






































citation


AsiaCALL Online Journal Received: 18/08/2021 

ISSN 1936-9859; https://asiacall.info/acoj Revision: 26/08/2021 

Vol. 12, No. 5, 2021  Accepted: 27/08/2021 

pp. 1-16  Online: 01/09/2021 

 

CITATION | Nguyen, T. K., & Nguyen, T. H. T. (2021). Acceptance and Use of Video Conferencing for Teaching 

in Covid-19 Pandemic: An Empirical Study in Vietnam. AsiaCALL Online Journal, 12(5), pp. 1-16. 

 

Acceptance and Use of Video Conferencing for Teaching in Covid-19 Pandemic: 

An Empirical Study in Vietnam 

Nguyen Thanh Khuong1*, Nguyen Thi Hong Tham2 

 

1 Ho Chi Minh University of Law, Ho Chi Minh City, Vietnam 
2 Hoa Sen University, Ho Chi Minh City, Vietnam 

*Correspondence: Nguyen Thanh Khuong, Ho Chi Minh University of Law, Vietnam. E-mail: 

ntkhuong@hcmulaw.edu.vn 

 

Abstract 

The Coronavirus disease outbreak of 2019 (COVID-19) has fundamentally altered the nature 

of learning at all levels, from university to primary school. In Vietnam, continual learning is 

ensured through the use of video conferencing applications. Video conferencing is a teaching 

tool that is used to facilitate communication and engagement between professors and students 

during an epidemic. The study employs a unified theory of acceptance and use of technology 

(UTAUT) to ascertain the elements that influenced the adoption of video conferencing for 

online training in Vietnam during the COVID-19 pandemic. A survey of 203 instructors who 

have used video conferencing for instruction during the COVID-19 epidemic was conducted 

and evaluated using a structural equation model (SEM). The results indicate that significant 

elements influencing the use of video conferencing for teaching during Covid-19 include effort 

expectancy, habit, hedonic motivation, and behavioral intention to use, which together account 

for 59 percent of video conferencing for teaching usage (R2=0.59). 

Keywords: video conferencing, UTAUT2, teacher video conferencing adoption 

 

1. Introduction 

COVID-19 will touch over 1.6 billion learners in 190 nations worldwide by 2020, 

according to United Nations data (Brief, 2020). In Vietnam, schools at all levels must be closed 

during the COVID-19 outbreak in accordance with Directive No. 16/CT-TTg on the 

implementation of social distancing. Dispatch No. 1247/BGDDT-GDCTHSSV on enhancing 

the safety of preschool children, students during their study over the Internet on March 14, 2020 

to implement solutions to ensure the continuous learning of students. On March 23, 2020, at 

the college and university level, Dispatch No. 988/BGDDT-GDDH was issued regarding 

ensuring the quality of distant learning during the covid-19 outbreak. Next, on March 25, 2020, 

the Ministry of Education and Training issued directives, including Dispatch No. 

1061/BGDDT-GDTrH on Internet and Television Instruction for General Education Institutions 

mailto:ntkhuong@hcmulaw.edu.vn


https://asiacall.info/acoj Nguyen, T. K., & Nguyen, T. H. T. Vol. 12; No. 5; 2021 

2 
 

and Institutions During the Period of Student Leave from School Due to COVID-19 in the 2019-

2020 school year.  

Adapting teaching and learning practices in the midst of the COVID-19 pandemic is a 

significant problem for Vietnam's education sector. However, this is an excellent opportunity to 

further enhance online learning approaches. One extensively used technique is to educate using 

conference technologies in conjunction with learning management systems (LMS).  In Vietnam, 

video conferencing tools such as Zoom, Google Meet, Microsoft Teams, and Skype are widely 

used. This also happened in Malaysia, Thailand and Iran (Hashim, 2006; Rahimi & Bigdeli, 

2014). Throughout covid-19, this was a frequently employed method of instruction. According 

to Townsend, A.M., Demarie, S.M., & Hendrickson, A. R. (2001) in turns out that the student 

achieves good results when using the conference system (Townsend, Demarie, & Hendrickson, 

2001). Another study by MacLaughlin, Supemaw & howard (2004) found that learning through 

conferences gives the same academic performance as traditional learning (MacLaughlin, 

Supemaw, & Howard, 2004). Additionally, numerous research has been conducted on students' 

intended behavior when it comes to video conferencing in order to ascertain the elements that 

influenced their use of this instrument during the Covid-19 pandemic ((Bui, Luong, Nguyen, 

Nguyen, & Ngo, 2020); (Ngo, Nguyen, & Tran, 2020); (Pham & Ho, 2020); (Fatani, 2020); 

(Hiroyuki, O., 2021); (Nguyen, T. N. M., & Nguyen, P. H., 2021); Nguyen, H. U. N., & Duong, 

L. N. T. (2021)). However, studies on the intended behavior and usage of video conferencing 

for teaching of instructors are of little notice at various school levels.  

