“Antecedents promoting e-learner’s engagement behavior: Mediating effect of e-learner’s intention to use behavior”

Figures & data

Figure 1. Research model.

Table 1. Measurement items

Constructs	Measurement Variable	Sources
Performance Expectancy (PE)	PE1: I find E-learning useful for meeting my learning requirements. PE2: Using an E-learning platform enables oneself to accomplish learning quickly. PE3: Using E-learning increases my chances of attaining things that are important to me.	Venkatesh et al. (2003) & (Venkatesh et al., 2012);
Effort Expectancy (EE)	EE1: It is easy for me to become skilled at using E-learning platform. EE2: My interaction with the E-learning platform is clear and understandable. EE3: Learning to work with an E-learning platform is easy for me.
Social Influence (SI)	SI1: People who influence my behavior think that I should use E-learning platform. (People here refer to: lecturers, instructors, colleagues, friends, family members). SI2: People who are important to me think that I should use an E-learning platform. SI3: People whose opinion I value prefer that I use E-learning platform.
Facilitating Conditions (FC)	FC1: I have the necessary resources to use an E-learning platform. FC2: I have the knowledge that is necessary to use an E-learning platform. FC3: Necessary guidance was provided to me when I faced difficulty to use E-learning platform. FC4: The technology which is necessary for adoption of E-learning platform in my university is updated and modern.
Learner Self-Efficacy (SE)	SE1: I could complete my learning activities using an e-learning platform if I have never used a platform like it before. SE2: I could complete my learning activities using an e-learning platform if I have only software manuals for reference. SE3: I am confident of using the e-learning platform even if there is no one around to show me how to use it.
E-learner’s Intention to Use Behavior (IUB)	IUB1: I intend to use E-learning platform in the future. IUB2: I intend to engage in E-learning routinely. IUB3: I intend to use E-learning for upgrading my knowledge and skills.
E-learner’s Engagement Behavior (LE)	LE1: I set aside a regular time each week to work on the e-learning platform. LE2: I took notes while studying on an e-learning platform. LE3: I revisited my notes when preparing for e-learning related assessment tasks. LE4: I often searched for further information when I encountered something in the e-learning that puzzled me. LE5: When I had trouble understanding a concept or an example in e-learning, I went over it again until I understood it. LE6: If I watched a video lecture that I did not understand at first, I would watch it again to make sure I understood the content. LE7: I was inspired to expand my knowledge through an e-learning platform. LE8: I found e-learning interesting. LE9: I enjoyed learning through the e-learning platform. LE10: I often responded to other learners’ questions. LE11: I contributed regularly to course discussions. LE12: I shared learning materials (e.g., notes, multimedia, links) with other classmates in the e-learning platform.	Deng et al. (2019)

Table 2. Demographic profile

Demographics		N	Percentage
Age	Below 20	74	22.02
	Between 20 and Below 23	203	60.42
	23 and above.	59	17.56
Gender	Male	141	41.96
	Female	195	58.04
Year of Study	First-year	96	28.57
	Second year	184	54.76
	Third year	56	16.67
Course Specialisation	B. Com	79	23.51
	BBM	102	30.36
	M.Com	104	30.95
	MBA	51	15.18
Prior Experience	Less than one year	66	19.64
	1–2 years	106	31.55
	3–4 years	95	28.27
	5–6 years	43	12.80
	7–8 years	3	0.89
	8 years and above	1	0.30
	Not at all	22	6.55

Source: Author’s computation.

Table 3. Measurement model

Construct	Items	Mean	Factor Loading	Cronbach’s Alpha	AVE	Composite Reliability
Performance Expectancy	PE1	4.664	0.924	0.926	0.872	0.953
	PE2	4.708	0.941
	PE3	4.833	0.935
Effort Expectancy	EE1	4.801	0.889	0.871	0.794	0.920
	EE2	4.967	0.917
	EE3	5.080	0.866
Social Influence	SI1	4.821	0.806	0.851	0.769	0.909
	SI2	4.580	0.917
	SI3	4.560	0.903
Facilitating Condition	FC1	4.958	0.847	0.867	0.714	0.909
	FC2	5.030	0.872
	FC3	4.753	0.836
	FC4	4.839	0.825
Self-Efficacy	SE1	4.342	0.900	0.891	0.820	0.932
	SE2	4.494	0.926
	SE3	4.714	0.891
E-learner’s Intention to Use Behavior	IUB1	4.943	0.913	0.898	0.831	0.936
	IUB2	4.893	0.924
	IUB3	5.042	0.898
E-learner’s Engagement Behavior	LE 1	4.229	0.736	0.927	0.628	0.938
	LE 2	4.497	0.795
	LE 3	4.577	0.825
	LE 4	4.768	0.835
	LE 5	4.878	0.799
	LE 6	4.902	0.780
	LE 7	4.783	0.736
	LE 8	4.765	0.821
	LE 9	4.631	0.799
	LE 10*	4.262	0.589
	LE 11*	4.208	0.606
	LE 12*	4.229	0.548

