Continuity of Academic Work through e-Learning during the

COVID-19 Lockdown: The Case of Ghana

Mateko Okantey

, Nana Kofi Annan

, Dennis Soku

, Samuel Mensah

, Isaac Korboe

and Kojo Akuokoh Frimpong

Wisconsin International University College, No. 23 Akoto Bamfo Street North Legon, Accra, Ghana

University of Ghana, Legon, Accra, Ghana

Keywords: COVID-19, e-Learning, Information and Communication Technology, Tertiary Institution, Online, Virtual,

Teaching, Learning.

Abstract: The outbreak of the Novel Corona Virus (COVID-19) pandemic took the whole world by surprise. It virtually

brought everything to a standstill due to the lockdown. Though every sector of the global economy had its

fair share of the dire consequences of the COVID-19, one of the most affected area included education.

Educational institutions shut down to avoid the spread of the virus. The situation led to the compulsory

adoption of E-Learning or online learning as a step in the gap to enable the continuation of academic work.

The objective of the study was to categorize the technologies used by universities during the COVID-19

pandemic; determine the extent of use of these technologies during the lockdown; examine the effectiveness

of these technologies for teaching and learning. The survey approach of research design was used to observe

education institutions adoption of e-learning technology during the COVID-19 and interpreted the findings

based on existing theory. The result shows that the strongest relationship amongst the factors was perceived

usefulness and attitude towards use.

1 INTRODUCTION

Corona Virus (also called COVID-19) was first seen

in the Wuhan region of China in the early part of

December 2019 and since then it has spread across

many countries, including Ghana (Shereen et al.,

2020). The presence of this virus in nations has

brought in its wake a number of stagnations,

including restriction of movements of people (since it

is believed to be transferred from human to human),

the collapse of businesses (Ozili & Arun, 2020) and

the disruption of academic calendars of most

universities especially in Ghana just to mention a few

(Haruna, 2020).

The Government of Ghana, in an attempt to check

and control further spread of the virus closed all

educational institutions in the country. In the interim,

recommended E-Learning for the Tertiary

https://orcid.org/0000-0001-5737-8491

https://orcid.org/0000-0001-5881-9148

https://orcid.org/0000-0002-0216-9841

https://orcid.org/0000-0001-6476-8252

https://orcid.org/0000-0002-5351-2718

https://orcid.org/0000-0002-1372-2796

Institutions. In this, each University in the country,

either public or private, was expected to choose its

own suitable online programme, train its teaching

staff and students and then the teaching staff would

use the virtual platform to train its learners till the end

of the semester or till the virus was gotten rid of in the

country. This shift completely changed the academic

environment.

Online education is one of the methods of

education in today’s world of technology. It has, with

time, become an important part of school education.

Until now, E-learning has been used by most

universities as a supplement to the traditional

classroom or face-to-face teaching. Some universities

are involved only in traditional methods of teaching,

while others combine the traditional method with

some amount of e-learning. In the wake of the

COVID-19 pandemic, most schools up to the tertiary

216

Okantey, M., Annan, N., Soku, D., Mensah, S., Korboe, I. and Frimpong, K.

Continuity of Academic Work through e-Learning during the COVID-19 Lockdown: The Case of Ghana.

DOI: 10.5220/0010443302160224

In Proceedings of the 13th International Conference on Computer Supported Education (CSEDU 2021) - Volume 2, pages 216-224

ISBN: 978-989-758-502-9

level had to continue academic work. The only option

available in this current situation as an emergency

response was to resort to some form of e-Learning as

recommended by the president.

Responses by education providers have been varied

from having no response to social isolation strategies

on campus and rapid curriculum redevelopment for

fully online offerings (Crawford et al., 2020).

The context of the problem is that Online

education has become necessary in the wake of the

COVID-19 pandemic, with most universities

resorting to various online means of continuing

academic work to avoid the disruption in the

academic calendar. Schools rushed to convert

teaching resources used with the traditional face-to-

face method to online content as a necessary means

of engaging students to minimize the effect on

academic work during the COVID-19 pandemic. The

challenge is the appropriateness and effectiveness of

these technologies and the consideration of

pedagogical issues that come with the use of these

technologies. In the bid to continue academic work

and prevent the disruption of most universities’

academic calendar, responses were sudden and

spontaneous.