In this study, the author focuses on the Unified Theory of Acceptance and Use of Technology 

(UTAUT) ((Venkatesh, Morris, Davis, & Davis, 2003); (Venkatesh, Thong, & Xu, 2012)) to 

discover the elements that impact the intention and usage behavior of teachers in employing the 

video conferencing system to educate during the COVID-19 pandemic. The subjects and scope 

of inquiry are instructors at different levels of education who have utilized video conferencing 

technology to educate during the COVID-19 pandemic in Vietnam.  

2. Literature review 

2.1 Video Conferencing 

Video conferences, according to the Oxford Dictionary, are "meetings in which persons from 

diverse locations communicate via voice and video." According to the United Nations 

Development Programme, free video conferencing tools such as Zoom, Google Meet, 

Microsoft Teams, and Skype were heavily used during the COVID-19 crisis. By utilizing video 

conferencing, businesses may increase their productivity, optimize and expedite decision-

making, and reduce customer and employee travel costs associated with communication, 

exchange, and meeting procedures. Video conferencing in education enables continuous 

instruction throughout the COVID-19 cycle and lays the groundwork for the creation of online 

teaching activities in remote learning situations ((Fatani, 2020); (Sahi, Mishra, & Singh, 2020)).  

 



ACOJ- ISSN 1936-9859 AsiaCALL Online Journal                                               Vol. 12; No. 5; 2021 

3 
 

2.2 Research model 

Information systems research has extensively examined new technology adoption. Trend-

conscious conduct was identified using the Psychosocial Perspective on Theory of Reasoned 

Action (TRA) ((Azjen, 1980); (Fishbein & Ajzen, 1977)). Ajzen created the Theory of Planned 

Behavior (TPB) by adding a component of perceived behavioral control to the TRA theory 

((Ajzen, 1985);(Ajzen, 2002)). TPB urges the researcher to look at how consumers' social 

sensitivity affects their decision to utilize an online system (Crespo & del Bosque, 2008). In 

order to explain human behavior linked to information technology adoption, the Technology 

Acceptance Model (TAM) builds on the TRA's theoretical underpinnings ((Davis, 1989); 

(Davis, 1993)). The IDT detailed how people assimilate technical advancements (Rogers, 

1995). Venkatesh et al. created the UTAUT to explain information system users' intentions and 

behavior. A combination of the TPB and TAM models, IDT, the Motivation Model (MM) 

(Davis, Bagozzi, & Warshaw, 1992), the Model of Personal Computer Use (MPCU) 

(Thompson, Higgins, & Howell, 1991), and Social Cognitive Theory were used to construct 

UTAUT (SCT) (Compeau & Higgins, 1995). Performance expectancy, effort expectancy, 

social influence, and facilitating condition were used to produce UTAUT. Venkatesh et al. later 

introduced UTAUT2, which adds hedonic motivation, price value, and habit to the original 

UTAUT concepts. Then UTAUT2 includes demographics like age, gender, and experience. 

2.3 Hypotheses 

Performance Expectancy (PE) is described as an individual's belief that by 

implementing a specific system, they will be able to achieve competitive advantages at work 

(Venkatesh et al., 2003).  Perceived usefulness in TAM, extrinsic motivation in MM, job fit in 

MPCU, and result expectation in SCT are the five constructs that make up various models of 

performance expectancy. The teacher believed that video conferencing would boost their 

performance and that individual students would be happier with it. As a result, the subsequent 

hypothesis has been proposed  

Hypothesis 1: PE has a beneficial effect on the intention to use video conferencing 

(VCI). 

Effort Expectancy (EE) is described as the ease with which information systems can be 

combined (Venkatesh et al., 2003).  According to Amoaka (2004), the effort is anticipated to 

determine the end-intention users to use the information system (Amoako-Gyampah & Salam, 

2004). Teachers' decisions on whether or not to use video conferencing are influenced by the 

system's expected effort. The following is the H2 hypothesis:  

Hypothesis 2: EE has a beneficial effect on a person's intention to engage in video 

conferencing (VCI). 

The degree to which an individual is aware that other influential individuals believe the 

new method is better for work is referred to as social influence (SI) (Venkatesh et al., 2003).  

The subject norm in TAM, social element in MPCU, and image in IDT are all examples of 



https://asiacall.info/acoj Nguyen, T. K., & Nguyen, T. H. T. Vol. 12; No. 5; 2021 

4 
 

social influence as a direct predictor of behavioral intention. Teachers would be influenced by 

the social aspects of video conferencing in deciding whether or not to use it. As a result, it 

proposes:  

Hypothesis 3: SI has a beneficial effect on the intention to use video conferencing (VCI). 

