Presentation Source at Las Vegas Conference in February 23, 2008

1. “ The effects of LMS quality and e-learner’s characteristics regarding e-learner’s scholastic performance: A proposal for e-learning success model 2 ” ASBBS 15 th International Conference Feb. 21-24, 2008 Imperial Palace Hotel, Lasvegas, USA. Jong-Ki Lee (Research Professor, Kyungpook National University, South Korea) [email_address] http://LMS4U.kr

4. ISS Model transition D&M IS Success Model, 1992 Updated D&M IS Success Model, 2003 Seddon & Kiew Model, 1997 Pitt et al. Model, 1995 System quality Information quality use User satisfaction Individual impact Organizational impact Service quality System quality Information quality use User satisfaction Individual benefit Service quality PEOU Information quality Perceived usefulness User satisfaction System quality Information quality use User satisfaction Individual impact Organizational impact

5. Application of ISS Learning content quality Interaction service quality LMS quality Learning environment Satisfaction IT Adapted SERVQUAL Contextual quality Representational quality IT Adapted SERVQUAL IT Adapted SERVQUAL satisfaction

7. Research model IQ SVC SQ EMP SA RR SRS H8 H7 H6 H4 H2 SRE ESP H1 H3 H5 SA: e-learner’s satisfaction on LMS SQ: system quality on LMS SVC: service quality on interaction IQ: information quality on LMS EMP: e-learner’s empathy SRE: self-regulatory efficacy SRS: self-regulated learning strategy ESP: e-learner’s expected performance RR: real record performance

12. Scale Mehrabian & Epstein (1972 ) 6 EMP Real Record 1 RR Wang(2003) 3 SA Zimmerman & Martines-Pons (1988) 4 SRS Kim and Park (2001 ) 4 SRE Kettinger & Lee(1997) 4 SVC Lee et al.(2002) 7 IQ DeLone & McLean (1992) 4 SQ Researcher item Variable

13. Demographics table 2. Convergent validity analysis 0.6 2 Etc. 5.0 17 Art and physical 11.4 39 Engineering 8.5 29 Natural science 34.3 117 Social science 40.2 137 Cultural science Major 33.1 113 Senior 25.2 86 Junior 27.0 92 Sophomore 14.7 50 Freshman Grade 0.6 2 50 and above 0.9 3 45-49 0.6 2 40-44 0.9 3 35-39 2.3 8 30-34 31.1 106 25-29 63.6 217 20-24 Age 38.7 132 Female 61.3 209 Male Gender Percent(%) Frequency Item

14. Demographics (2) table 2. Convergent validity analysis 1.2 4 15 hours and above 5.9 20 7-14 hours 32.3 110 3-6 hours 52.5 179 1-2 hours 8.2 28 Less than 1 hour Computer use time (1day) 1.5 5 8 subjects and above 6.5 22 6-7 subjects 3.8 13 4-5 subjects 35.8 122 2-3 subjects 52.5 179 1 subject Courses taken during e-Learning career 1.8 6 6 times and above 10.6 36 4-5 times 42.8 146 2-3 times 44.9 153 First E-Learning career Percent(%) Frequency Item

15. Demographics (3) table 2. Convergent validity analysis 15.5 53 University graduation 7.3 25 College graduation 7.6 26 University dropout 68.3 233 High school graduation 1.2 4 Middle school graduation Degree 4.4 15 8 hours and above 7.9 27 5-7 hour 29.3 100 3-4 hour 55.4 189 1-2 hour 2.9 10 Less than 1 hour e-Learning time (1 week) Percent(%) Frequency Item

16. Convergent validity table 2. Convergent validity analysis 0.888 0.642 0.915 EMP 0.748 0.571 0.842 SRE 0.815 0.644 0.878 SRS 0.948 0.856 0.960 ESP 0.780 0.574 0.842 SQ 0.923 0.677 0.936 SVC 0.892 0.583 0.907 IQ 0.861 0.679 0.893 SA Cronbach α AVE Composite reliability Construct

17. Correlation coefficient of construct and AVE Symmetrical value is square root of AVE 1.000 0.053 0.179 0.260 0.356 0.139 0.197 0.140 0.255 RR 0.801 0.494 0.456 0.395 0.383 0.525 0.448 0.465 EMP 0.755 0.598 0.489 0.402 0.427 0.443 0.448 SRE 0.802 0.489 0.361 0.421 0.425 0.442 SRS 0.952 0.367 0.445 0.427 0.541 ESP 0.757 0.612 0.623 0.542 SQ 0.822 0.648 0.667 SVC 0.763 0.624 IQ 0.824 SA RR EMP SRE SRS ESP SQ SVC IQ SA Variable