Socio-demographic characteristics
A total of 659 (92.17% response rate) postgraduate medical and health science students participated in this study, including 519 males (78.8%) and 140 females (21.2%). Approximately 54.6% of the study participants were 25–29 years old, and approximately 0.9% of the study participants were 40 years old or older. Approximately 51.4% of participants had incomes between 10,000 and 15,000 ETB. The majority of respondents (54.8%) had less than 2 years of work experience. Approximately 29.7% of the respondents were second-year postgraduate health science students. A total of 5.6% of the respondents were resident 4 (R4) medicine specialty students (Table 1). The greatest percentages of respondents (10.2%) were from the gynecology department, and the smallest percentages of respondents (0.2%) were from the integrated emergency surgery and obstetrics department. .
Multicollinearity test
SEM analysis was used to evaluate the hypotheses after evaluating the measurement model’s validity and ensuring that there were no strong relationships between the exogenous constructs and that collinearity was assessed. Multicollinearity was found to be nonexistent in this investigation (Table 2).
Measurement model assessment
Evaluation of the measurement model involves checking the model fit, internal consistency, discriminant validity, and convergent validity of indicators/items using CFA (Fig. 4).
Confirmatory factor analysis
Reliability and validity of the construct
The results shown in Table 3 are the square root of the AVE of the construct, and other values refer to the significant correlation between constructs. The values in bold (diagonal values) are greater than the other values in the columns, and the raw and HTMT ratios are less than 0.9. As a result, the discriminant validity of the model’s constructs has been achieved (Tables 3 and 4).
Table 5 shows that the Cronbach’s alpha and composite reliability are greater than 0.70 for all the constructs. The AVE values are greater than 0.70 for all the constructs. All of the constructs, therefore, had strong convergent validity.
Kaiser‒Meyer‒Olkin test and Bartlett’s test of sphericity
We examined the findings of Bartlett’s sphericity test and the Kaiser-Meyer-Olkin (KMO) sample adequacy assessment. The total KMO (0.89) shows excellent partial correlation, and the Bartlett’s test of sphericity is significant.
Goodness of fit statistics for path analysis
The results in Table 6 show that the values of the fitness model met the required level.
Experience with using the internet, smartphones and computers
Approximately 83.8% of the 659 respondents had more than 6 years, and approximately 2.3% of the study participants had between 1 and 3 years of experience using mobile devices. Approximately 76.8% of the participants owned computer/laptop, smartphone and tablet ICT devices, and 0.8% of the students owned tablets. Approximately 75.1% of respondents used mobile data. Additionally, a minimum of 24.9% of respondents used broadband internet for internet connections. Approximately 93.2% of the respondents were comfortable using a computer, laptop, smartphone, tablet, or web application, and 6.8% of the respondents were not comfortable (Table 7).
Acceptance of using e-learning
In this study, 400 (60.7%; 95% CI: [56.9–64.4], p < 0.001) postgraduate medical and health science students scored above the median. Three questions with five Likert scales were used to assess the acceptance of e-learning, and the median score was 12, with a standard deviation of 2.95. The score ranged from 3 to 15, with 15 being the highest possible score. Therefore, 60.7% of the students agreed to use an e-learning system.
Factors associated with acceptance of using e-learning
Exogenous constructs such as self-efficacy, accessibility and facilitating conditions explained 35.0% of the perceived ease of use construct, which has an R2 of 0.35. Self-efficacy, accessibility, facilitating conditions and perceived ease of use explained 61.1% of the variance in perceived usefulness, for which the R2 value was 0.61. Perceived usefulness and perceived ease of use explained 52.0% of the variance in the attitude construct, with an R2 of 0.52. perceived usefulnessfulness, perceived ease of use and attitude explained 63.0% of the endogenous construct ( acceptance to use the e-learning construct), with an R2 of 0.63. According to the R2 value is considered high when it is greater than 0.67, moderate when it is between 0.33 and 0.67, and weak when it is between 0.19 and 0.33 (Table 8).
