Knowledge-Based
Development In Sudan: Key Factors Affecting
The
Use Of K-BDSS Tools In Small And Medium-Sized Enterprises
Nour
Eldin Mohamed Elshaiekh¹, Chong Chin Wei², Peter Charles
Woods²
¹ Future University, Sudan, ²
Multimedia University, Malaysia
ABSTRACT:
This study identifies
factors affecting the perceived use of Knowledge-Based Decision Support System
(KBDSS) tools in small and medium-sized enterprises (SMEs) in
Keywords: Knowledge-based
development, decision support system, K-BDSS tools, medium-sized company,
1. Introduction
There is a general consensus in the literature that the challenges
facing modern societies call for development strategies that are knowledge
based. Much of the recent empirical literature emphasizes the importance of
adopting such development strategies (Carrillo, 2004; Ergazakis
et al, 2007; Ergazakis et
al, 2006; Ovalle et al, 2004).
However, despite the fact that this view is widely held internationally,
there are still a lot of issues which need to be addressed, especially in a
growing economy such as that of
For a knowledge-based decision support system (K-BDSS) to be effective
in the context of the knowledge-based economy it needs to operate in an
environment that advances innovation and favours the acquisition of knowledge
as well as learning (Laszlo & Laszlo, 2002).
Knowledge-based development (KBD) is essentially an infrastructure that
enlarges the social collection of knowledge. It is a strategy that facilitates
the flow of knowledge and it is also an improvement knowledge approach, based
on the recognition, systematization and improvement of the social universe.
Knowledge-based development is the process of using KM combined with a decision
support system (DSS), thereby enhancing the DSS and helping decision makers to
make ‘good’ decisions (Ovalle et al, 2004).
Many international organizations have adopted frameworks in their
strategic plans concerning global development, which clearly indicates that
there is a link between KM and knowledge-based development (Carrillo, 2004; Ergazakis et al, 2004; Komninos,
2002).
In the literature it is commonly accepted that knowledge and development
are linked together. Research in urban development and urban studies and
planning together with knowledge management and intellectual capital has
created a favourable environment for the advent of a new concept in the
scientific community, that is, the concept of the knowledge city (KC) (Carillo, 2004; Dvir & Pasher, 2004).
It is argued (Ergazakis et al, 2004) that the
KC is a city that aims to achieve knowledge-based development by encouraging
the continuous creation, sharing, evaluation, renewal and updating of
knowledge. This can be achieved through continuous interaction between a city’s
citizens and interactions with other cities’ citizens. The citizens’
knowledge-sharing culture as well as the city’s appropriate design, IT networks
and infrastructures are envisaged to support these interactions.
Moreover, an artificial intelligence (AI)-based DSS has been developed
for designing such interactions, by selecting and prioritizing the most
appropriate interventions and actions (Ergazakis et
al, 2007; Ergazakis et al, 2008). The system consists
of two subsystems. The first (developed using the technology of expert systems)
assesses the necessity of a particular intervention and proposes its most
appropriate form. The second prioritizes the selected interventions based on
multi-criteria decision making.
The DSS can be integrated with the KB to assist the user in selecting
the appropriate decisions. K-BDSS tools derived from the field of (AI) have
improved over the years and are characterized by their ability to represent
heuristic knowledge and to work with large amounts of data in a systematic
decision-making process (Turban & Aronson, 2005). In this context, (Jong, 2005) defines a K-BDSS as a combination between an
expert system and an AI system which is designed to enhance the decision
maker’s ability to make good and efficient decisions. A K-BDSS is a set of
procedures and approaches that maximizes the use and reuse of the knowledge
decision assets within an organization. A K-BDSS should assist users in the
planning process by presenting information and interpretations for a variety of
options.
To the best of our knowledge,
there are no studies on the use of the K-BDSS in Sudanese SMEs. Nevertheless,
the extant literature does provide indications as to the likely factors that
may affect the perceived use of K-BDSS tools in SMEs in
This study attempts to: evaluate how a KB is used in a developing
country such as
For the purposes of this research, a medium-sized company is defined as
a company that has between 50 and 249 employees and has either an annual
turnover not exceeding €50m or an annual balance sheet total not exceeding €43m
(Dictionary of Business and Management, 2006).
