Preparing first-year students in higher education for ethical decision making: Identifying and understanding personal values in a South African Ubuntu context

Full text

Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journalCode=rsar20
South African Journal of Accounting Research
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/rsar20
Acceptance of e-learning applications by
accounting students in an online learning
environment at residential universities
Wendy Terblanche, Ilse Lubbe, Elmarie Papageorgiou & Nico van der Merwe
To cite this article: Wendy Terblanche, Ilse Lubbe, Elmarie Papageorgiou & Nico van der
Merwe (2023) Acceptance of e-learning applications by accounting students in an online learning
environment at residential universities, South African Journal of Accounting Research, 37:1, 35-61,
DOI: 10.1080/10291954.2022.2101328
To link to this article: https://doi.org/10.1080/10291954.2022.2101328
Published online: 09 Aug 2022.
Submit your article to this journal
Article views: 197
View related articles
View Crossmark data

Acceptance of e-learning applications by accounting students in an
online learning environment at residential universities
Wendy Terblanche *
Nkuhlu Department of Accounting, University of Fort Hare, East London, South Africa
Ilse Lubbe
College of Accounting, University of Cape Town, Cape Town, South Africa
Elmarie Papageorgiou
School of Accountancy, University of the Witwatersrand, Gauteng, South Africa
Nico van der Merwe
School of Accounting Sciences, North-West University, Potchefstroom, South Africa
(Received 23 January 2022; accepted 6 July 2022)
Technology acceptance models have been used in the higher education context to
understand students’acceptance of various learning technologies. Not only was the
use of e-learning technologies heightened during the COVID-19 pandemic, but the
shift to predominantly online teaching and learning was abrupt. It has become clear
that acceptance of e-learning technology will be crucial for higher education in a post-
COVID-19 world. Thus, the purpose of this study was to determine the acceptance of
e-learning applications by accounting students at residential universities in South Africa.
The UnifiedTheory of Acceptance and Useof Technology(UTAUT2)was adapted for
this study to examine the relevance of its constructs in understanding students’intent to use
e-learning applications. Accounting students registered at four South African universities
completedan electronic questionnaire (n= 1 864). StructuralEquation Modelling usingthe
Partial Least Squares method was used to test the hypothesised relationships.
The findings indicate that performance expectancy, social influence, facilitating
conditions, and habit have a significant relationship with behavioural intention to use e-
learning applications. However, gender, academic performance, and level of study do
not have a significant moderating effect on these relationships.
The study reported in this paper contributes to technology acceptance research by
testing the UTAUT2 model in a cross-institutional context with a larger sample size
than used in similar studies. Furthermore, it has practical value for higher education
policymakers, institutions, and lecturers in their attempts to adapt to blended and online
learning models.
Keywords: technology acceptance; e-learning; Unified Theory of Acceptance and Use
of Technology (UTAUT2); South Africa; accounting education; higher education
1. Introduction
Higher education institutions have shifted their pedagogical approach over the past three
decades to align with and utilise changing technologies (Ng’ambi et al., 2016). South
© 2022 South African Journal of Accounting Research
South African Journal of Accounting Research is co-published by NISC Pty (Ltd) and Informa Limited (trading as Taylor & Francis Group)
*Corresponding author. Email: wterblanche@ufh.ac.za
South African Journal of Accounting Research, 2023
Vol. 37, No. 1, 35–61, https://doi.org/10.1080/10291954.2022.2101328

