more detail below.
6. Results
6.1. Participants’ Demographic Information
Demographic factors refer to factors that identify participants and their characteristics in terms of gender, age,
tenure, academic qualification and their usage and perception of Geographic Information Systems (GIS). In the
survey, all participants were male employees because in the Ministry of Water and Electricity (MOWE), Water
Sector, only men worked there so the percentage was 100% male. The participants’ age categories were diverse
in that 42% were in the 18 - 30 age category, while most participants were in the 31 - 40 age group (51% of all
age groups). Furthermore 5% fell under the 41 - 45 age groups and the smallest age group (2%) consisted of
participants who were 46 to 65 years of age. No-one in the group was 65 or older. Most participants (91%) are
full-time employees and only a few (9%) work on a part-time basis. Most employees (86%) are permanent and
only 14% work at MOWE on a temporary or contract basis. Participants’ academic qualifications vary in that
8% have their secondary certificate, 19% have a diploma, while the majority (56%) have a Bachelor degree. In
terms of postgraduate qualifications, 17% hold a Master’s degree. Table 2 summarizes participants’ demo-
graphic information.
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6.3. Correlations
The Pearson correlations were calculated for all variables included in this paper. These calculations show the
relationships between the determinants in relation to perceptions of GIS and the relationship between them and
the usage of GIS (see Table 4). These calculations also indicate the relationship between the usage of GIS and
outcome variables (see Table 5). The results show there is no relationship between perception of GIS and train-
ing. Results reveal that the level of perception is significant and positively related to incentives (r = 0.164, p <
0.05) and negatively related to managerial support (r = −0.336, p < 0.01). These results reveal that perceptions of
GIS are significant and positively related to IT experience (r = 0.256, p < 0.01) and exposure to GIS (r = 0.391, p
< 0.01). There is also a positive relationship between perception of GIS and level of usage (r = 0.470, p < 0.01).
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1 2 3 4 5 6 7
a a a
1. TRA 1.000 0.041 0.361 0.350 0.386 0.112 0.080
c a a a
2. INC 0.041 1.000 0.132 0.439 0.276 0.452 0.164c
1 2 3 4 5
The results also show that at the other end of the research model there is no significance at all between usage
and efficient decision-making or cost savings. However, there is a significantly positive relationship between
usage and enhanced risk management (r = 0.141, p < 0.05) and improved customer relationships (r = 0.477, p <
0.01) in the outcomes.
7. Discussion of Results
In this discussion of results, the hypotheses will be discussed in detail regarding their significance and the test
results for each one. Table 9 highlights the results of hypotheses testing and briefly summarizes the findings.
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Table 6. Results of multiple regression analysis with perception of GIS as a dependent variable.
Table 7. Results of multiple regression analysis with usage as a dependent variable and perception of GIS as an independent
variable.
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7.2. Hypothesis 6
Hypothesis 6 was designed to test if perception of GIS has an impact on the usage of GIS. Regression analysis
shows that the results are consistent with previous studies regarding the relationship and impact between the two
variables perception of GIS and its usage [46] [47] [61]-[64]. Findings for the impact of perception of GIS on its
usage were significant and a link was detected between the two variables.
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