The aforementioned hypotheses were tested together using structural equation modeling (SEM). SEM analysis revealed that attitude had the most substantial effect on the intention to use e-learning, which was greater than the effects of the other predictors, and facilitating conditions had the most substantial effect on the perceived ease of use of e-learning. Additionally, self-efficacy had the most substantial effect on the perceived usefulness of e-learning, and perceived usefulness had the most substantial effect on the attitude toward the use of e-learning among students (Fig. 5).
SEM for predictors of acceptance of using e-learning among postgraduate medical and health science students at first-generation universities in the Amhara region, Ethiopia, 2023
The results showed that accessibility (β = 0.231, 95% CI: [0.154, 0.308], p < 0.01), self-efficacy (β = 0.156, 95% CI: [0.042, 0.269], p < 0.01) and facilitating conditions (β = 0.361, 95% CI: [0.246, 0.472], p < 0.01) had direct effects on students’ perceived ease of use, supporting hypotheses H1a, H2a and H3a, respectively. Additionally, facilitating conditions (β = 0.274, 95% CI: [0.179, 0.381], p < 0.01), self-efficacy (β = 0.451, 95% CI: [0.346, 0.547], p < 0.01) and perceived ease of use (β = 0.212, 95% CI: [0.127, 0.304], p < 0.01) had direct effects on students’ perceived usefulness, which supports hypotheses H1b, H2b and H5a, respectively. In Contrast Accessibility (β = -0.030, 95% CI: [-0.087, 0.026], p value = 0.280) had no direct effect on students’ perceived usefulness, and Hypothesis H3b was not supported.
PEOU (β = 0.167, 95% CI: [0.078, 0.255], p < 0.01) and PU (β = 0.613, 95% CI: [0.521, 0.699], p < 0.01) had direct effects on students’ attitude, which supports hypotheses H5b and H4b, respectively. PEOU (β = 0.210, 95% CI: [0.118, 0.299], ATT (β = 0.377, 95% CI: [0.255, 0.496], p < 0.01) and PU (β = 0.332, 95% CI: [0.209, 0.455], p < 0.01) had direct effects on students’ acceptance of e-learning, supporting hypotheses H5b, H6a and H4a, respectively (Table 9).
Mediating effects
Table 10 was generated by estimating the specific indirect effect path estimand algorithm feature in AMOS software. There are three mediators, PU, PEOU and ATT, among the seven variables used in the proposed research model. The table shows that there are 35 indirect effects. In three cases (ACC ◊ PU ◊ ATT, ACC ◊ PU ◊ ATT ◊ ACe and ACC ◊ PU ◊ ACe), mediating effects were found to be no significant in predicting acceptance of e-learning among postgraduate medical and health science university students in the context of e-learning. On the other hand, 32 indirect effects were found to be positive. Perceived usefulness (β = 0.131, P < 0.001), and perceived ease of use (β = 0.029, P < 0.01) significantly mediate the relationship between self-efficacy, and acceptance of e-learning. Accessibility had a positive indirect effect on acceptance of e-learning through perceived ease of use (β = 0.040, p < 0.01). Facilitating condition had a positive indirect on acceptance of e-learning through perceived ease of use (β = 0.070, p < 0.01), and perceived usefulness (β = 0.084, p < 0.001). Perceived ease of use had a positive indirect effect on acceptance of e-learning through perceived usefulness (β = 0.062, p < 0.001). Perceived ease of use had also a positive indirect effect on acceptance of e-learning through attitude (β = 0.055, p < 0.001). Perceived usefulness had also a positive indirect effect on acceptance of e-learning through attitude (β = 0.214, p < 0.001).
In most cases, PU alone does not have the ability to mediate the relationships between accessibility (ACC) and attitude (ATT), between accessibility (ACC) and attitude (ATT) and acceptance of e-learning (ACe), or between accessibility (ACC) and acceptance of e-learning (ACe) (Table 10).
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