African universities’adoption of electronic learning (e-learning) has been strongly influ-
enced by both global trends and the South African economic and digital development
context (Cranfield et al., 2021;Ng’ambi et al., 2016). Although most residential univer-
sities had adopted e-learning to varying degrees, in 2020 a precipitous and involuntary
shift from classroom-based to online learning was brought about by the COVID-19 pan-
demic (Czerniewicz et al., 2020). According to the International Association of Univer-
sities (2020), more than 1.5 billion students and youth across the planet have been
affected by school and university closures during the COVID-19 outbreak.
Managing the transition to online learning has presented several challenges to students,
lecturers, and universities, including access to the internet, suitable devices and conducive
study environments (Cranfield et al., 2021; Czerniewicz et al., 2020; Marinoni et al., 2020).
In reflection upon these challenges, it has become clear that the interventions implemented
by four residential universities in South Africa and accounting students’experience of the
shift to online learning can provide useful insights into how higher education in South
Africa may continue in the current decade to adapt to and benefit from e-learning and
the use of e-learning applications.
For technologies to be effective, they must be accepted and used (Granić& Marangu-
nić,2019; Venkatesh et al., 2003; Venkatesh et al., 2012). Technology acceptance is an
important topic in information technology research in general (Al-Emran & Granić,
2021; Hwang et al., 2016) and in educational research in particular (Granić& Marangunić,
2019). For online learning, e-learning technologies must be accepted and used by students,
otherwise optimal learning will not take place. Acceptance of e-learning technology is
especially crucial for higher education in a post-COVID-19 world.
The purpose of this study was to examine accounting students’acceptance of e-learn-
ing applications, the determinants of students’intention at residential universities to use e-
learning applications, and the factors that moderate these. The following research ques-
tions were identified:
1) What are the determinants (constructs) of accounting students’acceptance of e-
learning applications in an online learning environment?
2) What are the moderating factors that have an effect on these determinants?
To address the research questions, a quantitative research methodology was followed to
administer and analyse the findings of an online questionnaire, namely the Unified Theory
of Acceptance and Use of Technology (UTAUT2) questionnaire developed by Venkatesh
et al. in 2012.
The study involved four universities in South Africa, a diverse nation with a population
of roughly 60 million people. South Africa is known for its variety of cultures, 11 official
languages, different religious beliefs, and socio-economic diversity (South African Gov-
ernment, 2021). This cross-institutional study robustly tested the UTAUT2 model in a
unique context with a larger sample size and provides insight into the determining
factors that motivate accounting students to use e-learning applications. The findings
also provide a better understanding of the factors that moderate the determinants of stu-
dents’intention to use such applications. The value of this study lies in the fact that it ident-
ifies determinants that predict the intention of accounting students to use e-learning
applications, which will, in turn, assist higher education institutions in the design of
online and blended learning environments.
The next section sets out the literature review and hypothesis development. The
method and data collection are discussed thereafter, followed by the results and the key
36 W. Terblanche et al.

findings. The paper closes with a conclusion, including an acknowledgement of the limit-
ations of the study and recommendations for future research.
2. Literature review and hypotheses development
The literature discussed in this section pertains to the changes in the higher education land-
scape, the shift to the use of technology, and technology acceptance models. The 10
hypotheses for the study were developed from the review of the aforementioned literature.
The literature review revealed several previous studies that have applied technology accep-
tance models. However, the authors could find no study that explored and applied the
UTAUT2 model in a higher education setting in South Africa, using accounting students
as participants.
2.1. Technology in higher education
The higher education landscape has undergone rapid changes in a drive for an online learn-
ing pedagogy to be adopted by lecturers (Ng’ambi et al., 2016). Since the middle of the last
decade, digital learning and online platforms have become an integral part of the daily life
of many students (Henderson et al., 2017). Online learning makes use of the internet and
implies a distance between students and lecturers, while e-learning is a form of online
learning conducted via electronic media which may be used to support both classroom-
based and distance learning (Kumar Basak et al., 2018; Riahi, 2015). E-learning and
mobile learning (m-learning) are both forms of digital learning with a number of digital
learning tools considered as both e-learning and m-learning tools (refer to Kumar Basak
et al. (2018) for a comparison of these concepts). In this paper, we define e-learning appli-
cations as interactive online services that provide learners with information, tools, and
resources which, in turn, support and enhance education delivery and management
(Kumar Basak et al., 2018).
The potential of digital technologies to support teaching and student learning is well
established (Henderson et al., 2017). Several studies have focused on how technology is
incorporated into the learning space (Alfadda & Mahdi, 2021; Bond et al., 2018; Khan
et al., 2017; Wang, 2020). Benefits include the enhanced diversity of provision and
equity of access to higher education, alongside the increased efficiency of delivery and per-
sonalisation of learning processes (Czerniewicz et al., 2020).
However, the transition from traditional pedagogy and learning to digital and blended
formats is a process which requires institutional strategy, planning, and adoption (Graham
et al., 2013; Leow et al., 2021). Ng’ambi et al. (2016) identified four thematic phases in the
technology enhanced learning transition from 1996 to 2016, culminating in the fourth
phase predominated by mobile learning and social media, impacted by marketisation, digi-
tisation, and the unbundling of higher education (Czerniewicz et al., 2020).
Calls for online learning have shifted the method of teaching from the traditional lec-
turer-centred approach to a student-oriented approach, with the focus on what students
should be able to do with course material as opposed to what the lecturer should teach
(Sava, 2016). The traditional lecture format, commonly used in higher education, places
the student in a passive position, as the lecturer does most of the talking, most of the ques-
tioning, and thus most of the thinking (Agyei & Razi, 2021; Leow et al., 2021; Maiorana,
1991). Even so, innovative teaching styles and methods have evolved from ‘flipping the
classroom’(Brown et al., 2016;Lento, 2016) to gamification of learning (Bergdahl
et al., 2018; Oliveira et al., 2022; Salloum et al., 2019). E-learning enables students to
South African Journal of Accounting Research 37