The facilitating condition (FC) is that the degree to which an individual believes that a 

company and technical infrastructure exist to create the system easier to use (Venkatesh et al., 

2003). Perceived behavioral control, facilitating condition, and compatibility are the principles 

indicated by three separate conceptions in TAM, MPCU, and IDT. The technological features 

of a video conferencing facility would have an impact on the teacher's decision to use it or not. 

As a result, the following proposal was made:  

Hypothesis 4: FC has a beneficial effect on the intention to use video conferencing 

(VCI). 

Hedonic Motivation (HM) is described as joy or enjoyment experienced as a result of 

utilizing the system, as well as a significant contribution to the desire to use the new system 

(Brown & Venkatesh, 2005).  Research on information systems has found that hedonic 

motivation is directly connected to the adoption and use of technology. The teacher's decision 

to employ video conferencing would be influenced by its hedonic motive. The H5 theory is as 

follows:  

Hypothesis 5: HM has a beneficial effect on the intention to use video conferencing 

(VCI). 

Habit (HB) is described as the degree to which humans tend to perform behaviors 

automatically as a result of learning, and habit is sometimes confused with automaticity 

(Limayem, Hirt, & Cheung, 2007). Venkatesh (2012) claims that HB has a direct or indirect 

influence on behavioral intention (Venkatesh et al., 2012). The HB of a video conferencing 

system will influence the teacher's decision to use the system in the future. As a result, we have 

hypothesis H6 and H7:  

Hypothesis 6: HB has a beneficial effect on the intention to use video conferencing 

(VCI). 

Hypothesis 7: HB has a beneficial effect on the use of video conferencing (VCU). 

Price Value (PV) is defined as a consumer cognitive trade-off between the perceived 

benefits of applications and the financial cost of using them. (Dodds, Monroe, & Grewal, 1991). 

Venkatesh (2012) defines PV as users' perceptions of trade-offs between benefits and costs 

(Venkatesh et al., 2012). The teacher's decision to employ video conferencing would be 

influenced by the PV. As a result, we've got the H8 hypothesis:  

Hypothesis 8: PV has a beneficial effect on the intention to use video conferencing 

(VCI). 

Video conferencing intention (VCI) is defined as an individual's intention to perform a 



ACOJ- ISSN 1936-9859 AsiaCALL Online Journal                                               Vol. 12; No. 5; 2021 

5 
 

specific action or as a subjective probability of completing the behavior, as well as the cause of 

a specific act of usage (Mun, Jackson, Park, & Probst, 2006). The goal of video conferencing 

is to get teachers to use it. As a result, we have the hypothesis H9:  

Hypothesis 9: VCI has a beneficial effect on the use of video conferencing (VCU). 

The analysis included demographic data (DE), such as age, gender, school level, and 

information technology communication (ICT) experience, as suggested by UTAUT2. As a 

result, it speculates on the following: 

Hypothesis 10: DE has an effect on both independent and dependent elements. 

 

3. Methods 

3.1 Research method 

The research was conducted in two stages: (1) preliminary qualitative research and (2) formal 

quantitative research. To create a draft scale, the author used the theory of unifying and 

accepting technology use (UTAUT2) and the actual situation of using conferences to teach 

during the COVID-19 outbreak in Vietnam. Following that, the author discussed the draft scale 

with teachers who used the video conferencing system to teach during the COVID-19 epidemic 

in order to calibrate it and provide a preliminary experimental scale for research. The scale from 

the preliminary study was used in the formal study after it was corrected. The observed variables 

in the official study were quantified using a 5-point Likert scale (at the lowest level 1 is strongly 

disagree and the highest level is 5 strongly agree). The survey will be distributed via social 

media platforms, forums, and teacher communities in Vietnam. A total of 215 data samples were 

collected, with 203 samples (12 invalid samples) of 29 observed variables being usable. SPSS 

software (Cronbach's Alpha and EFA) and AMOS were used to clean and analyze the collected 

data (CFA and SEM). The formal study's analysis included exploratory factor analysis (EFA); 

reliability analysis (Cronbach's Alpha); confirmatory factor analysis (CFA); and analysis of the 

structural equation model (SEM) to test the model and the research model's hypotheses. 

3.2 Data collection and Descriptive statistics 

The descriptive data are used to determine indicators for teachers who used video conferencing 

to train students during the Covid-19 epidemic. Video conferencing app: 54% of respondents 

utilized ZOOM, with 32% using MS Team, the rest were Google Meet and other applications, 

11% and 3% respectively. Gender distribution is unequal, with roughly 70% female and 30% 

male. Age: respondents aged 31-40 and 41-50 account for 50.2 percent and 29.6 percent, 

respectively; the remaining respondents are aged 22-30 and above 50. ICT Experience: Nearly 

85% of respondents reported having more than ten years of computer experience; the remainder 

reported having fewer than five years. School level: more over half of the respondents are now 

in high school, and around one-quarter of them are currently in secondary school. Another one-

quarter of the respondents are currently at university, and the remaining nine percent are in 

primary school. 