In
2. Background And
Literature Review
2.1. Why
Knowledge-Based Development?
Over the last few years, the attention of researchers has shifted toward
investigating the adoption of KBD because the storehouse of human knowledge
about the physical characteristics of our world has been steadily expanding.
The accumulation of knowledge about the social characteristics of civilization
is also accelerating. There is also a massive amount of new knowledge in other
scientific fields such as health, biology, physics etc. (Ergazakis
et al, 2007).
According to (Carillo, 2004) a knowledge-based
society can lead the way to a global society in which all the basic human needs
can be satisfied by future generations while maintaining a healthy, physically
attractive, and biologically productive environment.
According to (Conley & Wei, 2009) organizations have created K-BDSS
for sustained competitive advantage and to carry out consistent and efficient
decisions and activities. Therefore, the K-BDSS is instrumental for the local
and global success of organizations in
2.2. The Use Of K-BDSS In Small And Medium-Sized Enterprises
According to (Hart &
Porter, 2004; Turban & Aronson, 2005) a K-BDSS provides the user with ease
of access to previous decisions and data in order to support decision-making
tasks. KM systems, expert systems, artificial neural networks, hybrid DSS
intelligence, all these aim to make the manager’s decision an easy (or easier)
and effective one. The key variables affecting the use of K-BDSS are outlined
below. These are also tabulated together with the measures for each variable in
(Table 1).
2.2.1. Key Factors Affecting The Use Of K-BDSS
Technology Infrastructure
The technology
infrastructure necessary for K-BDSS is often perceived as expensive and hard to
use. Feedback from employees to improve accuracy, efficiency and flexibility
can nevertheless ensure the effective implementation and usage of a K-BDSS in a
company (Conley, 2009).
However, it may be
difficult for all employees to recognize the influence of the technology
infrastructure on K-BDSS unless their job is related to technology
infrastructure. In addition, in organizations that have just started to execute
K-BDSS systems, technology infrastructure cannot play a significant function as
the majority of the organizations do not have proper K-BDSS systems in place
(Chong, 2005).
Knowledge Contents
It has
been recommended by Barna (2003) and Jennex (2006) that the three KB projects to identify the
type of knowledge contents needed to build a successful K-BDSS. They found that
improvements could be made and recommend that a K-BDSS needs to have an
organization-wide knowledge structure; a standard, flexible knowledge
structure; and a common enterprise-wide knowledge structure that is clearly
articulated and easily understood.
Staff Training
The
role of training to facilitate software implementation is well documented in
relation to K-BDSS (Nelson & Cheney, 2000). Furthermore, it is recommend (Barna, 2003; Jennex, 2006) that:
users are trained on the use and content of the KB applications; clear
demonstrations be provided on how to use an application; employees be trained
on the new system; and that hands-on training should take place. In the same
vein (Gordon, 1999) points out that training professional play an important
role in the success of K-BDSS initiatives.
Organizational Culture
While the content of a
K-BDSS is the knowledge itself, a general K-BDSS also includes details on the
organization; processes, goals, strategies and culture (King, 2007).
Organizational culture is a significant factor which is inextricably associated
with K-BDSS within organizations. Organizations that encourage innovation and a
willingness to attempt new things among their employees have been found to have
better success with K-BDSS implementation (Ruppel
& Harrington, 2001).
Top Management Support
The support and commitment
of senior management is one of the important factors in the successful use of
K-BDSS. Top management support includes: active support of K-BDSS; setting a
personal example; communicating the company’s KB values to staff; and giving
formal and informal recognition to the importance of using the K-BDSS (Yeh et al, 2006).
Project Management
According
to (Chan et al, 2002) project management perceives lack of user participation
in the K-BDSS project as a failure factor which can result in misunderstanding
the users’ actual knowledge requirements. Many staff thinks that project
management failure is an important factor in the failure of KM projects.
User Satisfaction
It has been widely accepted
that user satisfaction with K-BDSS strategy has been more effective indicator
of organizational performance (Lo & Chin, 2009).How ever evaluation of a
K-BDSS can be done through project evaluations or through internal and external
review, executing user satisfaction surveys and as well through benchmarking.