revisit online course material, ask questions online and receive personal support, during
and outside of normal teaching hours (Mtshali, 2020). Technology-based pedagogical
methods and tools can support successful teaching in an online environment (Koehler
et al., 2014; Vonderwell & Turner, 2005). The move to online learning during the
COVID-19 pandemic further shifted the focus from lecturer-centred learning as students
now had to adapt to learning online.
One important change is the impact of the internet on higher education. Increasing
numbers of online courses are available and student enrolments have not only soared,
but continue to rise (Chen et al., 2018), indicating the need for universities to engage on
this platform effectively. An understanding of how students learn online and their partici-
pation in the online learning environment is critical to encourage high levels of student
engagement (Khan et al., 2017).
The fourth industrial revolution (4IR) brings significant benefits to higher education
through the introduction of new educational technologies to facilitate and support
student learning (Wang, 2020). Some technology-based learning methods/tools include
blogs, virtual worlds, gamified learning (Bergdahl et al., 2018; Salloum et al., 2019),
massive open online courses (Altalhi, 2021), wikis, simulations (Cummings et al.,
2015), podcasts, vodcasts, online discussion forums (Ohei & Brink, 2020), and digital
cases, where a student must make choices while working through a case study, leading
to pre-programmed outcomes (Dexter et al., 2020). Nevertheless, simply implementing
these educational technologies does not necessarily result in a high level of student engage-
ment and learning –they need to be utilised by the lecturer in a specific way (Bergdahl
et al., 2018).
Carcavallo (2020) argues that technology improves accounting undergraduate stu-
dents’ability to transition between theoretical concepts and practical application. A
recent study performed in Poland found that participants’engagement in e-learning had
a positive effect on their final performance (Krasodomska & Godawska, 2021). The
study observed a difference between engagement and performance based on nationality,
while gender was not found to be a significant difference. Therefore, accounting lecturers
would benefit from having an understanding of the determinants of students’acceptance of
e-learning applications as well as the ability to encourage students to engage with
technology.
The recent worldwide lockdown which started in late 2019 due to the COVID-19 pan-
demic has increased the use of online learning in 60% of universities (Marinoni et al.,
2020). The sudden switch from traditional pedagogy and learning to digital learning has
been problematic and left both lecturers and students in a state of uncertainty, often result-
ing in attempts to simply replicate offline approaches in the online space. Anderson (2020)
refers to the challenges faced in higher education due to the pandemic as having to achieve
the equivalent of a 10-year digital learning strategy in mere months. A digital learning
strategy involves both pedagogical changes and an adoption of relevant technologies.
This paper does not address the pedagogical aspects, but rather seeks to understand stu-
dents’behavioural intention to use e-learning applications.
2.2. Technology acceptance models
Several technology acceptance models have been developed with the aim to understand
better users’intention to use and their actual usage of technology (Oye et al., 2014).
Models such as the Technology Acceptance Model (TAM) (Venkatesh & Davis, 2000)
and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh
38 W. Terblanche et al.

et al., 2003) are key to understanding predictors of human behaviour towards potential
acceptance or rejection of technology. In attempting to empirically understand users’be-
havioural intentions in the domain of human–computer interaction research, researchers
have focused on technology acceptance models as the subject of several studies (Al-
Emran & Granić,2021).
Table 1 presents a brief summary of prior studies and models. (For a more detailed
history, refer to Oye et al. [2014].) Based on the social psychology-based general
models of Fishbein and Ajzen (1975, as cited in Sheppard et al., 1988) and Vallerand
(1997), models specific to the use of information systems and technology were developed
later.
In their study, Venkatesh et al. (2003) used data from organisations in four different
sectors (entertainment, telecom services, banking, and public administration) and com-
pared the results of eight models, which were able to explain between 17% and 53% of
variance in users’intention. The determinants of the eight models were distilled into
four determinants –performance expectancy, effort expectancy, social influence, and facil-
itating conditions –in the unified model which explained a significantly higher percentage
of variance in users’intention (69%). Four moderating factors –gender, age, experience,
and voluntariness of use –were included. In 2012, they extended their model for use in a
consumer context (UTAUT2) by adding three further determinants –hedonic motivation,
Table 1. Prior studies and models.
Prior studies Model
Fishbein and Ajzen (1975, as cited in
Sheppard et al., 1988)
Theory of Reasoned Action (TRA)
This study has its roots in social psychology and is a
widely accepted model for explaining and predicting
human behaviour.
Vallerand (1997)Motivational Model (MM)
This is an early seminal study supporting general
motivation theory as an explanation for behaviour.
Venkatesh and Davis (2000)Technology Acceptance Model (TAM)
This model was adapted for the information systems
context from TRA and designed to predict IT behaviour of
individuals on the job. It was later revised to TAM2 to
explain perceived usefulness and usage intentions in terms
of social influence (subjective norms, voluntariness,
image) and cognitive instrumental processes (job
relevance, output quality, result demonstrability, perceived
ease of use).
Venkatesh et al. (2003)Unified Theory of Acceptance and Use of Technology
(UTAUT)
This study described and analysed the prime theoretical
underpinnings of previous research using eight different
theoretical models, resulting in a unified theory with four
determinants: performance expectancy, effort expectancy,
social influence, and facilitating conditions.
Venkatesh et al. (2012)Extended Unified Theory of Acceptance and Use of
Technology (UTAUT2)
Venkatesh et al. (2012) extended the UTAUT model to a
consumer context by adding three determinants: hedonic
motivation, price value, and habit.
South African Journal of Accounting Research 39