With this background, this work seeks to find out

the extent to which the technologies are used, how

they were used, and the effectiveness of their usage

as pedagogical tools.

The study’s objectives are to categorize the

technologies used by universities during the COVID-

19 pandemic to determine the extent of use of these

technologies during the pandemic and examine the

effectiveness of these technologies for teaching and

learning. The researchers put forward three research

questions:

1. What categories of technologies were used by

universities during the COVID-19 pandemic?

2. To what extent were these technologies used

during the pandemic?

3. To what extent were these technologies

effective for teaching and learning during the

pandemic?

2 LITERATURE REVIEW

University education worldwide in the past had

mostly taken the form of on-campus teaching/

learning (Osei & Mensah, 2011) with some

Universities practising Distance Education, that is, an

approach that focuses on opening access to education

and training provision, freeing learners from the

constraints of time and place and offering flexible

learning opportunities to individuals and groups of

learners (Patru et al., 2002).

In Ghana, when on-campus face to face

teaching/ learning came to a standstill due to the

outbreak of the coronavirus that was transmitted from

human to human on 12

March 2020, the

Government of the day recommended E-Learning,

especially to the third cycle institutions of learning.

E-learning is seen as an innovative approach for

delivering well-designed, learner-centred, interactive,

and facilitated learning environment to anyone,

anyplace, anytime by utilizing the attributes and

resources of various digital technologies along with

other forms of learning materials suited for open,

flexible, and distributed learning environment (Khan,

2005). It is the act whereby telecommunication

technology is used in the delivery of training and

education (Goyal, 2012). This medium liberates both

parties involved in the training from limitations of

time and space through learning network models (P.-

C. Sun et al., 2008). It would also ensure that the

academic work was not disrupted. Different authors

have used other terms to describe E-Learning, such as

online teaching (Dhull, 2019), virtual learning system

(Kimovski et al., 2001), distributed-learning (Victor

& Hart, 2016), and web-based learning (Poon et al.,

2004). The “E” in E-Learning stands for

“Electronics”. It stands for all electronic devices that

are used in the teaching/ learning activity such as

mobile phone, laptops, Internet, Intranet, web just to

mention a few. In other words, E-Learning

incorporates all educational activities carried out by

individual or groups online or offline.

E-Learning activities are classified into two

modes, namely synchronous and asynchronous

modes. In the former, classes take place at an agreed

time, and one can observe communication between

the instructors and the learner through

teleconferencing. In the latter, learners access

educational material in their convenient time

anywhere at any time they wish (Nadia, 2006). From

this classification, it becomes evident that the

Government call on lecturers’ use of online teaching

is about using the synchronous mode.

Modalities of E-learning activities are classified

into four: individualized self-paced E-Learning

online where the learner uses the internet to access

learning materials and the Individualized self-paced

E-Learning offline, where individual accesses

learning materials without been connected to the

internet. The others are synchronous E-Learning,

where a group of learners are working together at an

agreed time via the Internet (Shaikh, 2011). In this

also we observe that what the Government meant for

Continuity of Academic Work through e-Learning during the COVID-19 Lockdown: The Case of Ghana

217

the tertiary institutions is the group-based E-Learning

synchronously. Talking about which model to use, it

is noted that several models have been put forwards

by researchers in the field of E-Learning. While one

group of model emphasizes on the interactions

between the teacher, student and the content, the other

one focuses on embedding tacit knowledge in

pedagogical model to enhance the e-learning

activities. For instance Anderson and others proposed

a model in which the instructor and the learner would

be represented. In this also they recommended that

the material resource should be made available to the

learner to access on the Internet. Another writer puts

forward a pedagogical model with three phases

namely for E-Learning activities namely: the content

organization phase where the content of the study are

collected and organized based on the requirements of

the industry or institution, the quality assessment

phase where theoretical and experimental methods

are used to analyze and access the contents. The final

stage is the content delivery phase, where the learner

is assessed to find out his aptitude and level (Irfan &

Shaikh, 2008). Pedagogy, the act of teaching, is

founded on some psychological theories.