In another development, it is suggested (Igbaria,
1998; Beijerse, 2000) that system quality,
information quality, causes system use, and user satisfaction are also good
evaluators of user satisfaction with K-BSS. Another line of it has been
augmented by Conley & Wei (2009) that quality influences approach and
performance in a KB system approach is also effective.
K-BDSS Benefits
According
to (Chiasson, 2001) the quality of the KM system does
not have a significant direct influence on users’ perceived benefits. However,
it is clear that a K-BDSS with system quality is necessary, but not sufficient,
to provide benefits that ensure a K-BDSS is running normally.
Table 1: Variables And
Measures
|
Variable |
Measure |
Researchers |
|
The perceived
use of K-BDSS tools in |
§ System
Usage § Perceived
Ease of Use |
(Chiasson, 2001; Turban & Aronson, 2005) |
|
Technology
Infrastructure |
§ Hardware
and Software Availability § Connectivity |
(Alavi & Leidner, 1999; Barna, 2003; Chong, 2005; Cross & Baird, 2000; Jennex & Olfman, 2002; Mandviwalla et al, 1998; Sage & Rouse, 1999) |
|
Knowledge Content |
§ Knowledge
Coverage § Knowledge
Structure § Knowledge
Relevance and Currency |
(Barna, 2003; Chua & Lams, 2005; Ginsberg & Kambil, 1999; Guptara, 1999; Holsapple & Joshi, 2000; Jennex
, 2006; Lucier, 2003; Mandviwalla
et al, 1998; Sage & Rouse, 1999; Waltz, 2003) |
|
Staff Training |
§ Training
in KBS Human Resource Development § Training
in KBS Technological Development |
(Alavi & Leidner, 1999; Barna, 2003; Cross & Baird, 2000; Ginsberg & Kambil, 1999; Jennex, 2006; Jennex & Olfman, 2002; Malhotra & Galletta, 2003) |
|
Organizational Culture |
§ Knowledge
Sharing § Knowledge
Acquisition § Perceived
Image § Management
Commitment |
(Alavi & Leidner, 1999; Barna, 2003; Davenport et al, 1998; Jennex
& Olfman, 2002; Sage & Rouse, 1999; Tanriverdi, 2005) |
|
Top Management Support |
§ Leadership § Strategic
Planning § Compensation
and Reward |
(Barna, 2003; Davenport et al, 1998; Holsapple
& Joshi, 2000; Jennex & Olfman, 2002; Yeh et al, 2006) |
|
Project Management |
§ User
involvement § Technical
and Business Expertise § Conflict
Management § Project
Evaluation § Project
Cost |
(Chua
& Lams, 2005; Guptara,
1999; Lucier, 2003; Waltz,
2003; Zopounidis et al, 1997) |
|
User Satisfaction |
§ Content § Accuracy § Format |
(DeLone, 2003; Dennis, 2005; Gelderman,
1998; Holsapple & Joshi, 2000) |
|
K-BDSS Benefits |
§ Knowledge
and Information Quality § System
Quality § User
Satisfaction Index |
(DeLone, 2003; Dennis, 2005; Gelderman,
1998; Holsapple 7 Goshi,
2000; Jennex, 2006; Poston & Speier, 2005) |
3. The
Problem
Despite international recognition of and a wealth of literature on the
K-BDSS process, there are still a lot of issues that need to be addressed in
this field. In particular, the process through which knowledge is created,
stored, shared and used within SMEs in
Why is effective application of the knowledge-based decision support
system proceeding slowly in SMEs in
How can we create an enabling environment which will advance innovation
and favour the acquisition and dissemination of knowledge as well as learning?
4. Research
Model And Hypotheses
The
following research model (Figure 1) was developed based on the factors
identified in the literature in order to assist this study in answering the
above questions.
Figure 1: K-BDSS In Medium-Sized Companies As
Derived From The Literature
According
to the above model, the following hypotheses were tested:
Hypothesis 1
Technology infrastructure is indisputably a key enabler in the
implementation of a K-BDSS and its perceived use (Alavi
& Leidner, 1999; Cross & Baird, 2000; Lee
& Hong, 2002).
H1: There is a positive relationship between
technology infrastructure and the perceived use of K-BDSS tools in small and
medium-sized companies in
Hypothesis 2
The
knowledge content tool helps developers build a K-BDSS (Barna,
2003; Elfaki et al, 2008; Jennex,
2006).