https://asiacall.info/acoj Nguyen, T. K., & Nguyen, T. H. T. Vol. 12; No. 5; 2021 

6 
 

4. Results 

4.1 Exploratory Factor Analysis (EFA) 

PV1 and PV3 were excluded from the first exploratory factor analysis (EFA) because 

their factor loadings were less than 0.50. After eliminating PV1 and PV3, the price value (PV) 

component was left with just one variable to evaluate. As a result, the PV component of the 

exploratory factor analysis was omitted (EFA). The second EFA then extracted eight items from 

the 26 indicators. As predicted by the theoretical model, the variables coalesce into eight 

groupings factors in the rotational component matrix, including effort expectancy (EE), 

facilitating condition (FC), hedonic motivation (HM), habit (HB), video conferencing intention 

(VCI), and video conferencing usage (VCU). According to Table 1, the EFA factor loadings of 

all indicators vary between 0.641 and 0.904.  

Table 1. Structure of Components and Scale for Video Conferencing Usage 

Observed variables 

Factor 

Loading 

C
ro

n
b

a
ch

 

A
lp

h
a
 

A
v
er

a
g
e 

V
a
ri

a
n

ce
 

E
x
tr

a
ct

ed
 

EFA CFA CR AVE 

Performance Expectancy (PE) 0.763 0.531 

1 
I am always available to use the video conferencing 

system to teach online.  
PE1 0.70 0.82 

  2 
I'm really interested in learning how to use and 

operate the video conference system.  
PE2 0.64 0.80 

3 
I'll be using a video conferencing system that fully 

combines online teaching support tools.  
PE3 0.84 0.79 

Effort Expectancy (EE)   0.892 0.679 

4 
I find it simple to teach online using the video 

conferencing method.  
EE1 0.90 0.87 

  5 
I know how to use the video conferencing 

technology.  
EE2 0.78 0.84 

6 
The video conferencing system gives me complete 

instructions to help me with the teaching process.  
EE3 0.71 0.83 



ACOJ- ISSN 1936-9859 AsiaCALL Online Journal                                               Vol. 12; No. 5; 2021 

7 
 

Observed variables 

Factor 

Loading 

C
ro

n
b

a
ch

 

A
lp

h
a
 

A
v
er

a
g
e 

V
a
ri

a
n

ce
 

E
x
tr

a
ct

ed
 

EFA CFA CR AVE 

7 
It's simple to learn how to use the video 

conferencing technology.  
EE4 0.87 0.75 

Social Influence (SI)   0.827 0.627 

8 

My colleagues believe that using the video 

conferencing system for online education will be 

more effective.  

SI1 0.75 0.88 

  9 
Other schools also use the video conferencing 

system for online education.  
SI2 0.89 0.76 

10 
My supervisor believes that I should use video 

conferencing to teach online to boost engagement.  
SI3 0.73 0.73 

Hedonic Motivation (HM)   0.778 0.544 

11 

I will have access to all necessary information for 

online training via the video conferencing 

technology.  

HM1 0.67 0.85 

  12 

Utilizing a video conferencing solution enables me 

to expand my capacity for online interaction with 

students.  

HM2 0.70 0.83 

13 

When I'm teaching online via video conferencing, 

it's simple for me to enlist the assistance of another 

instructor.  

HM3 0.76 0.83 

Habit (HB)   0.847 0.650 

14 
I have made it a practice to use the video 

conferencing system for online instruction.  
HB1 0.77 0.83 

  
15 

I am confident in using the video conferencing 

system for online education.  
HB2 0.79 0.85 

16 I can't quit using video conferencing to teach online.  HB3 0.77 0.77 



https://asiacall.info/acoj Nguyen, T. K., & Nguyen, T. H. T. Vol. 12; No. 5; 2021 

8 
 

Observed variables 

Factor 

Loading 

C
ro

n
b

a
ch

 

A
lp

h
a
 

A
v
er

a
g
e 

V
a
ri

a
n

ce
 

E
x
tr

a
ct

ed
 

EFA CFA CR AVE 

Facilitating Condition (FC)   0.841 0.571 

17 
Have ideal settings for using the video conferencing 

equipment to teach online  
FC1 0.69 0.79 

  

18 

Provide good settings for obtaining video tutorials 

for learning and for using the video conferencing 

system for online teaching.  

