MEASURING INFORMATION, SYSTEM AND SERVICE QUALITIES FOR THE EVALUATION OF E-LEARNING SYSTEMS IN PAKISTAN.
Byline: F. Kanwal and M. RehmanABSTRACT: Higher education institutes have integratede-learning systems (ELS)in their educational infrastructure. The development and implementation of ELS needs continuous quality evaluation and assessment to attract more learners.A conceptual model consisting of system, service and information qualities was evaluated to measure the user satisfaction. Data was collected from 356 learners including Post Graduate (52%) and Graduate (48%)students of Virtual University, Pakistan. To investigate the research hypotheses, Structural Equation Modelling (SEM) was employed. The empirical analysis indicated that information quality (0.38, p<0.05), service quality (0.14, p<0.05)and system quality(0.48, p0.70 indicated the recommended threshold of Cronbach's a(Table-2).
The convergent validity was estimated by the methods of composite reliability and average variance extracted (AVE). The AVE is the amount of variance shared by the variables to the amount of variance in measurement errors (Fornell and Larcker, 1981). Whereas, the sum of variance and covariance of variables divided by total variance is the composite reliability. The composite reliability ranging from 0.82-0.91 depicted that all the itemssurpassed the threshold recommended value of 0.60. Therefore, the results demonstrated the acceptable reliability of the instrument items. Moreover, the values >0.50 of AVE showed that all the items exceeded the threshold value (Table-2).
Table-2: Reliability analysis and convergent validity.
###Cronbach's Value###AVE###Composite Reliability
###Constructs
###(> 0.70)###(> 0.50)###(> 0.60)
###Service Quality###0.911###0.597###0.864
###System Quality###0.861###0.550###0.820
###Information Quality###0.924###0.564###0.918
Structural Model Analysis: Having proven the reliability and convergent validity, Structural Equation Modelling (SEM) was performed for the hypotheses testing. SEM was considered to be a better approach for examining multiple equations of dependence relationship and testing the theories. It provided insight that allowed simultaneous examination of a series of dependent relationships among the variables of the model(Hair et al., 2013; Byrne, 2010 andFornell and Larcker, 1981). AMOS 20.0 was employed and the maximum likelihood method was adopted to evaluate the parameters of the hypothesized model based on variance-covariance matrices.
To assess the research model, a variety of fit indices should be considered as recommended for SEM applications. Table-3 showed the nine fit indices used and the threshold values indicated the acceptable model fit (Byrne, 2010). Table-3 indicated the threshold values of acceptable fit against the fit indicesof the hypothesized model of this study. The fit indices were within the acceptable range.
Table-3: Fit indices of hypothesized model.
Fit indices###Threshold values###Hypothesized model
2/df###<5 preferably 0.90###0.930
Comparative Fit Index###>0.90###0.976
Adjusted Goodness of Fit Index###>0.80###0.896
Normed Fit Index###>0.90###0.958
Parsimony Normed Fit Index###>0.60###0.720
Root Mean Square Error of Approximation###<0.08###0.058
Relative Fit Index###Close to 1###0.944
Root Mean Square Residual###Close to 0###0.035
The next step of data analysis in this study was to analyze the significance of the relationships in the hypothesized model. Table-4 illustrates the detailed results of the hypothesized relationships. The results of this study indicated that the endogenous variables like service quality (b=0.143 and p<.05), information quality (b=0.415 and p<.05) and system quality (b=0.450 and p1.96 depicted the significance level. The t-value for system, service and information qualities are 2.135, 2.804 and 2.036 respectively indicated the significance of path coefficients.
The R2 value and path coefficients are depicted in Figure 1. This study calculated the R2 which indicated the amount of variance endogenous variables explained by the exogenous variables. The strength of dependent and independent relationship were specified by the standardized path coefficients which were 0.48 for system quality, 0.14 for service quality and 0.38 for information quality.The t-values and path coefficients are also illustrated in Figure 1. The model explained that the 71% of variance in the construct were of satisfaction, thus supporting H1, H2, H3. Moreover, the path coefficients of each exogenous variable on endogenous variable were used to calculate the Total effect. The Total effect was measure by the following equation reported by(Hair et al.,2013)
Total effect of information quality = (0.38+0.078+0.302) = 0.76
Similarly, the Total effect of system quality and service quality was 0.79 and 0.61 respectively. Hence, all the exogenous construct are strong predictors of satisfaction.
The findings indicated that the system quality was significant in the assessment of an ELS. The finding was consistent with the results of prior studies conducted by (Mohammadi, 2015; Alsabawy et al., 2013; Eom et al., 2012; Hassanzadeh et al., 2012; Chen, 2010 and Ozkan and Koseler, 2009). System quality was the measure of technically sound and error-free system which in turn increased the satisfaction of learners for the use of ELS. Another hypothesis (H2) was also found to be significant where service quality was the direct and significant predictor of satisfaction towards the future use of ELS. The findings were consistent with the prior research conducted by (Mohammadi, 2015; Balaban et al., 2013; Wang and Chiu, 2011 and Ozkan and Koseler, 2009).
Table-4: Hypotheses testing results.
Hypotheses###Path###t-value###p-value###S.E###Results
H1###System###Quality###.450###2.135###.033###.211###Supported
###Satisfaction
H2###Service###Quality###.143###2.804###.005###.051###Supported
###Satisfaction
H3###Information###Quality###.415###2.036###.042###.204###Supported
###Satisfaction
Moreover, the results of the present research support H3 where learners' satisfaction was positively influenced by the information quality andthe findings were also consistent with the prior research carried out by(Mohammadi, 2015; Salem and Salem, 2015; Alkhalaf et al.,2012; Eom et al.,2012; Ozkan and Koseler, 2009 andRoca et al.,2006). Information quality like relevancy, completeness and accuracy of information and contents increased the learner's satisfaction. In addition, user friendly, required and up-to date information and content produced by the information system enhanced the satisfaction of the learner.
User satisfaction led to achieve the learners' educational goal. In this study, the variance explained by the satisfaction was 71%. The model was statistically significant and the results were consistent with the prior research reported by (Eom et al.,2012 and Wang and Chiu, 2011).
In a study conducted by (Ghiaset al., 2013) evaluated the three quality constructs to measure the ELS intention and inept to clarify the user satisfaction. (Niwaz et al., 2013)emphasized only the influence of service quality to evaluate the user satisfaction in job placement.Thequality assessment and evaluation were the endeavoring factors in the ELS enhancement and promotion.This research assertedthe values in the theoretical and practical implication of ELS assessment and evaluation.
In the last decade, Virtual University of Pakistanimproved itself to a high extent, but the advancement and continuous improvements were required to enhance the educational infrastructure. The need was to pay attention to the quality features of ELSlike service, information and system qualities to retain satisfaction of the learner and attract learners trust.
Conclusion: This study empirically examined the quality features influencing user satisfaction towards the use of e-learning system. Quality features like system, service and information qualitiespositively and significantly affected the user satisfaction. The findings indicated that designing educational contents with understandability, relevance, accuracy and consistency must evoke learner's interest. The availability of the e-learning systems, interaction and response time are recommended which significantly affect the user satisfaction for future use. Moreover, reliability, ease of use and efficiency positively affect the user satisfaction and enhances the learner's interest towards using the ELS.
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| Publication: | Pakistan Journal of Science |
|---|---|
| Geographic Code: | 9PAKI |
| Date: | Sep 30, 2016 |
| Words: | 2311 |
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