H2: There is a positive relationship between knowledge
content and the perceived use of K-BDSS tools in small and medium-sized in
Hypothesis 3
The
training of users in the use and content of the KB is considered essential to
the successful implementation of a K-BDSS (Gordon, 1999; Jennex,
2002; Nelson & Cheney, 2000).
H3: There is a positive relationship between staff
training and the perceived use of K-BDSS tools in small and medium-sized
companies in
Hypothesis 4
Organizational
culture is seen as the main obstacle to using a K-BDSS (Chase, 1997; Gupta et
al, 2000).
H4: There is a positive relationship between
organizational culture and the perceived use of K-BDSS tools in small and
medium-sized companies in
Hypothesis 5
Top
management support is increasingly recognizing the benefits of using a K-BDSS
(Chard, 1999).
H5: There is a positive relationship between top
management support and the perceived use of K-BDSS tools in small and
medium-sized companies in
Hypothesis 6
Project
management of K-BDSS tools in a company depends on the support and involvement
of users (Chan et al, 2002; Hoffer & Valacich, 1998).
H6: There is a positive relationship between project
management and the perceived use of K-BDSS tools in small and medium-sized
companies in
Hypothesis 7
User
satisfaction results from the feelings and attitudes about the total aggregate
of benefits that a user hopes to receive from interaction with a K-BDSS (Beijerse, 2000).
H7: There is a positive relationship between user
satisfaction and the perceived use of K-BDSS tools in small and medium-sized
companies in
Hypothesis 8
The
knowledge or information quality of a K-BDSS has a higher total effect on
perceived K-BDSS benefits (Chiasson, 2001).
H8: The benefits of K-BDSS tools are associated positively
with the perceived use of K-BDSS tools in small and medium-sized companies in
5. Research
Methods
A deductive method of
research was used for the data analyses. The
main purpose of employing this method was to provide a sound and straightforward
way of analysing quantitative data, by using the Statistical Package for the
Social Sciences (SPSS) to answer the research questions. A multiple field
approach was conducted in three purposively selected companies (DAL Motors,
SUTRAC, and SAYGA). The selection of these companies was based on two factors:
(i) they use a DSS in their management operations
more upon when compared with other companies in
The
questionnaire technique of data collection was employed, which provided data on
the specified variables in the model.
5.1. Instrumentation
The
five-point Likert scale was used to identify the
factors that influence the perceived use of K-BDSS tools in SMEs. All questions were formulated and
developed to answer the research questions referring to all variables and their
measurements. All factors were measured by
using different item questions, as shown in Table 3.
5.2. Sampling Techniques
There
are approximately 89 KB, SMEs in
According
to (Sekaran, 2003), the sample size for a
given population of 1300 is 297. Even though 600 questionnaires were
distributed to staff, 200 to each company, only 314 questionnaires were
successfully collected. Of the 314 (52.33%) questionnaires that were returned
successfully, only 268 (44.66%) copies were completely answered. The remaining
of 46 questionnaires could not be included in the study due to incomplete data
or poor responses (see Figure 2).
Figure 2: Staff Response Rate.
5.3. Questionnaire
A
questionnaire is a set of questions given to a sample of people (Stake, 2005). In a questionnaire, the responses
are collected in a standardized way, so questionnaires are more purposeful,
than interviews and, generally, the questionnaire is a comparatively quicker
means by which to collect data (Carter, 2000).
The questionnaire used in
this study consisted of three parts. The first part collected data on each
respondent to create a demographic profile. In the second part, there were five
questions measuring each respondent’s current usage of K-BDSS tools. In the
third part, there were 70 questions measuring independent variables or factors
that influence the perceived use of K-BDSS tools. As stated above, the
five-point Likert scale was used to seek opinions; the respondents drawing a
circle around one of five points from bad to good.
Prior to data entry to the software tools, one important step that must performed is data cleaning routines which try to removed unused data and dealt with missed data equally handled unrelated answers in the software's database. This is more related to the quantitative data collected through the questionnaire, with main purpose of testing, verification of the hypothesis. Thus it must be noted that during quantitative data analysis certain steps must be followed that combined the use of SPSS. This study however focused on hypothesis testing through rigorous statistical explanations following three approaches; Reliability, Validity and KMO Testing; Factor Analysis; and Multiple Regressions.