FC2 0.81 0.73 

19 
Find instructions on how to use the video 

conferencing technology to teach online. 
FC3 0.72 0.76 

20 
I quickly learned how to use the video conferencing 

equipment to support my teaching.  
FC4 0.78 0.75 

Price Value (PV)       

21 
Online teaching with video conferencing provides 

more information and comfort than it costs.  
PV1 

Eliminated 

  

22 
Video conferencing increases engagement and 

ensures teaching.  
PV2   

23 
Using video conferencing to teach online will save 

you time and effort.  
PV3   

Video Conferencing Intention (VCI)   0.876 0.703 

24 
I intend to use the video conferencing system for 

online instruction.  
VCI1 0.72 0.82 

  25 
I always consider employing the video conferencing 

technology.  
VCI2 0.88 0.75 

26 
I intend to employ video conferencing for online 

instruction more regularly.  
VCI3 0.82 0.60 

Video Conferencing Usage (VCU)   0.853 0.668 



ACOJ- ISSN 1936-9859 AsiaCALL Online Journal                                               Vol. 12; No. 5; 2021 

9 
 

Observed variables 

Factor 

Loading 

C
ro

n
b

a
ch

 

A
lp

h
a
 

A
v
er

a
g
e 

V
a
ri

a
n

ce
 

E
x
tr

a
ct

ed
 

EFA CFA CR AVE 

27 
I always teach online using the video conferencing 

method.  
VCU1 0.73 0.80 

  28 
I use the video conferencing equipment to improve 

my relationships with students.  
VCU2 0.77 0.70 

29 
I had numerous unique experiences using the video 

conferencing system to teach online.  
VCU3 0.90 0.71 

 

4.2 Confirmatory Factor Analysis (CFA) 

Confirmatory Factor Analysis (CFA) validates the model's fit to the data. According to Table 2, 

the Chi-square (χ2)/DF value is 1.197; the GFI value is 0.895; the TLI value is 0.975; the CFI 

value is 0.979; and the RMSEA value is 0.031. According to Table 1, all observed variables had 

high standardized CFA factor loadings, ranging between 0.604 and 0.883. According to Fornell 

& Larcker (1981), an average variance extracted (AVE) of 0.53 to 0.70 (more than 0.50) 

indicates that the measures have a high degree of convergent validity (Fornell & Larcker, 1981). 

Additionally, because all AVEs are greater than the associated squared correlation coefficients, 

the measures acquire discriminant validity (r2). Additionally, the Cronbach alpha values for all 

variables in official measurements are adequate, indicating that they are valid measures (>0.70).  

Table 2. CFA Indicator 

Indicator Level of acceptance Results Reference 

RMSEA RMSEA <0.08 0.031 
(Hair, 

2009) 

GFI 

If greater than or equal to 0.9, the model fits 

satisfactorily. 

Between 0.8 and 0.9 is an acceptable level of model 

fit. 

0.895 

(Seyal, 

Rahman, & 

Rahim, 

2002); (Hu 

& Bentler, 

1999) 

CFI 
If greater than or equal to 0.9, the model fits 

satisfactorily. 
0.979 

(Hair, 

2009) 



https://asiacall.info/acoj Nguyen, T. K., & Nguyen, T. H. T. Vol. 12; No. 5; 2021 

10 
 

TLI 
If greater than or equal to 0.9, the model fits 

satisfactorily. 
0.975 

(Hair, 

2009) 

CMIN/Df 

(χ2/dF) 
1.0 < Cmin/df <3.0 1.197 

(Hair, 

2009) 

 

4.3 Structural Equation Model (SEM) Analysis  

The maximum likelihood (ML) estimation of the structural equation model (SEM) yields 

theoretical scale indices of χ2/dF=1.786; GFI=0.825; TLI=0.900; CFI=0.910; and 

RMSEA=0.062. In this case, the model is a good match for the market data. Table 3 shows the 

SEM in the estimates, which reveal that EE, HM, and HB have positive effects on VCI with 

=0.157 (p=0.003), 0.222 (p=0.003), and 0.294 (p=0,001), respectively, supporting H2, H5, and 

H6. Neither the path from PE, SI, and FC to VCI, nor the line from HB to VCU are, however, 

dismissed. And the findings back up H9 by demonstrating that VCI has an effect on VCU with 

=0.592 (p=0.001).  

Table 3. Analysis results of relationship 

Hypothesis Relationships Estimate SE CR P - value result 

H1 VCI  PE 0.056 0.079 0.706 0.480 Rejected 

H2 VCI  EE 0.157 0.054 2.943 0.003 Supported 

H3 VCI  SI 0.071 0.056 1.263 0.206 Rejected 

H4 VCI  FC 0.122 0.070 1.735 0.083 Rejected 

H5 VCI  HM 0.222 0.074 2.998 0.003 Supported 

H6 VCI  HB 0.294 0.068 4.326 *** Supported 

H7 VCU  HB 0.110 0.074 1.486 0.137 Rejected 

H9 VCU  VCI 0.592 0.094 6.275 *** Supported 

The ANOVA test is used to examine if any variations in the connection between PE, EE, SI, 

FC, PV, HM, VCI, and VCU are due to demographic factors such as age, gender, school level 

or ICT experience. The data suggest that relationships between independent and dependent 

variables are unaffected by age, gender, school level or ICT experience and are statistically 

significant at p=0.05. As a result, H10 is deemed invalid. This research generally supports four 

out of every ten hypotheses (see Figure 1). 