Other studies that focused on how theories,

practices and assessments were applied to online

learning and carried out on 47 published works and

researches on online teaching/learning in 2008 using

the qualitative content analysis Method concluded

that effective online instruction is dependent upon

three factors, namely: well-designed course content

and motivated interaction between the learners and

instructors; creation of a sense of online learning

community; and the rapid advancement of technology

(A. Sun & Chen, 2016).

3 THEORETICAL FRAMEWORK

Adoption of technology to be used to perform any

kind of task can be viewed from an individual or

institutional perspective. In this study, we consider e-

learning as the technology and its adoption for

teaching and learning as a stop-gap measure during

the Corona Virus Disease (COVID-19) pandemic.

The study takes a look at both individual and

institutional factors of e-learning adoption. There are

several Information System (IS) theories which

researchers use to determine the factors which affect

technology adoption and use depending on the

objective of the study.

Some of the theories which have been used in e-

learning studies are the Technology Acceptance Model

1, 2 & 3 (TAM 1, TAM 2 & TAM 3) (Davis, 1985;

Hanif et al., 2018), Unified Theory of Acceptance and

Use of Technology 1 & 2 (UTAUT 1 & UTAUT 2)

(Chang, 2012; Huang & Kao, 2015) and Theory of

Reasoned Action (TRA) (Ajzen, 1991; Fishbein,

1979). The rest is the Theory of Planned Behaviour,

Innovation Diffusion Theory, Model of PC utilization

and General Extended TAM for E-Learning

(GETAMEL) (Abdullah & Ward, 2016a). The

theoretical underpinning of this study focuses on TAM,

UTAUT and GETAMEL to establish a conceptual

framework based on the objectives of the study.

Abdullah et al. developed The General Extended

Technology Acceptance Model for E-Learning

(GETAMEL) to investigate relevant external factors of

the Technology Acceptance Model (TAM) within the

context of e-learning adoption (Abdullah & Ward,

2016b). GETAMEL identified five external factors as

having a significant influence on Perceived Usefulness

and Perceived Ease of Use, According to the

proponents of GETAMEL, Experience (XP),

Subjective Norm (SN), Enjoyment (ENJOY),

Computer Anxiety (CA) and Self-Efficacy (SE) are

significant external factors which impact on TAM in

the adoption of e-learning.

3.1 Conceptual Framework

The conceptual framework takes its theoretical

background from TAM, UTAUT -2 and GETAMEL.

In this study, it is acknowledged that the mass

adoption of e-learning technology for teaching and

learning was necessitated under a state of emergency

of the COVID-19 lockdown as a stop-gap measure to

continue academic work. This came as a surprise to

most educational institutions, especially those who

have not been doing e-learning. Because of this, the

Perceived Usefulness (PU) and Perceived Ease of Use

(PEOU) which influence attitude toward using and

behavioural intention to use e-learning technology in

this context are further influence by external factors.

In our investigation, we propose that Subjective

Norm (SN), Price Value (PV) and System

Accessibility (SA) are some external factors that

influence the factors of TAM as shown in figure 1.

The conceptual framework is to show that Subjective

Norm (SN), Price Value (PV) and System

Accessibility (SA) have a correlation with Perceived

Usefulness (PU), Perceived Ease of Use (PEOU),

Attitude and Intention to use e-learning system. In

similar works, using TAM, UTAUT-2 and

GETAMEL have shown that there exist to some

extent, a degree of correlation between the external

factors and the core constructs of TAM and UTUAT.

CSEDU 2021 - 13th International Conference on Computer Supported Education

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Figure 1: Conceptual Framework.

3.1.1 Subjective Norm

Subjective norm (SN) is the social behaviour of how

people act based on their beliefs and assumptions of

how other people think of them. The adoption of e-

learning, in this case, was not optional; therefore, the

subjective norm (SN) might not be an influencing

factor. However, selecting a particular e-learning

technology can be significantly influenced by

subjective norm (SN). In this situation, the

hypothetical assumption is that the adoption of a

particular e-learning system by a school on behalf of

faculty and students is influenced by what other peer

or higher esteemed schools are using. The schools’

decision in this context represents that of faculty and

students to use a specific e-learning system. The

hypothesis for this external factor is as follows:

H5: Subjective norm (SN) influence attitude toward

usage (ATU).

H6: Subjective norm (SN) influence perceived

usefulness (PU).

