A holistic success model for sustainable e-learning based on the stakeholder approach: Case of Vietnamese students during the COVID-19 pandemic

Figures & data

Table 1. Proposed research factors

ISSM factors	HSMSE factors	Stakeholders
Information Quality	Course content quality	Lecturer
System Quality	Educational System Quality Technical System Quality	Institution Service Provider
Service Quality	Instructor quality Support Service Quality	Lecturer Service Provider
Use	E-learning Use	Student
Satisfaction	Satisfaction	Student
Net Benefits	Academic Performance	Student
	Perceived Usefulness	Student
	Self-learning ability	Student

Figure 1. Proposed research model.

Table 3. Discriminant value

	MSV	MaxR(H)	SRL	SSQ	ACP	IQ	ESQ	CCQ	TSQ	SAT	USE	PU
SRL	0.340	0.911	0.845
SSQ	0.459	0.881	0.495***	0.800
ACP	0.278	0.876	0.451***	0.408***	0.780
IQ	0.380	0.862	0.416***	0.441***	0.408***	0.775
ESQ	0.380	0.871	0.355***	0.543***	0.444***	0.508***	0.783
CCQ	0.485	0.881	0.583***	0.678***	0.494***	0.610***	0.605***	0.802
TSQ	0.381	0.892	0.356***	0.533***	0.444***	0.555***	0.578***	0.572***	0.850
SAT	0.456	0.854	0.521***	0.510***	0.480***	0.601***	0.580***	0.660***	0.573***	0.812
USE	0.487	0.861	0.540***	0.589***	0.527***	0.617***	0.616***	0.697***	0.617***	0.676***	0.803
PU	0.487	0.857	0.545***	0.492***	0.504***	0.613***	0.595***	0.655***	0.562***	0.653***	0.698***	0.810

Table 4. Results of hypothesis testing

Hypotheses	Construct path	Model 1 (original)	Model 2 (SAT)	Model 3 (PU)	Model 4 (USE)
H4b	IQ → PU	.228***		.222***
H1b	CCQ →PU	.225**		.234**
H2b	ESQ →PU	.234***		.223***
H5b	SSQ →PU	−.051		−.048
H3b	TSQ →PU	.146**		.151**
H6b	SRL →PU	.223***		.228***
H4a	IQ →SAT	.155**	.192***
H1a	CCQ →SAT	.194**	.231***
H2a	ESQ → SAT	.138**	.181***
H4a	SSQ →SAT	−.005	−.012
H3a	TSQ →SAT	.133**	.161**
H6a	SRL →SAT	.124**	.172***
H4c	IQ →USE	.129**			.175***
H1c	CCQ →USE	.177**			.224***
H2c	ESQ →USE	.134**			.188***
H5c	SSQ → USE	.079			.067
H3c	TSQ →USE	.155**			.188***
H7a	PU → SAT	.178**
H7b	PU →USE	.222***
H6c	SRL →USE	.109**			.166***
H8	SAT → ACP	.187**	.603***
H9	USE →ACP	.310***			.623***
H7c	PU → ACP	.207**		.590***
CMIN/df		1.916	1.923	2.013	1.967
CFI		.932	.942	.937	.940
GFI		.843	.867	.861	.865
AGFI		.812	.839	.832	.836
RMR		.029	.037	.035	.033
RMSEA		.053	.054	.056	.055
PCLOSE		.121	.151	.039	.082