ACOJ- ISSN 1936-9859 AsiaCALL Online Journal                                               Vol. 12; No. 5; 2021 

11 
 

 

Figure 1. The acceptance and use of video conferencing model 

  4.4. Discussions 

In summary, four of the study's ten hypotheses are accepted, and structural equation model 

(SEM) analysis indicates that independent and intermediate components may account for 

approximately 59% (R2=0.59) of the variation. According to the study, 59% of video 

conferencing usage behavior in teaching during the COVID-19 outbreak was driven by 

variables that directly affect video conferencing usage. In terms of EE, HM, and HB, factors 

are those that have a direct effect on the teacher's intention to use the video conferencing system, 

as well as on the teacher's action. 

This study examined the factors affecting the acceptance and use of video conferencing 

for teaching in Vietnam during the Covid-19 pandemic, using the unified theory of acceptance 

and use of technology (UTAUT2) framework. Our study discovered that effort expectancy had 

a significant effect on teachers' behavioral intention to use video conferencing (EE). Additional 

studies (Tarhini, Al-Busaidi, Mohammed, & Maqableh, 2017) corroborate this finding. A study 

found that EE had a big effect on the employment of animation and storytelling during this 

regard (Suki & Suki, 2017). As a result, the teaching-learning process appears to be simplified 

during Covid-19 pandemic when using video conferencing. Hedonic motivation (HM) had a 

major positive effect on the behavioral intention of teachers to use video conferencing. HM was 

connected to a mobile learning adoption intention (Moorthy, Yee, T'ing, & Kumaran, 2019). 

When it involves video conferencing, enjoyable learning experiences are critical. A user-

friendly environment and electronic content significantly contribute to the creation of 

pleasurable learning experiences (El-Masri & Tarhini, 2017). As a result, educational designers 

should keep these characteristics in mind. The findings of this study indicated that habit (HB) 



https://asiacall.info/acoj Nguyen, T. K., & Nguyen, T. H. T. Vol. 12; No. 5; 2021 

12 
 

had a positive effect on teachers' intentions to use video conferencing systems. in keeping with 

Venkatesh et al. (2012), routine use of a technology incorporates a significant effect on its 

adoption (Venkatesh et al., 2012). In general, our study found that teachers' behavioral 

intentions to use video conferencing had a big effect on their actual video conferencing use. 

Our findings corroborated prior research ((Hoque & Sorwar, 2017); (Suki & Suki, 2017); 

(Ravangard, Kazemi, Abbasali, Sharifian, & Monem, 2017)). The intention to use video 

conferencing was predictive of actual use. the particular use of video conferencing was also 

contingent teachers' behavioral intent to try and do so. In summary, our findings indicate that 

teachers are willing to use video conferencing to boost the standard of their teaching 

experiences. We believe that teachers come from a range of economic, social, and cultural 

backgrounds in developed and developing countries. These various circumstances may have a 

major impact on teachers' intentions to adopt a brand new instructional system. Video 

conferencing may be a new approach within the Covid-19 pandemic and better education in 

Vietnam as a developing country. As a result, additional research on video conferencing system 

adoption is strongly recommended. 

 

5. Conclusion 

The purpose of our work - to examine the instructor's acceptance and use of Video Conferencing 

technology using the UTAUT2 methodology - has been accomplished. The findings indicate 

that variable measures ensure reliability. Both EFA and CFA generate high factor loadings for 

the variables, and their measures exhibit discriminant validity. Additionally, the SEM analysis 

demonstrates that factors such as effort expectancy, hedonic motivation, and habit all influence 

and affect video conferencing adoption; conversely, video conferencing adoption has an effect 

on video conferencing usage. 

According to the study's conclusions, teachers must make substantial efforts to continue 

continuous education throughout the COVID-19 epidemic by utilizing video conferencing 

technology. Obtaining support from communities and professionals in the same field has also 

resulted in the development of various values beneficial to instructors, which is one of the major 

reasons for utilizing video conferencing for teaching. By utilizing video conferencing to educate 

during the COVID-19 epidemic, instructors were able to develop favorable behaviors that 

enabled them to use information technology to enhance education. The intention to use 

conference sessions is most strongly influenced by habitual variables. Thus, teachers should 

use video conferencing on a regular and consistent basis to build habits when adopting remote 

learning.  

For online training, the study's findings shed light on the most widely used methods for ensuring 

continuous learning between teachers and learners who may interact and converse in real time. 

As a result, the use of conference meeting apps is required for distance training programs and 

during the covid-19 epidemic. However, to ensure efficient usage of this application, teachers 

require training and direction on how to use it effectively during the teaching process. 



ACOJ- ISSN 1936-9859 AsiaCALL Online Journal                                               Vol. 12; No. 5; 2021 

13 
 

Additionally, teachers play a critical role in the session's effectiveness and the happiness of 

learners while using conference meeting programs for teaching ((Selim, 2007); (Shee & Wang, 

2008); (Alqahtani & Rajkhan, 2020)). The teacher will operate not just as a teacher, but also as 

a transmitter of knowledge, assisting learners in their learning process through the use of 

conference meeting software. 