H7: Subjective norm (SN) influence perceived ease of

use (PEOU).

3.1.2 Price Value

Price value (PV) is a trade-off between what the user

stands to benefit from the technology and the sacrifice

to make. Findings from related research show that

price value (PV) is vital in influencing consumers

intention to use. It also shows that price value (PV)

tends to have a positive influence on the intention to

use when the benefit from the technology is far more

than the monetary commitment. Venkatesh et al.

explain price value (PV) as the cognitive trade-off of

the consumer between the anticipated benefits of the

technology and the financial cost. From the marketing

background price value (PV) has two dimensions:

financial cost and nonfinancial cost. The financial

cost is the value that is derived in contrast to the price

that the consumer pays for the technology while the

nonfinancial cost is the value that is obtained in return

for time and efforts expended. In this study, the price

value (PV) is evaluated using both the financial and

nonfinancial values to explore the influence on

schools attitude toward using particular e-learning

technology with the following hypothesis.

H1: Price value (PV) influence perceived usefulness

(PU).

H2: Price value (PV) influence attitude toward usage

(ATU).

3.1.3 System Accessibility

System accessibility (SA) is when a user can have

access to use the technology conveniently and

frequently without obstructions. The challenge of a

lack of required technical infrastructure, including

unstable internet connection makes it difficult for the

user to access and conveniently use the technology.

Students relent to use e-learning platforms when it has

network connectivity, speed and access reliability

problems. Accessibility technically is a factor which

affects the usability and success of an e-learning

system. Consequently, system accessibility (SA) is one

of the significant external factors that other studies

have shown using TAM to determine e-learning

adoption. An e-learning system which has poor system

accessibility, irrespective of a positive attitude toward

usage and intention to use, the system will not be

usable. The hypothesis for this factor is as follows:

H8: System accessibility (SA) influence perceived

ease of use (PEOU).

H9: System accessibility (SA) influence perceived

usefulness (PU).

3.1.4 Perceived Usefulness

Perceived Usefulness (PU) is the extent to which a user

of any technology believes that using that technology

can facilitate his/her job performance. This is one of

the core constructs of TAM, which has proved a

significant favourable influence on attitude toward

usage and intention to use. How useful faculty and

students perceive the e-learning system will influence

their attitude toward usage and behavioural intention to

use. The hypothesis to evaluate this construct is:

H3: Perceived usefulness (PU) influence attitude to

use (ATU).

The conceptual framework expands the research

questions as follows to achieve the objective of the

study based on the identified hypothesis.

To determine the technology used by schools

Q1: What technologies were adopted by schools?

Q2: What factors influenced the decision to adopt the

technologies?

To examine the effectiveness of these technologies

Continuity of Academic Work through e-Learning during the COVID-19 Lockdown: The Case of Ghana

219

Q3: How effective were the technologies for teaching

and learning?

To determine the extent of use of these technologies

Q4: How were the technologies used for teaching and

learning?

Q5: What were the roles and responsibilities of

faculty and students in using the technologies?

Q6: What were the factors which influenced the use

of technology by faculty and students?

Q7: How useful were the technologies?

3.1.5 Perceived Ease of Use

Perceived ease of use (PEOU) is the extent to which

a person thinks that it is easy to use a particular

technology to perform a task. Existing research shows

that PEOU has a positive influence on attitude toward

usage and perceived usefulness (PU). Similarly, we

are hypothesizing that faculty and students perceived

ease of use of the e-learning system would influence

their attitude toward the use and perceived usefulness

of the system as follows:

H4: Perceived ease of use (PEOU) influence attitude

to use (ATU).

4 METHODOLOGY

4.1 Research Design / Method

To a large extent, the scientific validity of any

empirical study depends on the research design. The

research design is what provides the scientific basis for

interpreting the findings based on a specific philoso-

phical world view. In this study, the survey approach

of research design is used to observe education

institutions adoption of e-learning technology during

the COVID-19 and interpret the findings based on

existing theory. The world view which forms the basis

for this research design is more of post-positivism.

Because of this, the quantitative research strategy of

inquiry is chosen as a means for creating knowledge.

