E-mail: ibrahimeskandarfadhel@gmail.com
E-mail: syzul@yahoo.com
School of Human Development and Techno-communication, University
Malaysia Perlis, Malaysia
E-mail: amaniali@unimap.edu.my
E-mail: omar.mazni@gmail.com
Submission: 05/09/2018
Revision: 20/09/2018
Accept: 24/09/2018
ABSTRACT
Currently the high rate
of systems fails, and challenges face the researchers’ results in relegate
systems success measure to the back seat. With the big debate among the
researcher about what is the best measure of system’s success this study come
out with a new perspective. Current study makes united between the dependent
variables that measures system’s success, instead of depending on one variable
researchers should depend on three variables together. With 344 users from
three different universities Ibrahim’s triangle model approved as a valid
success measure. All the hypothesis accepted and there are strong positive
relationships between the triangular factors.
Keywords: Systems Success; Satisfaction;
Benefit; Loyalty
1. INTRODUCTION
High
rate of system fails, limited of engineered frameworks that can help
organization to know about the quality and benefits of their systems towards
the users’ satisfactions and loyalty, lack of theoretical grounding, lack of
data collection and empirical data. In the Arab word knowledge about systems
quality and success are lack in line with almost nonexistence studies in Yemen
with the high numbers of systems dissatisfying users and organizations (FADHEL et
al., 2018a; FADHEL et al., 2018b).
There
is a hardness in engineered a framework that measures quality factors in the
higher education domain. In parallel, relationship of users’ satisfaction
towards loyalty of users to the systems is ambiguous. Researches are invited to
investigate these issues. It's highly recommended to engineer a framework that
make integration between the theories of software engineering and information
systems such as ISO 20510 and DM 2003. It’s of most important to conduct a comprehensive
study that develop a novel framework can solve all or parts of the issues
mentioned above (FADHEL et al., 2018a; FADHEL et al., 2018b).
Systems
users are expanding than ever before, this results in measurement complexity of
systems success. Researchers nowadays facing challenge of sophistication of
systems and their increasing number users, these phenomena can make lose sight
of the key elements such as (relevance, accuracy, timeliness etc.) of quality
that are playing a role in the success of systems. The measures of systems
success are growing increasingly which lead to more complexity which need
future studies (DELONE; MCLEAN, 2016).
ICT
plays a vital role e in most of the organization. Considerable budgets have
been spent on ICT to gain a sustainable competitive advantage. However, the
measurement of systems success has perplexed many scholars and experts until
now (GELDERMAN; KUSTERS, 2012). The prerequisites for measurement of systems
success has been a topic of argument. Because of the impact of non-controllable
variables, it seems to be impossible to directly compute or determine the
contribution of systems to organizational performance or on overall
organizational effectiveness (GELDERMAN; KUSTERS, 2012).
2. BACKGROUND
No
mutually agreed definition available and/or any reliable measurement
instruments (MARDIANA; TJAKRAATMADJA; APRIANINGSIH, 2015; MCNAB; LADD, 2014;
MWANGI, 2016). Therefore, basic concerns are still prevailing pertaining to the
explanation of quality criterion that could be potentially deployed to examine
systems quality and effectiveness (MWANGI, 2016).
Research
concerning to systems definitions and its success can be traced quite a few
decades ago. Yet, still lack of conceptualization exists especially with
regards to its definition and what can potentially cause system success.
Though, notable scholars in the area have forwarded some notable explanations.
Therein, scholars have also underlined need for urgent empirical attention for
theory refinement and explanation of the concept (MARDIANA et al., 2015).
Accordingly,
mixed results have also been reported in terms of what can explain systems.
Majority of the concerns outlined in relation to systems and software’s
includes ineffective measurements, limited theoretical grounding, reliance on
financial performance, lack of data collection, and limited knowledge on
prediction (MARDIANA et al., 2015; DELONE; MCLEAN, 2016). While many studies
have investigated the relationship between information systems (IS)
characteristics and IS use, the results have been inconsistent (FORSGREN et
al., 2016).
This
severe mixed results and lack of empirical focus on systems outlines
psychological, cognitive and passionate prospects which could intervene the
relationship between the instruments and predictors of success (SNEAD, MAGAL,
CHRISTENSEN; NDEDE-AMADI, 2015). Research in
the relationship of
information systems success (use, satisfaction and benefit)
has produced mixed results (GOEKE; CROWNE; LAKER, 2018).