For managers and educational policymakers, research indicates that using video conferencing 

technology for distance training and online teaching is a viable option. Developing online and 

distance education programs requires the use of video conferencing tools to facilitate 

communication and instruction between teachers and students. As a result, managers must 

develop an appropriate strategy for utilizing, exploiting, and integrating conference applications 

into learning content management systems appropriate for each specific level of study in order 

to ensure the quality of teaching and learning outcomes for learners. Additionally, as new 

technologies are implemented in education, managers must develop training and teacher 

development plans in order to develop a core human resource for the future development of 

online training and distance learning. 

In a follow-up study, the authors will explore the combined effects of the elements as well as 

expand the scope and subject of the research, modify the scales, add more variables to the 

research model, use random sampling, and make recommendations that will help educators, e-

learners, educational organizations, and service providers implement video conferencing and e-

learning strategies. 

 

References 

Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In Action control 

(pp. 11-39): Springer. DOI: 10.1007/978-3-642-69746-3_2 

Ajzen, I. (2002). Perceived behavioral control, self‐efficacy, locus of control, and the theory 

of planned behavior 1. Journal of applied social psychology, 32(4), 665-683. 

https://doi.org/10.1111/j.1559-1816.2002.tb00236.x 

Alqahtani, A. Y., & Rajkhan, A. A. (2020). E-learning critical success factors during the 

covid-19 pandemic: A comprehensive analysis of e-learning managerial perspectives. 

Education sciences, 10(9), 216. https://doi.org/10.3390/educsci10090216 

Amoako-Gyampah, K., & Salam, A. F. (2004). An extension of the technology acceptance 

model in an ERP implementation environment. Information & management, 41(6), 

731-745. DOI:10.1016/j.im.2003.08.010. 

Azjen, I. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs.  

Brief, P. (2020). Education during COVID-19 and Beyond. United Nations(Accessed 2020, at 

https://www. un. org/development/desa/dspd/2020/04/socia l-impact-of-covid-19.).  

https://www/


https://asiacall.info/acoj Nguyen, T. K., & Nguyen, T. H. T. Vol. 12; No. 5; 2021 

14 
 

Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A 

baseline model test and extension incorporating household life cycle. MIS quarterly, 

399-426. https://doi.org/10.2307/25148690 

Bui, T.-H., Luong, D.-H., Nguyen, X.-A., Nguyen, H.-L., & Ngo, T.-T. (2020). Impact of 

female students’ perceptions on behavioral intention to use video conferencing tools in 

COVID-19: Data of Vietnam. Data in Brief, 32, 106142. doi: 

10.1016/j.dib.2020.106142 

Compeau, D. R., & Higgins, C. A. (1995). Application of social cognitive theory to training 

for computer skills. Information systems research, 6(2), 118-143. 

https://doi.org/10.1287/isre.6.2.118 

Crespo, A. H., & del Bosque, I. R. (2008). The effect of innovativeness on the adoption of 

B2C e-commerce: A model based on the Theory of Planned Behaviour. Computers in 

Human Behavior, 24(6), 2830-2847. doi:10.1016/j.chb.2008.04.008 

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of 

information technology. MIS quarterly, 319-340. https://doi.org/10.2307/249008 

Davis, F. D. (1993). User acceptance of information technology: system characteristics, user 

perceptions and behavioral impacts. International journal of man-machine studies, 

38(3), 475-487. https://doi.org/10.1006/imms.1993.1022 

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to 

use computers in the workplace 1. Journal of applied social psychology, 22(14), 1111-

1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x 

Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of price, brand, and store 

information on buyers’ product evaluations. Journal of marketing research, 28(3), 

307-319. https://doi.org/10.2307/3172866 

Fatani, T. H. (2020). Student satisfaction with videoconferencing teaching quality during the 

COVID-19 pandemic. BMC Medical Education, 20(1), 1-8. 

https://doi.org/10.1186/s12909-020-02310-2 

Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to 

theory and research. Philosophy and Rhetoric, 10(2), 177-188. 

Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables 

and measurement error: Algebra and statistics. In: Sage Publications Sage CA: Los 

Angeles, CA. http://dx.doi.org/10.2307/3150980 

Hair, Joseph F. (2009). Multivariate Data Analysis: A Global Perspective. 7th ed. Upper 

Saddle River: Prentice Hall. 

Hashim, Z. (2006). Open and distance learning: The effectiveness of online discussion forums 

in promoting the use of English for communication. AsiaCALL Online Journal, 1 (1), 

22, 33. 