Although there are several stakeholders within the

tertiary education ecosystem, the research was

centred around the three (3) main stakeholders –

Students, Faculty and Administrative staff because of

their involvement in the use of the e-learning platform

during the COVID-19 pandemic. This formed the

basis for the population sample, data source and data

analysis. The selected stakeholders who constituted

the participants, performed different roles with the

e-learning platform. Therefore they had different user

experiences, which helped in appreciating their

perspectives.

4.2 Data Source

The population for the study was students, faculty, and

administrative staff from university institutions in

Ghana, which served as the data source. The

participation of members of the population sample was

mainly individual decision and willingness to take part

in the study. Because of this, the sample is convenient

and does not constitute a representative sample of all

the universities in Ghana. The respondents were not

given any incentive for their participation.

4.3 Data Collection Instrument

Google forms was used to prepare an online

questionnaire which was posted on the internet for the

participants to respond. Three (3) different sets of

questionnaires were developed for the various groups

of participants. The sections of the questionnaire

included technology adoption, usage, usefulness, ease

of use and demography, among others. The

questionnaires were all administered at the same time

for a period of six (6) weeks. In all 344 students, 33

faculty and 6 administrative staff responded

representing 90%, 9% and 1% respectively of the

total respondent from nine (9) universities.

5 FINDINGS

5.1 Research Model and Hypothesis

Having discussed the objectives and purpose of this

study, the research model for this study adapts the

Technology Acceptance Model. The model was

adopted to ascertain relationships between the factors

and how it applies to the continuity of academic work

through e-learning during the COVID-19 lockdown.

Figure 2 illustrates the research model:

Figure 2: Research Model for the study. Source: Author.

Perceive

usefulness



Actual

Use



Antecedentfactors

a.SubjectiveNorm

b.Accessibility

c.PriceValue

h2(a, b, c)

h1(a, b, c,)

Attitude

towards

use



Behavioral

Intention



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220

Guided by the trust path model, the study

adopted the quantitative survey research

methodology. The tested model comprises seven

constructs, each having multiple items that are

measured using a seven-point the likert scale ranging

from one to seven where 1= strongly disagree | 2 =

disagree | 3 = neutral | 4 = agree | 5 = strongly agree.

Respondents were asked to respond to a series of

questions using the likert scale ranging from one to

seven where 1= strongly disagree | 2 = disagree |3=

neutral |4 = agree | 5 = strongly agree. Table 4.1

illustrated below shows the factors, the number of

items under each scale, reliability assessment of pilot

study using cronbach alpha.

Table 1: Questionnaire Development.

Factors

No. of

questions

Cronbach

Alpha

Subjective Norm 5 0.53

Perceived ease of use 4 0.80

Perceived usefulness 4 0.93

Behavioural intention to use 5 0.91

Accessibility 5 0.81

Attitude towards use 4 0.91

Price Value 4 0.75

Actual use 3 0.86

For the student survey the instrument was divided

into two sections. The first section covered the

demographic details, the second section highlights the

path model of that contains eight factors that include;

Subjective Norm (SN), Perceived ease of use (PEOU),

Perceived usefulness (PU), Behavioural intention to

use (BI), Accessibility (AC), Attitude towards use

(AT), Price Value (PV) and Actual use (AU).

5.2 Demographic Analysis

Out of the 344 valid questionnaires retrieved, the data

showed that 137 respondents were male, representing

39.8% and 207 were female representing 60.2% of

the dataset, respectively. Again from the data set, it

was shown that the age bracket from 25-34yrs

constituted majority of the respondents representing

51.2% whereas the least participating respondents

were the 45 -54 ae bracket representing 1.7% of the

data set. In terms of different programmes that the

respondents belonged to within the education sector,

it was also shown that e-learning was prevalent within

the Humanities field since it constituted 49.1%,

followed by them were those that belonged to the

Health Sciences field, which constituted 31.1%

respectively. The least percentage of respondents in

the data set was from the Education sector, which

constituted 0.3%.

Furthermore, the dataset received more

respondents from private universities than public

ones. From the data, 276 of the respondents from the

private universities constituted 80.2% of the

respondents, whereas 68 respondents representing

19.8% of the dataset were from public tertiary

institutions. Again, from the Covid-19 period, 37

respondents from tertiary institutions representing

only 10.8% admitted to have used more than five (5)

technological platforms for virtual learning. 25.6%

represented by 88 respondents did not use

technology-enabled learning at all. However, more

than 43.9% represented by 151 respondents form

tertiary institutions used between 2 and 3 technology

platforms for teaching and learning.

