Presented at IPSC2012, Kota Kinabalu, Sabah

1. PUBLIC SECTOR READINESS
TOWARDS DATA MINING
TECHNOLOGY
Mohd Shaari Abd Rahman, Hafiza Aishah Hashim
& Zalailah Salleh
Universiti Malaysia Terengganu (UMT)

2. Who am I
• Mohd Shaari Abd Rahman
facebook.com/drmsar
• Fakulti Pengurusan dan
Ekonomi, UMT
http://fpe.umt.edu.my
shaari@umt.edu.my

3. Research Questions
• RQ 1: Do management and staff in the
Malaysian public sector have an
understanding of the concept of data
mining and ready to accept the
techniques of data mining in day-to-day
activities?

4. Research Questions
• RQ 2: Do variables such as gender, level
of education, job function and working
experiences (year) differences affect
public servants’ readiness toward data
mining?

5. What is Data Mining?
Process that allows the thorough
analysis of the data to draw out the
information (including patterns and
relationships) that will allow the
provision of required information to
users and enhance the decision-making
process.

6. Data Mining Readiness
• Optimism.
• Innovativeness.
• Perceived Ease of use.
• Perceived usefulness.

7. Individual Differences
• Variables such as age, education, gender, and
position are determinants of innovativeness.
• Individual differences played a crucial role in
the implementation of any technology and
has been a recurrent research theme in
various field/disciplines.

8. Findings

9. Awareness
• 14% used the term in
organisation.
• Over 50% were not know the
term.
• Over 80% were not sure.

10. Influencing Factor to employ data mining
% Agreement
100
80
60
80 90 92
40
50
20
0
Technological Organisational Human External
Resources

11. Readiness
• Public servants are receptive toward data
mining technology.
• Positive view of technology, a tendency to be
a pioneer, perceived the technology to be
useful and easy to use. All four components
of readiness suggested was found to be
positive and significant.

12. • Results found no difference in gender
Levene’s Test for
Equality of t-test for Equality of Means
Variances
Sex n Mean Std. Deviation
F Sig t df Sig
(2 tail)
Male 61 4.0426 .44402
2.866 .093 .946 130 .346
Female 71 3.9592 .55281
• The results may imply that technological experiences
and personal involvement with such technology which
have been given similar opportunity between genders
might as well eliminate differences between it.

13. • Results found significant difference between
different levels of education’s group
ANOVA results
Level of education n Mean Std. Deviation F Sig
Master's degree 27 4.3111 .36829
First Degree/equivalent 72 3.8792 .50933 7.934 .001
Diploma and lower 33 4.0000 .49497
Mean
Difference
(I) Education (J) Education (I-J) Sig.
Master's Degree First
.43194 .000
Degree/Equivalent
Diploma and lower .31111 .037
First Master's Degree
-.43194 .000
Degree/Equivalent
Diploma and lower -.12083 .458
Diploma and lower Master's Degree -.31111 .037
First
.12083 .458
Degree/Equivalent

14. • Results found no difference between job function
ANOVA
F Sig
Job function n Mean Std. Deviation
Accounting 49
4.0347 .46929
Finance 19
3.9789 .55536
.638 .592
Information Management 9
4.2333 .31225
Auditing 39
4.0410 .42718
• A similar no significant differences in readiness among
different job function were also found in banking
sector in Malaysia (Dahlan et al., 2002).

15. • Results found a significant readiness difference between
among different working (years) experience.
ANOVA
Year of experience n Mean Std. Deviation F Sig.
< 4 Years 65 3.8831 .55073
4-6 years 31 4.2806 .47358 7.218 .001
>6 years 34 3.9529 .33955
Mean
Difference
(I) Experience (J) Experience (I-J) Sig.
< 4 Years 4-6 years
-.39757(*) .001
>6 years
-.06986 .776
4-6 years < 4 Years
.39757(*) .001
>6 years
.32770(*) .020
>6 years < 4 Years .06986 .776
4-6 years
-.32770(*) .020

16. Conclusions
• Awareness among public sector is rather low
• Results indicated high level of optimism,
innovativeness, perceptions of ease of use and
usefulness towards data mining technology.
• Results found no difference in gender and job
function in terms of readiness to implement data
mining.
• There is a difference readiness to implement data
mining among different level of education and
working experience (years).