Source: Author’s computation. *Notes: The factor loading for LE10, LE11 and LE12 ranged below the threshold i.e., 0.70; hence, three items of the LE were removed and excluded for further analysis.

Table 4. Discriminant validity: Fornell and Larcker

Construct	1	2	3	4	5	6	7
E-learner’s Intention to Use Behavior (1)	0.910
Effort Expectancy (2)	0.60	0.891
Facilitating Condition (3)	0.59	0.624	0.84
E-learner’s Engagement Behavior (4)	0.55	0.532	0.55	0.792
Performance Expectancy (5)	0.58	0.549	0.59	0.585	0.934
Self-efficacy (6)	0.56	0.525	0.47	0.524	0.582	0.906
Social Influence (7)	0.44	0.572	0.59	0.448	0.459	0.478	0.877

Source: Author’s computation.

Table 5. Discriminant validity—HTMT

Construct	1	2	3	4	5	6	7
E-learner’s Intention to Use Behavior (1)
Effort Expectancy (2)	0.681
Facilitating Condition (3)	0.668	0.708
E-Learner’s Engagement Behavior (4)	0.603	0.584	0.617
Performance Expectancy (5)	0.635	0.595	0.665	0.628
Self-Efficacy (6)	0.620	0.588	0.527	0.570	0.640
Social Influence (7)	0.498	0.671	0.681	0.494	0.487	0.538

Source: Author’s computation.

Table 6. Summary of results – Hypothesis testing

Path	Path Coefficients	t values	p values	R^2	Q^2
Performance Expectancy -> E-learner’s Intention to Use Behavior.	0.184	2.746	0.006*	0.519	0.420
Performance Expectancy -> E-learner’s Engagement Behavior	0.245	3.562	0.000***
Effort Expectancy -> E-learner’s Intention to Use Behavior.	0.263	3.594	0.000***
Effort Expectancy -> E-learner’s Engagement Behavior	0.103	1.454	0.146
Social Influence -> E-learner’s Intention to Use Behavior.	−0.035	0.556	0.579
Social Influence -> E-learner’s Engagement Behavior	0.042	0.663	0.507
Facilitating Conditions -> E-learner’s Intention to Use Behavior.	0.231	3.349	0.001*	0.468	0.281
Facilitating Conditions -> E-learner’s Engagement Behavior	0.162	2.518	0.012*
Self-efficacy -> E-learner’s Intention to Use Behavior.	0.229	2.966	0.003*
Self-efficacy -> E-learner’s Engagement Behavior.	0.152	2.325	0.020*
E-learner’s Intention to Use Behavior -> E-learner’s Engagement Behavior	0.146	1.838	0.063**

Source: Author’s computation. Notes ***p < 0.001, *p < 0.05, **p < 0.10.

Table 7. Mediating effect of E-learner’s intention to use behavior

	Performance Expectancy	Effort Expectancy	Social Influence	Facilitating Condition	Self-Efficacy	E-learner’s Intention to Use Behavior
Standardised Total Effects: E-learner’s Intention to Use Behavior. E-learner’s Engagement Behavior.	.184 0.272	.263 0.141	−0.035 0.036	0.231 0.195	0.229 0.185	0.000 0.146
Standardised Direct Effects: E-learner’s Intention to Use Behavior. E-Learner’s Engagement Behavior.	.184 0.245	.263 0.103	−0.035 0.042	0.231 0.162	0.229 0.152	0.000 0.146
Standardised Indirect Effects: E-learner’s Intention to Use Behavior. E-learner’s Engagement Behavior.	.000 0.027	.000 0.038	0.000 −0.006	0.000 0.033	0.000 0.033	0.000 0.000

Source: Author’s computation. Notes p<0.05.

Figure A1. Path Model

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

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Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412

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Deng, R., Benckendorff, P., & Gannaway, D. (2019). Learner engagement in MOOCs: Scale development and validation. British Journal of Educational Technology, 51(1), 245–262. https://doi.org/10.1111/bjet.12810

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