price value, and habit –explaining 74% of the variance in users’behavioural intention to
use technology.
Most of the studies and technology acceptance models were initially aimed at the
private and public sector. In their review of such studies from 2010 to 2020, Al-Emran
and Granić(2021) concluded that “applying, modifying and extending the model is still
valid across several applications and domains”(p. 4). Granićand Marangunić(2019)
reviewed studies from 2003 to 2018 on technology acceptance in an educational context
and found that technology acceptance models represent a credible method for facilitating
assessment of diverse learning technologies. Al-Emran et al. (2018) in their review of
TAM in a mobile-learning context concluded that there is scope for many other factors
still to be examined. UTAUT2 has been used in an educational context in various
studies (Ameri et al., 2020; Arain et al., 2019; Attuquayefio & Addo, 2014; Moorthy
et al., 2019; Sitar-Taut & Mican, 2021; Yang, 2013) with and without moderating
factors. Social influence, performance expectancy, hedonic motivation, and habit have
most commonly been found to significantly predict behavioural intention (Ameri et al.,
2020; Arain et al., 2019; Yang, 2013). Except for Sitar-Taut and Mican (2021), who
found that age moderated performance expectancy, the studies that included age, gender,
and experience did not find any significant effect of these variables on the relationships
with behavioural intention.
The UTAUT2 model was selected for use in this study due to its explanatory power and
its relevance to the current context. The four determinants from the UTAUT model, plus
the three additional determinants specific to a consumer context in UTAUT2, were
included. The UTAUT2 model was applied with students being considered the consumers
of the learning technologies.
As mentioned above, the following research questions were identified:
1) What are the determinants (constructs) of accounting students’acceptance of e-
learning applications in an online learning environment?
2) What are the moderating factors that have an effect on these determinants?
From these two research questions, ten hypotheses were developed and framed using
the UTAUT2 model.
2.3. Hypothesis development
First, hypotheses were developed for the determinants of the original UTAUT model: per-
formance expectancy, effort expectancy, social influence, and facilitating conditions. Per-
formance expectancy (PE) is the “degree to which using a technology will provide benefits
to consumers in performing certain activities”(Venkatesh et al., 2012, p. 159).If students
believe that using e-learning applications will benefit their learning more quickly or more
productively, and lead to higher marks, then performance expectancy is expected to
increase students’behavioural intention to use e-learning applications.
Regarding effort expectancy (EE), if the use of e-learning applications is easy, clear,
and understandable and students can become skilful in using them easily, then students
are more likely to use them (Venkatesh et al., 2003).
Social influence (SI) is the “extent to which consumers perceive that important others
believe they should use a particular technology”(Venkatesh et al., 2012, p. 159). In an
online learning environment these important others may include lecturers and other stu-
dents, as well as family and friends. Support from people within both their learning and
40 W. Terblanche et al.