Table 2. Results of the reliability and validity of measurement items

	Items	Factor loading	CR	AVE	CA	Sources
Self -regulated learning (SRL)	SRL3	.913	0.909	0.713	0.908	Al-Adwan et al. (2021)
	SRL4	.875
	SRL2	.763
	SRL1	.754
Support Service Quality (SSQ)	SSQ2	.851	0.876	0.640	0.873	Cidral et al. (2018)
	SSQ3	.765
	SSQ1	.738
	SSQ4	.571
Academic performance (ACP)	ACP4	.912	0.860	0.609	0.854	Islam (2013)
	ACP2	.807
	ACP1	.733
	ACP3	.653
Instructor Quality (IQ)	IQ4	.818	0.857	0.600	0.856	Al-Fraihat et al. (2020)
	IQ2	.767
	IQ1	.764
	IQ3	.743
Educational System Quality (ESQ)	ESQ3	.857	0.864	0.614	0.860	Seta et al. (2018)
	ESQ4	.817
	ESQ2	.751
	ESQ1	.517
Technical System Quality (TSQ)	TSQ1	.913	0.886	0.722	0.885	Seta et al. (2018)
	TSQ3	.835
	TSQ2	.751
Course Content Quality (CCQ)	CCQ3	.823	0.878	0.643	0.877	Al Mulhem and Wang (2020)
	CCQ1	.796
	CCQ2	.764
	CCQ4	.613
Satisfaction (SAT)	SAT1	.859	0.853	0.659	0.852	Yakubu and Dasuki (2018)
	SAT2	.849
	SAT3	.715
Percived Usefulness (PU)	PU4	.968	0.851	0.656	0.838	Mohammadi (2015)
	PU1	.696
	PU2	.621
The use of information system (USE)	USE1	.748	0.844	0.644	0.836	Shahzad et al. (2021)
	USE2	.729
	USE4	.677

Notes: Model fit indicators of CFA: chi-square = 1051.946; df = 549; p = 0.000; chi-square/df = 1.916 (< 3); TLI = 0.923 (>0.8); CFI = 0.933 (>0.8); RMSEA = 0.053 (<0.08) (Arbuckle, 2006; Byrne, 2001).

Al-Adwan, A. S., Albelbisi, N. A., Hujran, O., Al-Rahmi, W. M., & Alkhalifah, A. Developing a holistic success model for sustainable E-Learning: A structural equation modeling approach. (2021). Sustainability, 13(16), 9453. Article 16. https://doi.org/10.3390/su13169453

 Web of Science ®Google Scholar

Cidral, W. A., Oliveira, T., DiFelice, M., & Aparicio, M. (2018). E-learning success determinants: Brazilian empirical study. Computers & Education, 122, 273–290. https://doi.org/10.1016/j.compedu.2017.12.001

 Web of Science ®Google Scholar

Islam, A. K. M. N. (2013). Investigating e-learning system usage outcomes in the university context. Computers & Education, 69, 387–399. https://doi.org/10.1016/j.compedu.2013.07.037

 Web of Science ®Google Scholar

Al-Fraihat, D., Joy, M., Masa’deh, R., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102, 67–86. https://doi.org/10.1016/j.chb.2019.08.004

 Web of Science ®Google Scholar

Seta, H., Wati, T., Muliawati, A., & Hidayanto, A. (2018). E-Learning success model: An extention of DeLone & McLean IS’ success model. Indonesian Journal of Electrical Engineering and Informatics, 6(3). https://doi.org/10.52549/ijeei.v6i3.505

 Google Scholar

Al Mulhem, A., & Wang, S. (2020). Investigating the effects of quality factors and organizational factors on university students’ satisfaction of e-learning system quality. Cogent Education, 7(1), 1787004. https://doi.org/10.1080/2331186X.2020.1787004

 Web of Science ®Google Scholar

Yakubu, N., & Dasuki, S. (2018). Assessing eLearning Systems Success in Nigeria: An Application of the DeLone and McLean Information Systems Success Model. Journal of Information Technology Education Research, 17, 183–203. https://doi.org/10.28945/4077

 Web of Science ®Google Scholar

Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and is success model. Computers in Human Behavior, 45, 359–374. https://doi.org/10.1016/j.chb.2014.07.044

 Web of Science ®Google Scholar

Shahzad, A., Hassan, R., Aremu, A. Y., Hussain, A., & Lodhi, R. N. (2021). Effects of COVID-19 in E-learning on higher education institution students: The group comparison between male and female. Quality & Quantity, 55(3), 805–826. https://doi.org/10.1007/s11135-020-01028-z

 PubMedGoogle Scholar

Arbuckle, J. L. (2006). AMOS (Version 7.0) Computer software and manual. SPSS.

 Google Scholar

Byrne, B. M. (2001). Structural equation modeling with AMOS, EQS, and LISREL: Comparative approaches to testing for the factorial validity of a measuring instrument. International Journal of Testing, 1(1), 55–86. https://doi.org/10.1207/S15327574IJT0101_4

 Google Scholar