6. Data Analysis And Results
6.1. Demographic Profile Of The Respondents
The
first part of the questionnaire collected information on gender, age, nationality,
education level, length of service and current position in the company. Table 2
presents the demographic profile of the staff who responded to the
questionnaire and the frequency distributions.
Category Response(s) Frequencies Percentage
% Gender Male 163 60.8 Female 105 39.2 Age 21–35 132 49.3 36–50 118 44.0 Above 51 18 6.7 Nationality Sudanese 202 75.4 Other 66 24.6 Education
level Diploma 32 11.9 Bachelor Degree 13 6 50.7 Master 80 29.9 PhD 20 7.5 Number
of years of employment (length of service) with the company 1–2 Years 69 25.7 3–5 Years 131 48.9 > 6 Years 68 25.4 Current
position in the company Senior Manager 28 10.4 Manager 36 13.4 Assistant Manager 76 28.4 Supervisor 104 38.8 Technician 16 6.0 Other 8 3.0
Table 2: Demographic Profile.
6.2. Reliability
Testing
Reliability
testing was undertaken to ensure that all areas of each construct’s domain of
interest were covered and that the items truly measured what they were supposed
to measure (Cronbach, 1984).
Cronbach's
Alpha ‘’ value of all factors was found to be greater than
0.7. Hence, each item correlates “adequately” within each construct; A
Cronbach’s Alpha value of 0.7 or higher suggests good reliability, and the
indicators of this model's constructs’ validity are good (Hair et al, 2006).
Table 3 shows internal consistency reliability.
Table 3: Internal Consistency
Reliability for K-BDSS Usage.
|
Factor |
Number
of Measured Items |
Cronbach’s
Alpha |
|
Usage
of K-BDSS Tools |
5 |
0.859 |
|
Technology
Infrastructure |
6 |
0.747 |
|
Organizational
Culture |
10 |
0.896 |
|
Top
Management Support |
6 |
0.720 |
|
Staff
Training |
7 |
0.765 |
|
Knowledge
Contents |
9 |
0.737 |
|
Project
Management |
9 |
0.721 |
|
User
Satisfaction |
9 |
0.813 |
|
K-BDSS
Benefits |
9 |
0.820 |
|
Overall |
70 |
0.786 |
6.3. Data
Suitability
The
sampling adequacy measure generally indicates whether or not the variables can
be grouped into a smaller set of underlying factors; it should be greater than
0.5 for a satisfactory factor analysis to proceed. It can be seen from Table 4
that the KMO measure is 0.794, which indicates good partial correlations.
Table 4
also shows that
Table 4: KMO And
|
Construct |
KMO |
|
|
Usage of K-BDSS
Tools |
.755 |
.000 |
|
Technology
Infrastructure |
.669 |
.000 |
|
Organizational
Culture |
.839 |
.000 |
|
Top Management
Support |
.725 |
.000 |
|
Staff Training |
.743 |
.000 |
|
Knowledge
Contents |
.714 |
.000 |
|
Project
Management |
.685 |
.000 |
|
User
Satisfaction |
.725 |
.000 |
|
K-BDSS Benefits |
.793 |
.000 |
6.4. Validity
Testing (Factor Analysis)
Factor analysis
is a collection of methods used to examine how underlying constructs influence
the responses to a number of measured variables; it is a mathematical tool
which can be used to examine a wide range of data sets (DeCoster, 1998).
Based
on (Hair et al, 2006) for a good construct the items must have adequate
correlation with each other. Generally, a correlation value of less than 0.3
indicates a lack of convergence, whereas a value of more than 0.9 indicates a
lack of discriminate validity. Loadings of components must be more than 0.450
to be acceptable. Items that do not load properly may be dropped from the
instrument (Churchill, 1979; Hair et al, 2006).
7. K-Bdss
7.1. Current Usage Of K-Bdss
There
were five items in construct 1, ‘Current Usage of K-BDSS Tools in the
Organization (USAGE)’. The descriptive summary is provided in Table 5.
