Tugasan 1:ulasan jurnal
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Tugasan 1:ulasan jurnal Document Transcript

  • 1. KAE 3013 KPT 6044 : PEMBELAJARAN BERASASKAN ELEKTRONIK DAN WEB TUGASAN INDIVIDU ULASAN JURNAL DISEDIAKAN OLEH : i-THINK NAMA SUZANA BT. SAUDIN NO. MATRIK NO. TELEFON M20121000514 0122391423 PENSYARAH : PROF.MADYA DR. ABD LATIF
  • 2. SYSTEM CHARACTERISTICS, SATISFACTION AND E-LEARNING USAGE: A STRUCTURAL EQUATION MODEL (SEM) 1 T. Ramayah School of Management Universiti Sains Malaysia Jason Wai Chow Lee School of Business Nilai University College, Malaysia aramayah@usm.my bwclee@nilai.edu.my
  • 3. 1.0 PENGENALAN Pembelajaran elektronik (e-learning) adalah didokumenkan dalam kesusasteraan IT kerana mengikut Roca et al. (2006), ia semakin diberikan "persekitaran yang baru dan pengalaman pembelajaran yang berlaku juga di luar bilik darjah, kurikulum dan format berasaskan teks". E-pembelajaran biasanya melibatkan penyampaian kandungan kursus dengan menggunakan media elektronik, seperti Internet, Intranet, extranet, siaran satelit, pita audio / video , TV interaktif dan CD-ROM (Urdan & Weggen, 2000). Khan (2001)menerangkan e-pembelajaran sebagai sinonim dengan pembelajaran berasaskan web (Jejaring), latihan berasaskan Internet (IBT), maju pembelajaran diedarkan (ADL), Arahan berasaskan web (WBI), pembelajaran dalam talian (OL) dan pembelajaran terbuka / fleksibel (OFL ). Ramayah et. al.(2010) menyatakan bahawa di institusi pengajian tinggi di Malaysia, langkah-langkah pelaksanaan epembelajaran yang berjaya adalah kepuasan pengguna dan penerusan penggunaan kemudahan untuk penyelidikan dan pengajaran dan pembelajaran. Wang et al. (2007) berhujah bahawa ia adalah sukar untuk menangkap dimensi penuh sistem kejayaan e-pembelajaran dalam sesebuah organisasi kerana banyak kombinasi langkah-langkah individu, pengurusan dan organisasi boleh diguna pakai. Tambahan pula, pemeriksaan sistem epembelajaran yang berjaya dalam konteks IS adalah sukar kerana pemain yang berbeza atau pihak berkepentingan yang berbeza melihat manfaat daripada sistem (DeLone & McLean, 2003). Kajian ini adalah dari perspektif pelajar yang menggunakan sistem e-pembelajaran yang pada asasnya berasaskan web dalam alam dan kerana ia juga merupakan fenomena sistem komunikasi dan maklumat (IS) Wang et al. (2007), penulis berpendapat bahawa adalah wajar untuk mengkaji pelaksanaannya yang berjaya diperluaskan dengan menggunakan DeLone & McLean’s (2003) Model Kejayaan IS.Ia telah dicadangkan bahawa "walaupun sifat multidimensi dan jangka IS kejayaan, usaha hendaklah dibuat untuk mengurangkan dengan ketara bilangan langkah-langkah yang digunakan untuk mengukur kejayaan IS, supaya hasil penyelidikan boleh dibandingkan dan penemuan disahkan" (DeLone & McLean, 2003). Oleh itu, kajian ini melaksanakan model dipermudahkan DeLone dan ini McLean (2003) model dilanjutkan untuk memeriksa melalui model persamaan struktur (SEM), peranan kualiti (kualiti perkhidmatan, maklumat yang berkualiti dan kualiti sistem) dalam mempengaruhi kepuasan pengguna dan penggunaan berterusan daripada sistem e-pembelajaran di universiti awam di Malaysia.
  • 4. 2.0 Persoalan Kajian H1: System quality has a positive relationship with user satisfaction. H2: Information quality has a positive relationship with user satisfaction. H3: Service quality has a positive relationship with user satisfaction. H4: User satisfaction is positively related to usage continuance. H5: System quality is positively related to intention to use. H6: Service quality is positively related to intention to use. 3.0 RESEARCH METHOD 3.1 Data Collection Data was collected from 250 students from a public university in Penang, Malaysia using a structured questionnaire which was derived from the literature. The questionnaire consisted of 4 sections. The first section collected the demographic data, the second section elicited information about information quality, service quality and system quality, section three measured user satisfaction and the last section measured continuance intention. Since there was no list available, non-probability convenient purposive sampling method was used. The sample selected were students who have used the e-learning system as the measures required them to rate the system, information and service quality as well as the satisfaction and continuance intention. 3.2 Measures The measures were all adapted from published literature. The measures for service quality, information quality and system quality were from Lee and Lee (2008). Satisfaction measures were adapted from Spreng et al. (1996) whereas intention to use was adapted from Venkatesh et al. (2003). 3.3 Sample Profile The demographics of the respondents tabulated in Table 1 were derived from descriptive analysis. Females (69.6%) outnumber males (30.4) in this study which somewhat reflects the gender ratio of undergraduates for public universities in Malaysia. About 70% of the students were from the Arts stream while 30% were from Science. More than 66% of students stayed in
  • 5. the campus and the rest outside the campus. About 50% of students used the e-learning system for between 1-5 hours per day while about a quarter used the system for less than an hour per day. Twenty-eight percent of students claimed they belonged to the slightly frequent to extremely frequent user group of the system. Table 1: Demographics of respondents Gender Frequency Percent Male 76 30.4 Female 174 69.6 Malay 72 28.8 Indian 24 9.6 Ethnicity Chinese Others 148 59.2 6 2.4 Stream Arts Science 174 69.6 76 30.4 166 66.4 84 33.6 Residence In campus Outside campus
  • 6. Hours Frequency Percent Almost never 6 2.4 < 1 hour 62 24.8 1 – 5 hours 124 49.6 6 – 10 hours 38 15.2 11 – 15 hours 14 5.6 6 2.4 Extremely infrequent 16 6.4 Quite infrequent 50 20.0 Slightly infrequent 64 25.6 Neither infrequent nor frequent 50 20.0 Slightly frequent 46 18.4 Quite frequent 14 5.6 Extremely frequent 10 4.0 More than 20 hours Frequency of use 4.0 DATA ANALYSIS AMOS version 16.0 was used to analyze the hypotheses generated. AMOS and LISREL are the most widely used Structural Equation Modeling (SEM) software available in the market. Since we considered AMOS 16.0 to be more user friendly this software was adopted. We followed the 2-step analytical procedure suggested by Hair et al.(2010) whereby the measurement model was evaluated first and then the structural model was assessed next.