According to Stefanovic,
et al. (2016)
severe paucity of research can be tracked in terms of the success of systems
deployed in public systems. Very limited studies have
attempted to examine and outline notable factors that could potentially enhance
the quality of information in system.
Past studies have not considered the
nature of outcomes and what features they can possibly predict for businesses
such as quality of information, decision making and/or task completion
etcetera. Accordingly, these approaches did not focus on prospects that may
have influenced towards the success or failure of the systems. It can be
evidently understood that it is important for scholars to highlight all types
of factors that enhance or deplete the quality and effectiveness of the systems
and thus would also affect user evaluation of the systems (SNEAD, et
al., 2015).
It was pointed out that there is
need for systems evaluation because of users’ satisfaction (VAEZI et al., 2016). The most meaningful measures of success for managers,
designers, and end users are the measures that capture the ultimate outcomes of a
systems deployment and use (DELONE; MCLEAN, 2016). The satisfaction of the
students and quality of the systems should be the most important thing and WIS
should make students satisfy and meet their expectations (LEE; HUH; JONES,
2016). Up to date research of satisfaction and loyalty issues have dominated
the literature, but the relationships between these two concepts are ambiguous (FADHEL et al., 2018b; KHRED, 2017; MOSAHAB;
MAHAMAD; RAMAYAH, 2010).
There
is a cognitive gap between users’ expectations and what the users perceives.
Users satisfaction is one of the most acute fields of research in the area of
systems and softeware’s (VAEZI et
al., 2016).
Today researchers agree on that users’ satisfaction is more accessible measure
than the other measures. Users’ satisfaction can be adaptable specific
contexts. There is an important gap between what users are expect and what is
delivered as impacting systems success. The benefit that comes out from the
studying of user’ satisfaction gives the justification of the effort done in
studying it. Furthermore, studying the user satisfaction providing better
understand to importance of it to the organization and users (VAEZI et al., 2016).
The
benefit construct measures the systems outcomes and is therefore inevitably
compared to the systems purpose. For this reason, the benefit construct will be
the most contextual dependent and varied of the six DeLone & McLean
(D&M) framework success dimensions (DELONE; MCLEAN, 2016). Organizations
can know the success of their systems in terms of user satisfaction and benefit
(VAEZI et al., 2016). Alshibly (2015)
confirmed that benefit must be defined within the context of system under study
and within the frame of reference of those evaluate the system benefit.
In
electronic systems loyalty is the important success factor. Loyalty in the
domain of business has been studied extensively. However, very lack of studies
and little is known about how loyalty towards non-commerce systems. The
findings of studies that has been conducted in business domain are not
necessarily applicable to another domain. Examining user loyalty in
non-commerce systems is worthwhile. Different culture is playing a role in make
different result so, future studies is needed to generalizability the
non-commerce loyalty results (BERGER et al., 2017).
Results
in some studies found that satisfaction negatively associated to loyalty, other
study conducted by Zeithaml (2000) in medical field and the study of Afsar,
Rehman, Qureshi and Shahjehan (2010) on the bank customers in Pakistan founds
significant impact cited in (OSMAN; MOHAMAD; MOHAMAD, 2015).
3.1.
Problem
and Objective
Universities
in Mukalla Republic of South Arabia (South Yemen) has been applied without
measurement of their success. Organization stack-holders under pressure to
justify the cost of systems implementation and to know if these systems are
success and make the users satisfied (BAHESHWAN, 2016; KHRED, 2017).
The
current noticed problem is that researcher and systems mangers still confused
about the valid factor of systems success measurement. Some researcher agrees
on satisfaction some others agree on benefit whereas other researchers said
loyalty also can be a valid measure for systems success. With the currently
high rate of systems fail in the world, mix results, contrariness in the
results, lack of frameworks and ambiguous of the valid factor for determine
systems success (FADHEL et al., 2018a; FADHEL et al., 2018b).
This
study aims to come out with a new presiptive for systems success measurement by
providing a new concept (Ibrahim’s triangle model for systems success
measurement that made a unity between the dependent variables of success
measurement).
3.2.
Research
Methodology
Research
can be of any of the types either qualitative, quantitative or mixed methods.