Cronbach's
Alpha ‘’ value of USAGE
= 0.859, which is more than 0.7. Hence, each item correlates “adequately”
within the construct. Cronbach’s Alpha value of 0.7 or higher suggests good
reliability and indicators of this model's constructs’ validity are good (Hair
e al, 2006).
Table 5
shows the results of the factor analysis. As stated above, loadings of
components must be more than .450 to be acceptable. In this study, all factor
loadings (FA) were found to be greater than 0.450 and no items were dropped.
A KMO
value of 0.755 indicates that a ‘good’ or a strong partial correlation is
exhibited in the data for this study: this suggests that this data is suitable
for factor analysis (Kaiser, 1974). The factor score was saved as USAGE to be
used for further analysis. A single factor was extracted that explained 64% of
the total variation in the five items.
7.2. Factors
Influencing The Use of K-BDSS
Our
literature review on K-BDSS found eight key factors which it was assumed might
affect the perceived use of K-BDSS in SMEs in
The
Cronbach's Alpha ‘’ values of all
constructs (variables) are greater than 0.7, (See Table 3). Hence, each item
correlates “adequately” within the construct. Cronbach’s Alpha value of 0.7 or
higher suggests good reliability and indicators of this model's constructs’
validity are good (Hair et al, 2006).
Table 5
shows the results of the factor analysis, generally all factor loadings are greater
than 0.450, and no items were dropped.
Loadings of component must at least more than .450 to be acceptable.
Items that do not load properly may be dropped from the instrument (Churchill,
1979; Hair et al, 2006). A single factor was extracted that explained are shown
in the last column of the total variation in the all items.
Table 5: Factor Analysis Of Degree
for Variables.
|
Construct |
No. of Items |
KMO |
Mini F.L |
Max F.L |
Extract |
|
USAGE K-BDSS |
5 |
0.755 |
.690 |
.864 |
64% |
|
Technology
Infrastructure |
6 |
0.699 |
.526 |
.800 |
45% |
|
Organizational
Culture |
10 |
0.839 |
.602 |
.803 |
52% |
|
Top
Management Support |
6 |
0.725 |
.512 |
.788 |
42% |
|
Staff
Training |
7 |
0.743 |
.451 |
.856 |
43% |
|
Knowledge
Contents |
9 |
0.714 |
.455 |
.856 |
35% |
|
Project
Management |
9 |
0.685 |
.485 |
.652 |
31% |
|
User
Satisfaction |
9 |
0.725 |
.454 |
.861 |
40% |
|
K-BDSS
Benefits |
9 |
0.793 |
.530 |
.840 |
42% |
8. The
Relationship Between The Dependent Variable And The Independent Variables Using
Regression.
Multiple
linear regression analysis was used to test the research model, namely, the
relationship between one dependent variable and more than one independent variable.
Table 6
shows the results of multiple regression analysis between factors and the
perceived use of K-BDSS tools in small and medium-sized companies in
The
term ‘Multicollinearity’ describes the situation where two or more independent
variables are highly associated with each other. The last column in Table 6
shows that the highest VIF (Variance Inflation Factor) value is 1.365, which
means that there is no problem of Multicollinearity (Hair et al, 2006).
Table 6: Results Of Regression Model
Analysis Between The Dependent Variable (The Use Of K-BDSS Tools In SME’s In
|
Variable |
B |
Std. Error |
Beta |
T |
Sig. |
VIF |
|
Technology
Infrastructure |
.335 |
.059 |
.335 |
5.718 |
.000 |
1.111 |
|
Organizational
Culture |
-.003 |
.061 |
-.003 |
-.047 |
.962 |
1.193 |
|
Top Management
Support |
.221 |
.058 |
.221 |
3.799 |
.000 |
1.099 |
|
Staff Training |
-.139 |
.059 |
-.139 |
-2.349 |
.020 |
1.126 |
|
Knowledge
Contents |
.075 |
.057 |
.075 |
1.307 |
.192 |
1.063 |
|
Project
Management |
-.011 |
.057 |
-.011 |
-.193 |
.847 |
1.037 |
|
User
Satisfaction |
.105 |
.062 |
.105 |
1.689 |
.029 |
1.249 |
|
K-BDSS Benefits |
.071 |
.065 |
.071 |
1.092 |
.276 |
1.365 |
Table 7: ANOVA.
|
Source |
Sum of Squares |
Df |
Mean Square |
F |
Sig. |
|
Regression |
53.541 |
8 |
6.693 |
8.120 |
.000(a) |
|
Residual |
213.459 |
259 |
.824 |
|
|
|
Total |
267.000 |
267 |
|
|
|
R2= .201, F = 8.120
8.1. Stepwise
Regression
Stepwise regression was
used to identify the predictors of the use of K-BDSS tools in small and
medium-sized companies in
Table 8: Results Of The Stepwise Regression Model.
|
Model |
B |
Std.
