# The Multifaceted Nature of Online MBA Student Satisfaction and Impacts on Behavioral Intentions

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5 the course (see Table 3). Satisfaction with student-to-student interaction and online tools were not valuable predictors according to these statistics. The canonical discriminant function (as independent predictors) and factor matrix coefficients (as joint predictors) showed that satisfaction with faculty practices, course materials, and learning practices are the only predictors of intention to recommend the course. The eigenvalue reports the Pearson correlation between the discriminant scores and the dependent variable (intention to recommend the course). An eigenvalue of 0.77 explains 100% of the variance in the prediction equation, and the canonical correlation of 0.66 represents a significant correlation of the independent variables with the dependent variable. The effect size is calculated by squaring the canonical correlation and is medium-sized by Cohen s (1988) standards (d = 0.44). H 4 was partially supported. When asked, Will you recommend the university to others? 254 students (91.7%) responded yes, and 23 (8.3%) responded no. Consistent with H 4, students intention to recommend the university to others was predicted by their satisfaction with online tools. Contrary to our expectations, students intention to recommend the university was negatively and marginally predicted by satisfaction with course materials (p <.09), meaning that the higher the satisfaction with course materials, the higher the chance that students may not recommend the university. Also contrary to H 4 was the finding that students satisfaction with learning practices predicted their intention to recommend the university. Wilks s lambda and F values showed the relative importance of online tools, course materials, and learning practices as predictors (see Table 4). Students satisfaction with faculty practices and TABLE 2. Discriminant Analysis Statistics for Hypothesis 2: Intention to Recommend the Faculty Structure Canonical matrix function (pooled Category Wilks s λ F p coefficient predictors) Faculty practices Learning practices Course materials Student-to-student interaction Online tools Note. For all Fs, df = 274. TABLE 3. Discriminant Analysis Statistics for Hypothesis 3: Intention to Recommend the Course Structure Canonical matrix function (pooled Category Wilks s λ F p coefficient predictors) Faculty practices Learning practices Course materials Student-to-student interaction Online tools Note. For all Fs, df = 275. student-to-student interaction was not a valuable predictor. The canonical discriminant function (as independent predictors) and factor matrix coefficients (as joint predictors) verified the Wilks s lambda and F statistics. Students satisfaction with online tools was the strongest predictor; students satisfaction with learning practices satisfaction was the next strongest predictor; and students satisfaction with course materials was a negative, although marginal, predictor of students intention to recommend the university to others An eigenvalue of 0.09 and canonical correlation of 0.29 specify that the relation between the independent and dependent variables is not as strong as that relation in H 3 and H 4. The squared canonical correlation gives an effect size of 0.08, which also proposes a weak relation. Wilks s lambda is also large (λ = 0.92) and indicates that the function does not effectively discriminate between the yes and no responses. However, the model was a better predictor than what one would have expected to occur by chance, χ 2 (5, N = 276) = 23.95, p <.00. Summary In summary, H 4 was partially confirmed. Satisfaction with online tools predicted students recommend the university. Unexpectedly, students intentions were also predicted positively by their satisfaction with learning practices and negatively (although marginally) by their satisfaction with course materials. However, the discriminant function was weak and lacked statistical power, which was most likely because of the small number of no responses in the dependent variable. DISCUSSION The present study is an empirical test of an adaptation of the virtual learning environment models introduced by Piccoli et al. (2001) and Alavi and Gallupe (2003). First, we met our research goals by establishing that student satisfaction comprised five distinct factors: satisfaction with faculty practices, course materials, learning practices, studentto-student interaction, and online tools. 308 Journal of Education for Business

6 Second, we met our goals by showing that students behavioral intention to recommend the course, faculty, and university were predicted by different types of satisfaction. Figure 2 shows the final research model, revised to represent the study results. Faculty practices predicted students intention to recommend a faculty member to others, as we expected. However, course materials did not predict students intention to recommend faculty. Unexpectedly, learning practices predicted student intention to recommend faculty TABLE 4. Discriminant Analysis Statistics for Hypothesis 4: Intention to Recommend the University Structure Canonical matrix function (pooled Category Wilks s λ F p coefficient predictors) Faculty practices Learning practices Course materials Student-to-student interaction Online tools Note. For all Fs, df = 275. at a marginal significance level. Learning practices questions included topics such as practical application of course materials, learning critical thinking skills, and providing a thought provoking course. These aspects of the course may be attributed to the faculty member s discretion, especially if the faculty member expresses to students that he or she uses certain practices or methods specifically for teaching critical thinking or practical application. However, course materials was not important in predicting students intention to recommend faculty. These questions, such as satisfaction with the current nature and usefulness of reading materials, may be attributed more to the institution. The faculty member may even apologize to students for the book choice, as he or she may not have chosen the book. These informal apologies may become typical in a situation in which the faculty does not pick a standardized course book or materials. Participants Practices Outcomes Outcomes (attitudes) (behavioral) Faculty Faculty practices Timely Interactive Knowledgeable Course or materials Organized Useful Applied Used technology Current Faculty practices Course materials recommend faculty to others Students in full- and parttime online MBA Learning practices Applied Critical thinking Learning practices recommend course to others Student-to-student interaction Instructive Improved group skills Student-tostudent recommend university to others Administration or IT Web access Online tools Online tools FIGURE 2. Virtual learning environment research model: Study results (Adapted with permission from G. Piccoli, R. Ahmed, & B. Ivers, 2001, and M. Alavi & R. B. Gallupe, 2003). May/June

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