social environment may play a vital role in students’intention to use e-learning
applications.
It is critical for students to have the resources, knowledge, and assistance necessary to
use e-learning applications. These facilitating conditions (FC) are especially important in
an online learning environment when students are studying remotely (Camilleri & Camil-
leri, 2022).
Secondly, hypotheses were developed for the determinants added to the UTAUT2
model: hedonic motivation, price value, and habit. Hedonic motivation (HM), being
“the fun or pleasure derived from using a technology”(Ramírez-Correa et al., 2019,
p. 87), is positively related to consumers’intention to use technology (Ramírez-Correa
et al., 2019; Venkatesh et al., 2012). In a study performed by Kabilan et al. (2010) stu-
dents reported that learning English was more fun when using Facebook. Hedonic motiv-
ation is therefore expected to increase students’acceptance of the use of e-learning
applications.
In a consumer context, the price value (PV) of technology may relate to the consumers’
cost of software, hardware or data. In an online learning environment these costs may be
borne by the student or the institution. If e-learning applications are considered too expens-
ive to use or do not provide value for money, students are unlikely to use them (Venkatesh
et al., 2012).
Users have developed a habit (H) when they “tend to perform a behaviour automati-
cally because of learning”(Ramírez-Correa et al., 2019, p. 87). When the use of e-learning
applications has become a habit and students are in favour of using them, feel the need to
use them and usage has become a part of their daily lives, we expect to see a positive
relationship to behavioural intention.
Behavioural intention (BI) captures the motivational factors that influence behaviour
such as how hard people are willing to try and how much effort they plan to exert in
order to perform the behaviour (Ajzen, 1991). Behavioural intention is measured by stu-
dents’responses to questions about their intention to use e-learning applications in
future, to use them on a daily basis and their plans to continue using them frequently.
The relationships are specified in the following set of hypotheses (adapted from
Ramírez-Correa et al., 2019):
H1: Performance expectancy, PE, is positively related to behavioural intention, BI, in the
adoption of e-learning applications by accounting students.
H2: Effort expectancy, EE, is positively related to behavioural intention, BI, in the adoption
of e-learning applications by accounting students.
H3: Social influence, SI, is positively related to behavioural intention, BI, in the adoption of
e-learning applications by accounting students.
H4: Facilitating conditions, FC, are positively related to behavioural intention, BI, in the
adoption of e-learning applications by accounting students.
H5: Hedonic motivation, HM, is positively related to behavioural intention, BI, in the adop-
tion of e-learning applications by accounting students.
H6: Price value, PV, is positively related to behavioural intention, BI, in the adoption of e-
learning applications by accounting students.
H7: Habit, H, is positively related to behavioural intention, BI, in the adoption of e-learning
applications by accounting students.
UTAUT2 includes gender, age, and experience as moderating variables. These variables
have been adapted for the online learning environment in this study. Three attributes are
discussed that could influence the effect of the determinants on user acceptance of e-learning
applications, namely gender, academic performance and study year.
South African Journal of Accounting Research 41

The influence of gender on technology acceptance has received attention from
researchers over the years (Alfadda & Mahdi, 2021; Gefen & Straub, 1997; Ilie et al.,
2005; Papageorgiou & Callaghan, 2018; Sun & Zhang, 2006; Venkatesh & Morris,
2000). However, studies including gender as a moderator of technology acceptance have
found varying results. Early in the technology acceptance literature, Gefen and Straub
(1997) found that women and men differed in their perceptions but not in their use of
email. Venkatesh and Morris (2000) found that the decision to use technology was more
strongly influenced by perceived usefulness for men and ease of use for women. More
recently, Alfadda and Mahdi (2021) found no correlation between gender and any of the
variables that influence students’use of technology.
Academic performance is based on students’marks achieved during the academic year
for tests, assignments, projects, tutorial tests, and final exams (Baard et al., 2010; Steen-
kamp et al., 2009). Prior studies have indicated that there are several factors that influence
the academic performance of students, such as students’age, gender, attitude, extended
curriculum, personality, diversity, transition from secondary to tertiary education, and
prior academic achievement (Baard et al., 2010; Du Plessis et al., 2005; Lubbe &
Coetzee, 2018; Müller et al., 2007; Papageorgiou, 2017,2019; Papageorgiou & Carpenter,
2019; Steenkamp et al., 2009).
The question whether academic performance influences students’intention to
engage with e-learning applications has not previously been addressed by the
UTAUT2 model. Students’self-esteem and self-efficacy, that is, belief in their own
ability to behave in a manner necessary to perform academically, are important in pre-
dicting students’interaction behaviour and their academic performance (Terblanche
et al., 2021). In an online learning environment, students are required to interact
with lecturers and peers by using e-learning applications. If higher self-efficacy in
using e-learning applications leads to better academic performance, we would expect
academic performance to have a moderating effect on behavioural intention to use e-
learning applications.
In this study the students’year of study was used as a proxy for age and experience in
using e-learning technologies. Venkatesh et al. (2003) found a stronger effect of perform-
ance expectancy on behavioural intention for younger employees, while effort expectancy
and social influence were found to have a stronger effect on behavioural intention for both
older employees and less experienced employees. Venkatesh et al. (2012) found that age
and experience moderated the relationship of price value, hedonic motivation, and habit
on behavioural intention.
Since then, Khechine et al. (2014), Lakhal et al. (2021), Lu et al. (2009), Nikolopoulou
et al. (2020), and Sitar-Taut and Mican (2021) have included age and experience as mod-
erating variables in their studies, with varying results. Khechine et al. (2014) found a stron-
ger effect of performance expectancy for younger students and of facilitating conditions for
older students. Nikolopoulou et al. (2020) found no moderating effect of age or experience
on students’acceptance of mobile phones for study purposes, while Sitar-Taut and Mican
(2021) found that the effect of performance expectancy on behavioural intention to use
mobile learning was stronger for older students.
A number of studies in higher education have not included age and experience as
moderating variables (Arain et al., 2019; Azizi et al., 2020; Moorthy et al., 2019;
Raman & Thannimalai, 2021; Yang, 2013). In the current study, age was not directly
included because, similar to Moorthy et al. (2019), the age of students enrolled in
these programmes has a narrow range. Students in the final year of their undergraduate
qualification or in postgraduate studies are likely to have more experience of using e-
42 W. Terblanche et al.