ACOJ- ISSN 1936-9859 AsiaCALL Online Journal                                               Vol. 12; No. 5; 2021 

15 
 

Hiroyuki, O. (2021). The Integration of 21st Century Skill and Virtual Learning with COVID-

19. AsiaCALL Online Journal, 12(3), 22-27. Retrieved from 

https://asiacall.info/acoj/index.php/journal/article/view/30 

Hu, L., & Bentler, P. M. (1999). A multidisciplinary journal cutoff criteria for fit indexes in 

covariance structure analysis. Structural Equation Modeling, 6(1), 1-55. 

https://doi.org/10.1080/10705519909540118 

Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of 

intention: The case of information systems continuance. MIS quarterly, 705-737.  

MacLaughlin, E. J., Supemaw, R. B., & Howard, K. A. (2004). Impact of distance learning 

using videoconferencing technology on student performance. American Journal of 

Pharmaceutical Education, 68(3).  

Mun, Y. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information 

technology acceptance by individual professionals: Toward an integrative view. 

Information & management, 43(3), 350-363. DOI: 10.1016/j.im.2005.08.006 

Ngo, T. T., Nguyen, T. T. T., & Tran, T.-G. (2020). Influence of Learning by Using Video 

Conferencing Tools on Perceptions and Attitude of Vietnamese Female Students in 

COVID-19 Pandemic. Available at SSRN 3697029.  

Nguyen, H. U. N., & Duong, L. N. T. (2021). The Challenges of E-learning Through 

Microsoft Teams for EFL Students at Van Lang University in COVID-19. AsiaCALL 

Online Journal, 12(4), 18-29. Retrieved from 

https://asiacall.info/acoj/index.php/journal/article/view/60 

Nguyen, T. N. M., Tra, V. D., & Nguyen, P. H. (2021). Difficulties and some suggested 

solutions in studying online of the students in Van Lang University during the Covid-

19 pandemic. AsiaCALL Online Journal, 12(4), 9-17. Retrieved from 

https://asiacall.info/acoj/index.php/journal/article/view/58 

Pham, H.-H., & Ho, T.-T.-H. (2020). Toward a ‘new normal’with e-learning in Vietnamese 

higher education during the post COVID-19 pandemic. Higher Education Research & 

Development, 39(7), 1327-1331. https://doi.org/10.1080/07294360.2020.1823945 

Rahimi, A., & Bigdeli, R. A. (2014). ICT and EFL Students’ self-regulation mastery: 

Educational meat or poison. AsiaCALL Online Journal (ISSN 1936-9859). 

Rogers, E. (1995). Diffusion of Innovations (4th Eds.) ACM The Free Press (Sept. 2001). 

New York, 15-23.  

Sahi, P. K., Mishra, D., & Singh, T. (2020). Medical education amid the COVID-19 

pandemic. Indian pediatrics, 57(7), 652-657. DOI: 10.1007/s13312-020-1894-7 

Selim, H. M. (2007). Critical success factors for e-learning acceptance: Confirmatory factor 

models. Computers & Education, 49(2), 396-413. 

DOI:10.1016/j.compedu.2005.09.004 



https://asiacall.info/acoj Nguyen, T. K., & Nguyen, T. H. T. Vol. 12; No. 5; 2021 

16 
 

Seyal, A. H., Rahman, M. N. A., & Rahim, M. M. (2002). Determinants of academic use of 

the Internet: a structural equation model. Behaviour & Information Technology, 21(1), 

71-86. https://doi.org/10.1080/01449290210123354 

Shee, D. Y., & Wang, Y.-S. (2008). Multi-criteria evaluation of the web-based e-learning 

system: A methodology based on learner satisfaction and its applications. Computers 

& Education, 50(3), 894-905. DOI:10.1016/j.compedu.2006.09.005 

Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a 

conceptual model of utilization. MIS quarterly, 125-143. 

https://doi.org/10.2307/249443 

Townsend, A. M., Demarie, S. M., & Hendrickson, A. R. (2001). Desktop video conferencing 

in virtual workgroups: anticipation, system evaluation and performance. Information 

Systems Journal, 11(3), 213-227. https://doi.org/10.1111/j.1365-2575.2001.00103.x 

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of 

information technology: Toward a unified view. MIS quarterly, 425-478. 

https://doi.org/10.2307/30036540 

Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information 

technology: extending the unified theory of acceptance and use of technology. MIS 

quarterly, 157-178. https://doi.org/10.2307/41410412 

 

 

Biodata 

Nguyen Thanh Khuong is a lecturer of Information Technology at Ho Chi Minh University of 

Law, Vietnam. His academic interest areas are open and distance education, online learning, 

e-learning, information communication technology in education, information system in 

business and e-commerce.  

 

Nguyen Thi Hong Tham is a teacher at Nguyen Thai Binh secondary school and is currently 

studying for a Master of Arts in Linguistics at Hoa Sen University, Vietnam. She has over 10 

years of experience in teaching English for students. Her academic interest areas are about 

education and methodology in learning a foreign language. 

 