In terms of contact hours for e-learning, 60.8% of

the respondents admitted that the maximum contact

hours used for virtual learning was 1hr 30 mins per

lecture, whereas 9.3% of the respondents that their

maximum contact hours were less than 30mins of

lecture but 27.9% of the respondents admitted to have

had maximum contact hours from between 30 to

60mins.

5.3 Measurement Model

The Confirmatory Factor Analysis (CFA) was used as

an assessment of fit to ascertain dimensionality and

internal consistency for construct measurement. CFA

is applied to confirm a good representation of the

proposed items of constructs and the conceptual

model (Hair, Ringle, & Sarstedt, 2011). Hence, the

essence of a CFA test is to ascertain whether the

proposed research model and the data collected are

consistent or agree together in simple terms. About

this study. The fit statistics values for the structural

model were all within acceptable limits. The

minimum discrepancy or χ

(Chi-square) = 176.7, df

(Degree of Freedom) = 329, the Standardized Root

Mean Square Residual (SRMR) = 0.04, Root Mean

Error of Approximation (RMSEA) = 0.05, χ

/df ratio

= 1.74, Comparative Fit Index (CFI) = 0.98,

Goodness Fit index (GFI) = 0.94, Incremental Fit

Index (IFI) = 0.98, Tucker-Lewis Index (TLI) = 0.97

and Normed Indices of Fit (NFI) = 0.95.

5.4 Structural Model

In assessing the structural model, the data should

support the model’s specified theoretical relation-

Continuity of Academic Work through e-Learning during the COVID-19 Lockdown: The Case of Ghana

221

ships (Bagozzi & Yi, 2012). The hypothesis testing

was conducted in these parts; Hypotheses 1(a, b, c);

to assess the relationship between perceived

usefulness(PU) and its antecedent factors;

Hypotheses 2(a, b, c); to assess the direct

relationships between the Subjective Norm(SN),

Accessibility (AC), Price Value (PV) and Actual Use

(AU) respectively. Hypotheses 3; assesses the

relationship between Price value (PV) and Actual Use

(AU); Hypothesis 4, assesses the relationship

between Attitude towards Use (AT) and Behavioural

Intention to use (BI); and lastly, Hypothesis 5,

determines the relationship between Behavioural

Intention to use (BI) and Actual Use (AU). The

results revealed that except for the relationship

between SN and PU, SN and AU, PV and AU, all the

other relationships were found to be significant. This

implies that apart from H1a, H1c H2a, and H2c, all

the other hypothesis H1b, H1c, H2b, H3, H5, H5, H6

were all significant. Hence, path analysis of the

structural model as shown in Table below.

Table 2: Indices for the structured model.

Hypothesized Path

Standardized

Coefficients

t-value p-value Results

H1a: Subjective Norm

- Perceived Usefulness

0.18 0.219 0.827 Unsupported

H1b: Accessibility-

Perceived Usefulness

0.64 7.76 *** Supported

H1c: Price Value -

Perceived Usefulness

-0.29 .583 .827 Unsupported

H2a: Subjective

Norm-Actual Use

0.11 5.8 0.12 Unsupported

H2b: Accessibility-

Actual Use

0.20 5.8 *** Supported

H2c: Price Value -

Actual Use

0.002 0.29 9.77 Unsupported

H3: Perceived

Usefulness – Attitude

towards Use

0.88 15.79 *** Supported

H4:Attitude Towards

Use - Behavioral

Intention to Use

0.90 11.53 *** Supported

H5: Behavioral

Intention to Use -

Actual Use

0.50 9.83 *** Supported

*p< 0.05, **p < 0.01, ***p <0.001

5.5 Mediation Analysis

As indicated in the table below, by using Baron and

Kenny approach the results produced no mediation

and partial mediation. From the table below, three

main mediators of the model were assessed. These are

perceived usefulness and Attitude towards Use and

Behavorial Intention to Use. As shown from below it

was found that PU produced a No Mediation between

Subjective Norm and Attitude. Again PU also

produced a No mediation between Price Value and

Attitude towards Use.

