PUBLIC SECTOR READINESS TOWARDS DATA MINING TECHNOLOGYMohd Shaari Abd Rahman, Hafiza Aishah Hashim & Zalailah Salleh Universiti Malaysia Terengganu (UMT)
Who am I• Mohd Shaari Abd Rahman facebook.com/drmsar• Fakulti Pengurusan dan Ekonomi, UMT http://fpe.umt.edu.my firstname.lastname@example.org
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?
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?
What is Data Mining? Process that allows the thoroughanalysis of the data to draw out theinformation (including patterns andrelationships) that will allow theprovision of required information tousers and enhance the decision-makingprocess.
Data Mining Readiness• Optimism.• Innovativeness.• Perceived Ease of use.• Perceived usefulness.
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.
Awareness• 14% used the term in organisation.• Over 50% were not know the term.• Over 80% were not sure.
Influencing Factor to employ data mining % Agreement 100 80 60 80 90 92 40 50 20 0 Technological Organisational Human External Resources
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.
• Results found no difference in gender Levene’s Test for Equality of t-test for Equality of Means VariancesSex n Mean Std. Deviation F Sig t df Sig (2 tail)Male 61 4.0426 .44402 2.866 .093 .946 130 .346Female 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.
• Results found significant difference between different levels of education’s group ANOVA resultsLevel of education n Mean Std. Deviation F SigMasters degree 27 4.3111 .36829First Degree/equivalent 72 3.8792 .50933 7.934 .001Diploma and lower 33 4.0000 .49497 Mean Difference(I) Education (J) Education (I-J) Sig.Masters Degree First .43194 .000 Degree/Equivalent Diploma and lower .31111 .037First Masters Degree -.43194 .000Degree/Equivalent Diploma and lower -.12083 .458Diploma and lower Masters Degree -.31111 .037 First .12083 .458 Degree/Equivalent
• 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).
• Results found a significant readiness difference between among different working (years) experience. ANOVAYear of experience n Mean Std. Deviation F Sig.< 4 Years 65 3.8831 .550734-6 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 .7764-6 years < 4 Years .39757(*) .001 >6 years .32770(*) .020>6 years < 4 Years .06986 .776 4-6 years -.32770(*) .020
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).