The best method depends on the research objective and purpose of which research
is going to be conducted, as each of them has their own merits and demerits
(FADHEL, 2015). With an adapted and validated instrument this study will be
conducted under quantitative research method approach best suited under the
current circumstances. Hence the data from the students were collected by the
questionnaire survey.
The
study aims to comprehensively explain the phenomenon by utilized a quantitative
method to achieve the maximum benefits and to measure the success of the
universities systems based on student’s perception.
Smart PLS used to perform the results as its categorized as one of the best
tools used for predicating the results of the models in fields of software
engineering and information systems.
The PLS
procedure was applied to estimate the variables of the research model. Following
Fadhel (2015) PLS algorithm used three times for mutual influence between the
model factors. Three model estimated: Model 1 testing the whole model with
relation satisfaction towards benefit and loyalty. Model 2 testing the whole
model with relation benefit towards satisfaction and loyalty. Model 3 testing
the whole model with relation loyalty towards satisfaction and benefit.
Based
on the literature review and the related past studies in the field that are
listed in chapter two, these hypotheses are suggested:
4.1.
STUDENTS’
SATISFACTION
Satisfaction
is a strong predictor of benefit and Loyalty (BERGER et al., 2017; RAMAYAH; AHMAD; HONG,
2012). These measures have helped to combine and further our knowledge and
understanding on user satisfaction and consider it as a tool to measure
success. Such measures have been enabling businesses to examine technological
advancements and explore how user satisfaction can be signalled through this
prospect. The measures are also very flexible which is why they can also be
applied to general settings (VAEZI
et al., 2016).
Denotes
to the measure of satisfaction of students with the major system features a
student interacts with. This primarily includes online support systems, reports
and access, university online systems and online course data banks. Review of
the literature has suggested that satisfaction of student with system and
online portals can be of significant value towards system benefit and enhancing
loyalty with these web systems.
4.2.
LOYALTY
In
the views of Kiran and Diljit (2011) customer loyalty is the core objectives of
every service business. Therein, loyalty is referred to behavioral expressions
and intentions of customers that are mainly outlined from the repeated purchase
of use of service (CRONON et
al., 2000).
Likewise, it is also expressed from the recommendations users give to others
about a service or commodity. In particular, enterprises focused on profit
making require a lot of loyal customers in order to keep the revenues intact.
Concerning to non-profit organization, the number of users and rate of return
is decided accordingly. In terms of academic institutions, higher scale
research and financial strength is considered important (KIRAN; DILJIT, 2011).
A
behavioural prospect that outlines acceptance and satisfaction with a certain
product or service and leads towards repeat using, encourages referrals and
recommendations. Loyal students in this context would be ones engaged in
repeatedly using the online system of the university and actively recommending
of the same to other students.
·
H2A, Students’ loyalty has a positive relationship
with the students’ satisfaction and significantly affect students’ satisfaction
of university web-based system.
·
H2B, Students’ loyalty has a positive relationship
with the benefit and significantly affect benefit of university web-based
system.
4.3.
BENEFIT
Similarly,
what are and can be the likely benefits of an information system is a major
question and is likely to be answered in an effective manner to better
understand and assess any information system (DELONE; MCLEAN, 2003; FADHEL,
2015). Talking about benefits, IS can be termed into four categories which are
productivity, innovation, user satisfaction, and management control. (DELONE;
MCLEAN, 2003; FADHEL, 2015).
Measuring
the key benefits of any information system can help in obtaining several
benefits such as decreasing cost, time efficiency, market expansion and other
intangible aspects like environment friendly services etcetera (WU; WANG,
2006). One of the highly important prospects of systems success is the benefit
measure which denotes to the influence and outcomes of the systems from
individuals to economies and societies at large.
Scholars
in the area have outlined a significant feature when it comes to systems and
their benefits. The benefits refer to the extent to which a system is healthy
and worthwhile for users, organizations, groups, business sectors and economies
at large such as system facilitation in decision making, productivity
enhancement, welfare or job effectiveness.
·
H3A, Benefit has a positive relationship with the
students’ loyalty and significantly affect students’ loyalty towards university
web-based system.
·
H3B, Benefit has a positive relationship with the
students’ satisfaction and significantly affect students’ satisfaction of
university web-based system.