Error |
Beta |
T |
Sig. |
|
Technology
Infrastructure |
.327 |
.056 |
.327 |
5.851 |
.000 |
|
Top Management
Support |
.214 |
.058 |
.214 |
3.708 |
.000 |
|
User
Satisfaction |
.138 |
.057 |
.138 |
2.405 |
.017 |
|
Staff Training |
-.134 |
.056 |
-.134 |
-2.389 |
.018 |
R2= 0.190
The regression equation is:
Perceived
use of K-BDSS = 0.327(TECH) + 0.214(MGT) + 0.138(SATS) – 0.134(STRN).
For every unit increase in
TECH, USAGE is expected to increase by 0.327 units, provided MGT, SATS and STRN
remain unchanged.
For every unit increase in
MGT, USAGE is expected to increase by 0.214 units, provided TECH, SATS and STRN
remain unchanged.
For every unit increase in
SATS, USAGE is expected to increase by 0.138 units, provided TECH, MGT and STRN
remain unchanged.
For every unit increase in
STRN, USAGE is expected to decrease by 0.134 units, provided TECH, SATS and
STRN remain unchanged.
8.2. The Stepwise Regression Using Association For Demographic Variables
Figure 3 and
Table 9 shows the results of Univariate Analysis of Variance between
demographic variables (Gender, Age, Nationality, Education Level, Length of
Service and Position) and the perceived use of K-BDSS tools in small and
medium-sized companies in
Figure 3: Results Of Stepwise Regression Model Using Association For Demographic Variables.
Table 9: Results Of Univariate Analysis Of Variance Between Demographic
Variables And The Use Of K-BDSS Tools In SME’s In
Variable Type III Sum
of Squares df Mean Square F Sig. A.2 Age 4.267 1 4.267 4.444 .036
Dependent Variable: USAGE
Notes: F = 4.444 (p= .036), t=-.355, R Squared = .234
9. Overall
Hypothesis Test
In conclusion, the
quantitative analysis results provide statistically significant support for hypotheses
H1, H3, H4 and H7, but not for hypotheses H2, H5, H6, and H8. Table 10 presents
this conclusion.
The acceptance rule of
factors (p< .05) and the significance value (p< 0.05) further confirm
that this variable is suitable and significant with dependent variable (
Table 10: Overall Hypothesis.
|
No. |
t-value |
p-value |
Result |
Relationship |
|
H1 |
5.718 |
.000 |
Supported |
Positive |
|
H2 |
-.047 |
.962 |
Not Supported |
No relation |
|
H3 |
3.799 |
.000 |
Supported |
Positive |
|
H4 |
-2.349 |
.020 |
Supported |
Negative |
|
H5 |
1.307 |
.192 |
Not Supported |
No relation |
|
H6 |
-.193 |
.847 |
Not Supported |
No relation |
|
H7 |
1.689 |
.029 |
Supported |
Positive |
|
H8 |
1.092 |
.276 |
Not Supported |
No relation |
10. Discussion
And Conclusions
This study attempted to
identify the key factors affecting use of K-BDSS tools in Sudanese small and
medium-sized companies. The results of the analysis provided strong evidence to
support some but not all of the proposed hypotheses.
Of the overall factors,
Technology Infrastructure made the greatest statistically significant
contribution to the perceived use of K-BDSS in Sudanese small and medium-sized
companies. It is observed
(Alavi, 1999; Hong, 2002; Cross & Baird, 2000) that technology
infrastructure is indisputably a key enabler in the implementation and the
perceived use of a K-BDSS. Basically, technology infrastructure is the driver
of the K-BDSS. In relation to this matter, it has been stated by Lyytinen and
Rose (2003) that having a K-BDSS and keeping up to speed with developments in
technology infrastructure are critical to a company’s success in sustaining
competitive advantage; however, doing so is an ongoing challenge for many
organizations.