learning applications than first- or second-year students. All four universities in this
study utilised e-learning applications to some extent prior to 2020; however, this has
increased significantly in 2020 due to the COVID-19 pandemic. Thus, students who
enrolled in university for the first time in 2020, or who had spent one year at university
in 2019, had no experience, or at most one year of experience, in using e-learning appli-
cations in a higher education environment. As indicated above, students’experience of
using e-learning applications is likely to moderate the relationship of the determinants,
particularly performance and effort expectancy, and habit, with behavioural intention to
use.
Three hypotheses were added to this study for the moderating variables:
H8: The relationship between the determinants and behavioural intention, BI, is significantly
moderated by the gender, G, of accounting students.
H9: The relationship between the determinants and behavioural intention, BI, is signifi-
cantly moderated by the academic performance, AP, of accounting students.
H10: The relationship between the determinants and behavioural intention, BI, is signifi-
cantly moderated by the study year, SY, of accounting students.
These 10 hypotheses informed the research model for this study, as illustrated in Figure 1.
The construct variables are Performance Expectancy (PE), Effort Expectancy (EE), Social
Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Price Value (PV),
Habit (H), and Behavioural Intention (BI). Gender (G), Academic Performance (AP) and
Study Year (SY) are the moderating variables.
Figure 1. Research model.
Adapted source: Ameri et al. (2020); Ramírez-Correa et al. (2019)
(F = formative construct; i = items in the questionnaire)
South African Journal of Accounting Research 43

3. Method and data collection
This was a quantitative study using both descriptive and inferential statistics and employ-
ing a deductive approach (Bryman & Bell, 2012; Gabriel, 2013; Pandey, 2019) to test the
hypotheses and answer the research questions. Data were collected from four South
African universities during the COVID-19 lockdown period. The study applied the
UTAUT2 model as established by Venkatesh at al. (2012). The UTAUT questionnaire
was developed in previous research to describe and analyse the prime theoretical underpin-
nings of technology acceptance models. Further research tested the robustness of the
UTAUT questionnaire and added three consumer-specific determinants to the UTAUT2
questionnaire. For this study the UTAUT2 questionnaire was adapted for the higher edu-
cation environment to examine the relevance of the various determinants to accounting stu-
dents’acceptance of e-learning applications in an online learning environment.
3.1. Research instrument
The UTAUT2 model (Venkatesh et al., 2012) was found to be relevant to this study as the
questions have wide application and are easily adaptable for the specific context. The tai-
lored questionnaire consisted of two sections, the first covering student demographics and
attributes, and the second, consisting of 28 questions, addressing the eight constructs: PE
(four questions), EE (four questions), SI (three questions), FC (four questions), HM (three
questions), PV (three questions), H (four questions) and BI (three questions) (see Appen-
dix 1). These constructs were measured using a five-point Likert scale, with the options
‘Strongly disagree’,‘Disagree’,‘Neutral’,‘Agree’and ‘Strongly Agree’.
3.2. Pilot study
The adapted questionnaire was first tested in a pilot study with a selection of participants at
the university responsible for the pilot study. The reliability of constructs in the adapted
questionnaire was tested using Cronbach’s alpha coefficient. All eight constructs were
found to be reliable (alpha ≥0.7) except for Habit (H) (alpha = 0.442). The H construct
was meticulously reviewed, and questions with poor factor loading were appropriately
revised. The results of the pilot study demonstrated the validity of the constructs within
this context.
3.3. Data collection
Accounting students from four traditional universities in South Africa were purposively
selected to inform this study. Currently, there are 26 public universities in South Africa,
of which 12 universities are classified as “traditional”, offering theoretically oriented uni-
versity degrees. The eight universities of technology offer vocational diplomas and
degrees, while the remaining six comprehensive universities offer a combination of both
types of qualifications (Universities South Africa, 2021).
Purposive sampling is a non-probability sampling method used when “elements
selected for the sample are chosen by the judgment of the researcher”(Black, 2019,
p. 215). The purposive selection of these four universities provided a representative
sample of the public residential universities in South Africa that offer accounting pro-
grammes for students studying towards becoming a professional accountant. Distance uni-
versities were not considered as relevant to this study, as the study focused mainly on
44 W. Terblanche et al.