Table 3: Mediation Effect.

Relationships

Direct

without

mediator

Direct

with

mediator

Mediation

effect

Subjective Norm →

Perceived Usefulness →

Attitude towards use

0.037(0.64) 0.23(0.68)

Mediation

Price Value → Perceived

Usefulness → Attitude

towards Use

0.006(0.9) 0.038(0.4)

Mediation

Accessibility →

Perceived Usefulness →

Attitude towards Use

0.67(***) 0.26 (***)

Partial

Mediation

Perceived Usefulness →

Attitude towards Use →

Behavioural Intention to

Use

0.86(***) 0.91(***)

Partial

Mediation

Attitude towards Use →

Behavioural Intention to

Use → Actual Use

0.62(***) 0.68 (***)

Partial

Mediation

*p< 0.05, **p < 0.01, ***p <0.001

Source Author.

Figure 3: Final Structural Model for Antecedents’ Factors

and Outcome of Trust.

From the figure above, it can be deduced that,

when e-learning was introduced, the strongest

relationship amongst the factors was perceived

usefulness and attitude towards use. This means that

once, tertiary institutions and it stakeholders realize

that a technology or virtual platform is useful, it will

influence their attitude towards using. As it can be

Perceived

Usefulness

Actual

Use

Subjective

Norm

Price

Value

Accessibility

-0.29 ns

0.64***

0.2 ns

Attitude

towards

Use

Behav-

ioural

Intention

0.88***

0.50***

0.90 ***

0.67(***)

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seen that the relationship between perceived

usefulness and attitude towards use is 0.9 with

significance. Secondly, accessibility remains the

strongest determinant to perceived usefulness.

Accessibility is the only factor that has a strong

positive influence on perceived usefulness. Secondly,

it is the only factor that directly has a strong

relationship with Attitude towards use without a

mediator. From then on, it is clearly seen that PU has

a strong positive relationship with AU with a

significant value of 0.88, AU also has a strong

positive relationship with BI with a value of 0.90 and

BI also has a moderate relationship with the resultant

variation with a regression weight of 0.50.

(Chi-square) = 176.7, df (Degree of Freedom) = 113, SRMR

= 0.03, RMSEA = 0.95, χ

/df ratio = 1.56, CFI = 0.98, GFI =

0.95, IFI = 0.98, TLI = 0.98 NFI = 0.96

Figure 4: Stage One: Measurement Model.

6 CONCLUSIONS

In conclusion, the study shows that accessibility has

direct positive influence on both perceived usefulness

and attitude toward use. The main constructs

perceived usefulness and attitude towards use also

showed strong relationship toward behavioural

intention to use. The findings provide a clear

relationship between SN and PU, SN and AU, PV and

AU, and further shows that all the other relationships

are significant. Therefore, it implies that hypothesis

H1b, H1c, H2b, H3, H5, H5, and H6 are significant.

REFERENCES

Abdullah, F., & Ward, R. (2016a). Developing a General

Extended Technology Acceptance Model for E-

Learning (GETAMEL) by analyzing commonly used

external factors. Computers in Human Behavior, 56,

238–256. https://doi.org/10.1016/j.chb.2015.11.036

Abdullah, F., & Ward, R. (2016b). Developing a General

Extended Technology Acceptance Model for E-

Learning (GETAMEL) by analyzing commonly used

external factors. Computers in Human Behavior, 56,

238–256. https://doi.org/10.1016/j.chb.2015.11.036

Ajzen, I. (1991). The Theory of Planned Behavior.

Organizational Behavior and Human Decision

Processes, 50, 179–211. https://doi.org/10.1016/0749-

5978(91)90020-T

Bagozzi, R. P., & Yi, Y. (2012). Specification, evaluation,

and interpretation of structural equation models.

Journal of the Academy of Marketing Science, 40(1), 8–

34. https://doi.org/10.1007/s11747-011-0278-x

Chang, A. (2012). UTAUT and UTAUT 2: A Review and

Agenda for Future Research. The Winners, 13, 10.

https://doi.org/10.21512/tw.v13i2.656

Crawford, J., Butler-Henderson, K., Jurgen, R., Malkawi,

B. H., Glowatz, M., Burton, R., Magni, P., & Lam, S.

(2020). COVID-19: 20 countries’ higher education

intra-period digital pedagogy responses. Journal of

Applied Learning & Teaching.