Top Management Support is
the second most important factor that is statistically significant to the
perceived use of a K-BDSS. This result is compatible with (Chard, 1999)
findings, which show that top management is increasingly recognizing the
benefits of using a K-BDSS. Moreover, it mentioned (Turban & Aronson, 2005;
Wilsey, 1999) that, for successful K-BDSS implementation, there needs to be
visible leadership and commitment from senior management, which must be
sustained throughout a KM effort. The leadership and commitment of top management
are the most critical factors for successful K-BDSS projects in
The third factor that is
significant is User Satisfaction. It is stated (Beijerse, 2000; DeLone, 2003;
Lo & Chin, 2009) state that user satisfaction results from the feelings and
attitudes derived from aggregating all the benefits that a user hopes to
receive from interaction with the K-BDSS. In this regard, it is founded
(DeLone, 2003) that most of Sudanese staff are impressionistic in their work
and performance. The core values of users are critical to the successful use of
a K-BDSS.
The fourth factor that has
a significant relationship with the use of K-BDSS in medium-sized companies is
Staff Training, but in this case it is negative one. Many researchers have
identified a positive relationship between them; it is mentioned (Schwarz,
2007; Nelson & Cheney, 2000; Davenport et al, 1998) that the role of
training to facilitate software implementation is well documented in the K-BDSS
field, and the lack of user training and failure to completely understand how
enterprise applications change organizational processes frequently appear to be
responsible for the problems during K-BDSS implementation. However, in
Further findings of
association between demographic variables and the use of K-BDSS shows that only
Age is significant (Sig. =.036< .05, B value is -.355). Staff who are 50
years and below are more likely to use a K-BDSS compared to those over 50 years
old due to barriers in exploiting technology. Many researchers have indicated
that age is one of the most significant factors influencing whether or not
people engage with technology.
Organizational Culture,
Knowledge Contents, Project Management, and K-BDSS Benefits were found not to
be significant to the perceived use of K-BDSS tools in Sudanese small and
medium-sized companies.
This study has identified
the factors that influence the perceived use of K-BDSS tools in small and
medium-sized companies in Sudan to be: Technology Infrastructure, Top Management
Support, User Satisfaction, Staff Training (negative due to the type, period
and time of training), and Age to a lesser extent.
To summarize, the results
of our analysis provide significant support for hypotheses H1, H3, and H7, but
not hypothesis H2, H5, H6, and H8. Only H4 is affected negatively.
It should be noted the data
collected from some respondents may not totally accurate because some of them
might feel the information requested should be kept confidential. The sample
obtained for this research study consisted of more males (61%) than females
(39%). Previous studies indicate that gender may have an impact on usage
behaviour (Bandyopadhyay & Fraccastoro, 2007; Venkatesh et al, 2008).
It should also be noted
that results may not be generalized to all other companies or to other
countries. These results are specific to
In order to have a highly
effective K-BDSS, a company must be equipped with all four success factors,
i.e. Technology Infrastructure, Top Management Support, User Satisfaction and
Staff Training. Implementation of a K-BDSS is, however, well worth pursuing
because effective usage can result in improved efficiency, increased income and
better productivity.
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About the Authors:
Dr. Nour-Eldin Elshaiekh is assistant professor in
Dr. Nour Eldin Mohamed Elshaiekh, Head of Knowledge Engineering
Department,
Dr. Chin Wei Chong is a senior lecturer in Faculty of Management at
Dr. Chong Chin Wei, Faculty of Management, Multimedia
University, Malaysia-63100 Cyberjaya; Tel: +60 3 8312 5653, E-mail:
cwchong@mmu.edu.my
Professor Dr. Peter Charles Woods is the Professor of Knowledge Management in
the Faculty of Creative Multimedia,
Prof. Dr. Peter Charles Woods, Faculty of Creative Multimedia, Multimedia University – Knowledge Management Centre, Malaysia-63100 Cyberjaya; Tel: +60 3 8312 5000/5018 Fax: +60 3 8312 5022; E-mail: p.woods@mmu.edu.m