students enrolled for residential studies who had suddenly been required to move to an
online learning environment. Issues essential to the purpose of the inquiry (Patton,
2002) included their mode of teaching, location, size, and resources.
The four universities selected for this study are classified as traditional universities and
offer theoretically oriented accounting degrees. No universities of technology were
selected, as degrees offered by these universities are of a more practical nature and
include work-integrated learning. The focus of this study was on accounting students
studying towards a professional accountancy qualification. Three of the four universities
selected enjoy financial stability and have access to established resources. The rural uni-
versity in the Eastern Cape has strong ties with the local community, but has a much
weaker financial health status, resulting in limited resources. Participants were students
at these four South African universities, studying towards an accounting degree (popu-
lation n= 10 235). Accounting graduates of the participating universities usually
proceed towards the professional accounting qualification. The participants included
both undergraduate and postgraduate students.
The data were collected using the adapted UTAUT2 questionnaire as administered in
previous studies (Ain et al., 2016; Ameri et al., 2020; Venkatesh et al., 2012; Yang, 2013).
Ethical clearance was obtained from the relevant structures of each of the participating uni-
versities. The students were informed of the objective of the study and asked to consent to
participate in the study before being invited to complete the online questionnaire. The
replies resulted in 1 864 usable responses and an overall response rate of 18%.
1
Students were asked to complete the questionnaire relating to their experiences of e-
learning during the time when the universities moved to emergency remote teaching. Stu-
dents were briefed in the introduction to the questionnaire regarding the definition of key
terms. ‘E-learning’was defined as a form of online learning conducted via electronic
media, typically on the internet (Maatuk et al., 2022; Riahi, 2015) and ‘E-learning appli-
cation’was defined as an integrated set of interactive online services that provide learners
in education with information, tools, and resources to support and enhance education deliv-
ery and management (Kumar Basak et al., 2018). The e-learning applications were speci-
fied to include each institution’s learning management systems, WhatsApp text or voice
messaging, email, websites, and chatrooms such as Microsoft Teams, Skype, Zoom,
Google Meet, and Blackboard Collaborate.
3.4. Description of the sample
Table 2 illustrates the descriptive statistics of the data collected from the sample (1 864)
which was larger than those in most previous studies. Black (2019), Hair et al. (2006),
Kline (2011), and Weston and Gore (2006) recommend a sample size of at least 200
respondents for Structural Equation Modelling (SEM) analysis.
The sample was well balanced across the various demographic variables and there-
fore the findings may be generalised to the entire population (Marshall & Rossman,
2014). The sample consisted of 40.0% males and 60.0% females, most of the respondents
(94.4%) were South African, and all races were represented. Exit-level students are
defined as students registered for the final year of an undergraduate degree, an honours
degree, advanced or postgraduate diploma in 2020 (28.1%). Non-exit-level students are
undergraduate students registered in their first or second year of study in 2020 (71.9%).
The majority of the students, 60.9%, indicated an achievement of 60% and above for
accounting, which is often used as a benchmark result for progression in accounting
degrees.
South African Journal of Accounting Research 45

3.5. Analysis
A SEM approach using the Partial Least Squares (PLS) method was used to test the pro-
posed research model (see Hair et al. (2011) for an explanation of PLS-SEM). Descriptive
statistics were calculated using IBM SPSS to describe the sample, showing frequency and
percentages for the overall sample. The responses were statistically analysed using
WarpPLS software. The measurement model was assessed for internal reliability, conver-
gent validity, discriminant validity, and the relationship between constructs. The results of
the structural model and hypotheses testing are reported below.
4. Results
4.1. Assessment of the measurement model
To assess the measurement model for internal reliability, convergent validity, discriminant
validity, and the relationship between constructs, various reliability statistics were calcu-
lated, including Cronbach’s alpha (CA), Composite Reliability (CR) and Average
Extracted Variance (AVE). In addition, Variance Inflation Factors (VIFs) were calculated
to examine the presence of multicollinearity among the variables (see Hair et al., 2011).
The CA values suggest internal consistency of the measurement constructs since they
are all above the recommended threshold value of 0.7 and all of the CR values exceed 0.7.
Both measures are above the acceptable values used in previous studies (Hair et al., 1998;
Nunnally & Bernstein, 1978). The AVE values exceed the threshold value of 0.5 (Fornell &
Larcker, 1981). These results indicate that the measurement constructs are reliable, hence
reliable findings could be obtained for the study. Hair et al. (2009) concluded that VIFs
below 5 suggest the absence of multicollinearity. The VIFs in Table 3 meet the rec-
ommended value and the tolerance values are all above 0.2, suggesting that there is no mul-
ticollinearity among the variables.
2
The structure and cross-loadings reported in Table 4 indicate the existence of conver-
gent validity for the measurement constructs as all the cross-loadings exceed 0.6. Items
Table 2. Demographic data of all the participants.
Demographic and moderating variables NFrequency % of sample
Gender
Male 745 40.0
Female 1 119 60.0
Nationality
South African 1 760 94.4
Non-South African 104 5.6
Race
African 992 53.2
Coloured 99 5.3
White 594 31.9
Indian 115 6.2
Prefer not to answer 64 3.4
Study year
Non-exit-level students 1 341 71.9
Exit level students 523 28.1
Academic performance
Low and average performers 729 39.1
High performers 1 135 60.9
46 W. Terblanche et al.