Davis, F. D. (1985). A technology acceptance model for

empirically testing new end-user information systems:

Theory and results.

Dhull, I. (2019). Online Learning. International Education

& Research Journal (IERT), 32, 32.

Fishbein, M. (1979). A theory of reasoned action: Some

applications and implications. Nebraska Symposium on

Motivation, 27, 65–116.

Goyal, S. (2012). E-Learning: Future of Education. Journal

of Education and Learning (EduLearn), 6(4), 239–242.

https://doi.org/10.11591/edulearn.v6i4.168

Hanif, A., Jamal, F. Q., & Imran, M. (2018). Extending the

Technology Acceptance Model for Use of e-Learning

Systems by Digital Learners. IEEE Access, 6, 73395–

73404. https://doi.org/10.1109/ACCESS.2018.2881384

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM:

Indeed a Silver Bullet. Journal of Marketing Theory

and Practice, 19(2), 139–152. https://doi.org/10.2753/

mtp1069-6679190202

Haruna, I. (2020). (16) (Pdf) Economic Impact of Covid-19

On Ghana: What Are The Channels? https://www.

researchgate.net/publication/340117125_ECONOMIC

_IMPACT_OF_COVID-19_ON_GHANA_WHAT_

ARE_THE_CHANNELS

Huang, C.-Y., & Kao, Y.-S. (2015). UTAUT2 Based

Predictions of Factors Influencing the Technology

Acceptance of Phablets by DNP [Research Article].

Mathematical Problems in Engineering; Hindawi.

https://doi.org/10.1155/2015/603747

Irfan, R., & Shaikh, M. U. (2008). Framework for

Embedding Tacit Knowledge in Pedagogical Model to

Enhance E-Learning. 2008 New Technologies, Mobility

Continuity of Academic Work through e-Learning during the COVID-19 Lockdown: The Case of Ghana

223

and Security. https://doi.org/10.1109/NTMS.2008.E

CP.48

Khan, B. (2005). Managing E-Learning Strategies: Design,

Delivery, Implementation and Evaluation. In

Information Science Publishing. Information Science

Publishing.

Kimovski, G., Trajkovik, V., & Davcev, D. (2001). Virtual

Learning System.

Nadia, S. (2006). E-Learning: A Guidebook of Principles,

Procedures and Practices. http://oasis.col.org/handle/

11599/53

Osei, C. K., & Mensah, J. A. (2011). A comparative study

of student academic performance in on-campus

teaching and distance learning in a Computer

Engineering Programme. Journal of Science and

Technology (Ghana), 31(1), Article 1. https://doi.org/

10.4314/just.v31i1.64890

Ozili, P. K., & Arun, T. (2020). Spillover of COVID-19:

Impact on the Global Economy. SSRN Electronic

Journal. https://doi.org/10.2139/ssrn.3562570

Patru, M., Khvilon, E., & UNESCO. (2002). Open and

distance learning: Trends, policy and strategy

considerations. http://Lst-Iiep.Iiep-Unesco.Org/Cgi-Bin/

Wwwi32.Exe/[In=epidoc1.in]/?T2000=016972/(100).

Poon, W.-C., Low, K. L.-T., & Yong, D. G.-F. (2004). A

Study of Web-Based Learning (WBL) Environment in

Malaysia. International Journal of Educational

Management, 18(6), 374–385.

Shaikh, M. U. (2011). E-Learning System Incoporation

Industrial and Academic Requirements. 1–3.

Shereen, M., Khan, S., Kazmi, A., Bashir, N., & Siddique,

R. (2020). COVID-19 infection: Origin, transmission,

and characteristics of human coronaviruses. Journal of

Advanced Research, 24. https://doi.org/10.1016/j.jare.

2020.03.005

Sun, A., & Chen, X. (2016). Online Education and Its

Effective Practice: A Research Review. Journal of

Information Technology Education: Research, 15, 157–

190.

Sun, P.-C., Tsai, R., 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, 1183–1202. https://doi.org/10.1016/

j.compedu.2006.11.007

Victor, S., & Hart, S. (2016). Distributed Learning: A

Flexible Learning and Development Model. 10.

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