  • 7. 4.1 Measurement Model Convergent validity measures the extent to which the items of a scale that are theoretically related are correlated. According to Hair et al. (2010) a composite reliability of 0.70 or above and an average variance extracted of more than 0.50 are deemed acceptable. As can be seen from Table 2, all the composite reliability values are above 0.70 except for intention which is acceptable as there are only 2 measurement items. The average variance extracted is all above 0.50. Therefore, we can conclude that convergent validity has been established. Next, we assessed the discriminant validity which is the extent to which a measure is not a reflection of some other variable. This can be established by low correlations between the all the measure of interest and the measure of other constructs. Also according to Fornell and Larcker (1981) when the square root of the average variance extracted is greater than its correlations with all other constructs then discriminant validity has been established. (see Table 3) Table 2: Result of CFA for measurement model Convergent validity Factor Internal reliability loading Construct Information Quality Item 0.896 0.66 0.78 0.54 0.74 SQ1 0.901 0.73 SQ2 0.68 SERQ2 0.77 0.53 0.74 SERQ3 0.51 0.76 SERQ1 0.75 0.64 SQ3 User Satisfaction variance 0.80 IQ3 Servis Quality reliability extracted IQ2 System Quality Average Cronbach alpha IQ1 Composite 0.77 US1 US2 0.911 0.67 0.79 0.76 0.76
  • 8. US3 Intention to Use BI1 0.71 0.837 0.71 BI2 0.68 0.52 0.73 Table 3: Discriminant validity of constructs Constructs (1) (2) (3) (4) (5) (1) Information Quality System Quality Service Quality User Satisfaction Intention 0.734 0.250 0.146 0.130 0.082 (2) (3) 0.714 0.166 0.232 0.104 0.728 0.090 0.063 (4) (5) 0.872 0.229 0.721 _____________________________________________________________________________________ 4.2 Structural Model The structural model was estimated using the maximum likelihood method (MLE). Fig. 2 presents the results. The fit statistics are presented in Table 3. All the fit measures from this study are above the recommended values suggesting a good model fit. The model accounts for 45% of the variance explained in user satisfaction and 44% of the variance in user intention. All the paths are significant at the 0.01 level. Information quality has the strongest effect on user satisfaction whereas user satisfaction has the strongest effect on user intention. Thus the results of the structural model have established support for H1, H2, H3, H4, H5 and H6 (See Table 4). Table 3: Fit indices Fit Measures df x2 x2/df GFI AGFI CFI RMSEA NNFI (TLI) Study 1 2.595 2.595 0.996 0.978 0.997 0.080 0.972 Recommended values ≤ 3.00 ≥ 0.90 ≥ 0.80 ≥ 0.90 ≤ 0.08 ≥ 0.90
  • 9. Table 4 summarizes the results of hypotheses testing in this study. Table 4: Hypotheses testing Hypothesis Critical ratios (CR) p-value Decision H1: System quality has a positive relationship with 3.256 0.001 Supported 5.399 0.000 Supported 2.948 0.003 Supported 5.069 0.000 Supported 2.837 0.005 Supported 4.697 0.000 Supported user satisfaction. H2: Information quality has a positive relationship with user satisfaction. H3: Service quality has a positive relationship with user satisfaction. H4: User satisfaction is positively related to usage continuance. H5: System quality is positively related to intention to use. H6: Service quality is positively related to intention to use. 5.0 CONCLUSION In this study, we found that system quality, information quality and service quality are significant factors influencing user satisfaction in using an e-learning system. User satisfaction is also found to be significant in affecting user’s intention to use. The findings provided by the study may enable the creators of e-learning systems to think seriously on these factors that will affect user satisfaction. In addition, this study may provide a direction as to how satisfaction can be cultivated among users in order to encourage them to use the e-learning system. The findings provided by the study may give empirically justified foundation for the creators to develop strategies to enhance their e-learning system’s quality by focusing on the user satisfaction. By understanding the determinants of user satisfaction, appropriate actions can be taken to increase the users’ perceptions of their experience on adoption of the e-learning system. In short, continued research is needed to improve this study and to address its limitations. It is hoped that this study will give a preliminary insight and understanding on user satisfaction and behavioral intention in order to maximize the actual use of the e-learning system.