Citekey: @Sun2008b

Sun, P.-C., Tsai, R. J., Finger, G., Chen, Y.-Y., & Yeh, D. (2008). What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183–1202.


Building on earlier research on e-learning, this study aims to build a model of learner satisfaction involving multiple dimensions including course, instructor, learner, tech, design, and environmental dimensions. The presentation of the study is succinct (as one could normally expect from the journal), in some cases too succinct to clarify doubts about the dataset (e.g., potential sampling bias) and meaningfulness of results (correlations).

Notes to me as an instructor:

  • Stats involved: correlations, multiple regression
  • Discussion points
    • conception of e-learning (the use of theories)
    • the impact of the specific sample on results
    • how this study could be useful?


A survey was conducted to investigate the critical factors affecting learners’ satisfaction in e-Learning. The results revealed that learner computer anxiety, instructor attitude toward e-Learning, e-Learning course flexibility, e-Learning course quality, perceived usefulness, perceived ease of use, and diversity in assessments are the critical factors affecting learners’ perceived satisfaction. (p. 1183)

  1. Introduction (p. 1183)

E-Learning is the use of telecommunication technology to deliver information for education and training. (p. 1183)

  1. Prior studies of e-Learning (p. 1184)

E-Learning is basically a web-based system that makes information or knowledge available to users or learners and disregards time restrictions or geographic proximity. Although online learning has advantages over traditional face-to-face education (Piccoli et al., 2001), concerns include time, labor intensiveness, and material resources involved in running e-Learning environments. (p. 1184)

Many researchers from psychology and information system fields have identified important variables dealing with e-Learning. Among them, the technology acceptance model (Ajzen & Fishbein, 1977; Davis, Bagozzi, & Warshaw, 1989; Oliver, 1980), and the expectation and confirmation model (Bhattacherjee, 2001; Lin, Wu, & Tsai, 2005; Wu et al., 2006) have partially contributed to understanding e-Learning success. These models tended to focus on technology. (p. 1184)

in Table 1. Six dimensions are used to assess the factors, including student dimension, instructor dimension, course dimension, technology dimension, design dimension, and environment dimension. (p. 1184)

Under the six dimensions previously identified, thirteen factors were involved. In the learner dimension those factors are learner attitude toward computers, learner computer anxiety, and learner Internet self-efficacy. The factors of instructor response timeliness and instructor attitude toward e-Learning were identified in the instructor dimension, and e-Learning course flexibility, e-Learning course quality in the course dimension. The technology dimension factors were technology quality and Internet quality. Finally, perceived usefulness and perceived ease of use were identified in design dimension and diversity in assessment and learner perceived interaction with others in the environmental dimension. These factors discussed by previous researchers cover nearly every aspect of e-Learning environments; however, they have never been integrated into one framework subject to examination for validation and relationship. This research develops such a framework including those factors shown in Fig. 1. (p. 1184)

Fig. 1. Dimensions and antecedents of perceived e-Learner satisfaction. (p. 1185)

  1. Variables and research model (p. 1185)

4.2. The subjects and the procedure (p. 1189)

Unclear whether the participants would naturally bias responses to some of the variables. For example, if there were no peer interactions designed in those 16 courses, e-learners may have no clue about how to respond to related survey questions. (p. 1189)

E-Learner volunteers enrolled in 16 different e-Learning courses at two public universities in Taiwan participated in the study. A total of 645 surveys were distributed by email. The initial and follow-up mailing generated 295 usable responses, resulting in a response rate of 45.7%. (p. 1189)

Table 2 summarizes the demographic profile and descriptive statistics of the respondents. (p. 1190)

  1. Data analysis (p. 1190)

5.1. Reliability and validity analysis (p. 1190)

Table 3 Descriptive statistics, correlation,a reliablilitiesb among study variables (n = 295) (p. 1191)

5.2. Pearson correlation analysis (p. 1191)

more discussion needed here
The authors should have briefly discussed the correlations here. What do they imply? How do correlations inform the regression analysis? etc. (p. 1192)

5.3. Hypothesis testing (p. 1192)

  1. Discussion (p. 1193)

From stepwise multiple regression analysis, seven variables are proven to have critical relationships with e-Learner satisfaction, namely learner computer anxiety, instructor attitude toward e-Learning, e-Learning course flexibility, course quality, perceived usefulness, perceived ease of use, and diversity in assessment. The results suggested that 66.1% (adjusted R2 = 66.1%, F-value = 82.96, p < .001) of the perceived e-Learner satisfaction’s variance can be explained by those seven critical variables. (p. 1193)

6.1. Learner dimension (p. 1193)

Because the mentality of treating computers as a necessary tool has matured, users’ attitude, efficacy or skills should no longer be considered an issue in the e-Learning environment. (p. 1194)

anxiety might still exist with certain users. (p. 1194)

6.2. Instructor dimension (p. 1194)

Since not every instructor is interested in teaching online, institutions should select instructors carefully. Teaching online differs from face-to-face education. Professional expertise should not be the sole criterion in selecting online instructors. (p. 1194)

6.3. Course dimension (p. 1194)

Course flexibility and quality are both proven to be significant in this research. Flexibility of an e-Learning course is a strong indication of student satisfaction. (p. 1194)

Of all independent variables, course quality has the strongest association with satisfaction. It includes overall course design, teaching materials, interactive discussion arrangements, etc. (p. 1194)

6.4. Technology dimension (p. 1195)

it is reasonable to claim that the technologies used in e-Learning environments are fairly mature. (p. 1195)

6.5. Design dimension (p. 1195)

perceived usefulness by learners significantly influences their satisfactio (p. 1195)

Perceived ease of use also has a significant impact on e-Learner satisfaction. Users’ notion of ease of use is an important antecedent to perceptions of satisfaction. (p. 1195)

6.6. Environment dimension (p. 1195)

Out of the two factors involved in environmental dimension, diversity in assessment has a significant impact on perceived e-Learner satisfaction. (p. 1195)

This study provides insights for institutions to strengthen their e-Learning implementations and further improve learner satisfaction. (p. 1196)

First, the research proposes an integrated model covering a variety of factors influencing e-Learners’ satisfaction; it might not be comprehensive due to the limitations of time and resources. Second, this work focuses on metrics from a specific digital learning system. The variance in different systems is not further investigated. Third, the dependent variable of this study is a single indicator, perceived e-Learner satisfaction. (p. 1196)

Fourth, the statistical methods used in this study are based on traditional assumptions; thus our results are established with these assumptions as a base. Finally, this research used stepwise multiple regression analysis to test the significance of variables. In the future, other statistical methods such as SEM (e.g., LISREL, EQS, PLS), or neural network may be employed to explore cause/effect relationship among variables. (p. 1196)

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