5. RESULTS
The analyzing results showed that
the factor loading of all items are perfect. The Cronbach’s Alpha for the
factors (Benefit = .859, Satisfaction = .802 and Loyalty = .965). The Composite
Reliability for the factors (Benefit = .905, Satisfaction = .863 and Loyalty =
.971). The rho_A for the factors (Benefit = .882, Satisfaction = .838 and
Loyalty = .988). The Average Variance Extracted for the factors (Benefit =
.705, Satisfaction = .564 and Loyalty = .825). Factor loading of all items are
above .6 so all items are ok and related to its construct. All the results are
perfect no violation issue. For more illustration see the table below.
Table 1: Analyzing Results
Factor |
Items Loading |
Cronbach’s Alpha |
rho_A |
Composite Reliability |
Average Variance Extracted |
Benefit |
.7713 |
.859 |
.882 |
.905 |
.705 |
.7653 |
|||||
.7964 |
|||||
.7162 |
|||||
.7511 |
|||||
Satisfaction |
.6681 |
.802 |
.838 |
.863 |
.564 |
.7202 |
|||||
.9047 |
|||||
.7517 |
|||||
.7609 |
|||||
Loyalty |
.6816 |
.965 |
.988 |
.971 |
.825 |
.6967 |
|||||
.7908 |
|||||
.7971 |
|||||
.7648 |
Source:
The Researcher
Hypothesis
testing showed that all the hypothesis was accepted. All the Hypothesis has a
significant value and positive relationship.
·
H1A,
Students’ satisfaction has a positive relationship with the students’ loyalty
and significantly affect students’ loyalty towards university web-based system.
(β = .385 T.Value = 6.456
P.Vlaue = 0.000) the hypothesis H1A was fully accepted.
·
H1B,
Students’ satisfaction has a positive relationship with the benefit and
significantly affect benefit of university web-based system.
(β = .552 T.Value = 1.380
P.Vlaue = 0.000) the hypothesis H1B was fully accepted.
·
H2A,
Students’ loyalty has a positive relationship with the students’ satisfaction
and significantly affect students’ satisfaction of university web-based system.
(β = .467 T.Value = 9.764
P.Vlaue = 0.000) the hypothesis H2A was fully accepted.
·
H2B,
Students’ loyalty has a positive relationship with the benefit and
significantly affect benefit of university web-based system.
(β = .368 T.Value = 7.327
P.Vlaue = 0.000) the hypothesis H2B was fully accepted.
·
H3A,
Benefit has a positive relationship with the students’ loyalty and
significantly affect students’ loyalty towards university web-based system.
(β = .111 T.Value = 2.286
P.Vlaue = 0.023) the hypothesis H3A was fully accepted.
·
H3B,
Benefit has a positive relationship with the students’ satisfaction and
significantly affect students’ satisfaction of university web-based system.
(β = .552 T.Value = 11.380
P.Vlaue = 0.000) the hypothesis H3B was fully accepted.
6. THE PROPOSED MODEL
In
below the proposed model of this study. This model has been fully accepted and
approved since all the hypothesis is accepted.
Figure 1: Ibrahim Triangle Model for Systems Success Measurement
Source:
The Researcher
7. CONCLUSION
After
confirmation based on all needed statistical analysis from the pilot results
the main data collected from students of the universities to see their
satisfaction with the universities web-based systems, are they benefited from
these systems and are they loyal to these systems. Results showed that students
are satisfied and gets benefit from the systems and have a degree of loyalty
towards the systems so, web-based systems of universities (Al-ahgaff –
Al-Anduls - Hadhramout) in Mukalla Republic of South Arabia (South Yemen) are
success.
Testing the
success based on three variables (independent variables: satisfaction, benefit
and loyalty) approved and validated. There is a significant positive
relationship between satisfaction towards benefit and loyalty. There is a
significant positive relationship between benefit towards satisfaction and
loyalty. There is a significant positive relationship between loyalty towards
satisfaction and benefit.
So, all these
factors related to each other strongly with positive sign. This study provides
a new contribution to the body of knowledge by the triangle measure of systems
success. In future researchers called to apply this triangle instead of depends
only on one dependent variable such as benefit of satisfaction. Researcher can use this model
in their researches by linking their independent variables the variable
satisfaction of Ibrahim’s’ triangle model.
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