with factor loadings less than 0.6 were eliminated from the model (EE5, H23). It was poss-
ible to get meaningful results using the selected constructs since there was convergent
validity.
Discriminant validity of the constructs was examined through inter-constructs corre-
lations. According to the results displayed in Table 5, discriminant validity exists on all
the measurement items as evidenced by the square root of the AVE values (diagonal
values) for the latent variables which exceed the corresponding correlation coefficient
values of other latent variables. As expected, the constructs are all positively correlated
to BI. Regarding the moderating variables, gender is negatively correlated to the con-
structs (except H), indicating that being male has a stronger correlation with the con-
structs than being female. AP is positively correlated to all the constructs, indicating a
Table 3. Reliability statistics (own source).
PE EE SI FC HM PV H BI
Cronbach Alpha (CA) 0.905 0.830 0.936 0.828 0.931 0.852 0.821 0.894
Composite Reliability (CR) 0.933 0.933 0.959 0.886 0.956 0.911 0.894 0.934
Average Extracted Variance
(AVE)
0.778 0.747 0.887 0.661 0.879 0.773 0.738 0.825
Variance Inflation Factors (VIF) 4.933 4.221 2.659 3.260 3.677 2.400 3.736 4.483
Tolerance 0.234 0.206 0.206 0.285 0.262 0.424 0.221 -
Table 4. Structure and cross loadings.
PE EE SI FC HM PV H BI
PE1 0.89 0.70 0.65 0.58 0.73 0.64 0.76 0.74
PE2 0.86 0.57 0.55 0.47 0.66 0.57 0.62 0.62
PE3 0.90 0.61 0.64 0.53 0.73 0.61 0.68 0.74
PE4 0.89 0.63 0.65 0.51 0.74 0.61 0.68 0.76
EE6 0.62 0.88 0.56 0.70 0.61 0.62 0.64 0.61
EE7 0.61 0.87 0.53 0.65 0.62 0.56 0.61 0.59
EE8 0.62 0.84 0.62 0.71 0.57 0.55 0.63 0.67
SI9 0.66 0.62 0.94 0.53 0.65 0.57 0.63 0.69
SI10 0.65 0.61 0.95 0.52 0.63 0.57 0.62 0.69
SI11 0.68 0.62 0.93 0.55 0.64 0.58 0.63 0.74
FC12 0.49 0.62 0.42 0.80 0.44 0.55 0.50 0.51
FC13 0.45 0.68 0.41 0.84 0.41 0.49 0.52 0.49
FC14 0.55 0.71 0.54 0.85 0.51 0.56 0.59 0.62
FC15 0.44 0.57 0.47 0.76 0.41 0.48 0.47 0.48
HM16 0.74 0.64 0.63 0.49 0.91 0.57 0.67 0.68
HM17 0.75 0.64 0.63 0.50 0.95 0.61 0.70 0.69
HM18 0.79 0.68 0.65 0.54 0.95 0.63 0.73 0.72
PV19 0.51 0.54 0.47 0.51 0.50 0.81 0.50 0.48
PV20 0.66 0.60 0.57 0.58 0.61 0.91 0.57 0.61
PV21 0.64 0.62 0.56 0.60 0.58 0.91 0.55 0.59
H22 0.67 0.65 0.58 0.59 0.66 0.54 0.90 0.69
H24 0.81 0.64 0.68 0.54 0.72 0.62 0.81 0.78
H25 0.54 0.57 0.46 0.52 0.56 0.45 0.87 0.58
BI26 0.79 0.68 0.69 0.58 0.71 0.60 0.74 0.92
BI27 0.65 0.60 0.62 0.59 0.60 0.54 0.68 0.87
BI28 0.77 0.69 0.73 0.59 0.70 0.61 0.74 0.94
South African Journal of Accounting Research 47

Table 5. Correlations among latent variables with square root of AVEs.
PE EE SI FC HM PV H BI G AP SY
PE 0.88 0.71 0.71 0.59 0.81 0.69 0.78 0.81 −0.05 0.16 −0.17
EE 0.71 0.86 0.65 0.80 0.70 0.67 0.72 0.72 −0.03 0.18 −0.02
SI 0.71 0.65 0.94 0.56 0.68 0.61 0.66 0.75 −0.04 0.09 −0.05
FC 0.59 0.80 0.56 0.81 0.54 0.64 0.64 0.65 −0.01 0.15 0.03
HM 0.81 0.70 0.68 0.54 0.94 0.64 0.75 0.74 −0.04 0.11 −0.10
PV 0.69 0.67 0.61 0.64 0.64 0.88 0.62 0.64 −0.05 0.12 −0.08
H0.78 0.72 0.66 0.64 0.75 0.62 0.86 0.80 0.02 0.16 −0.06
BI 0.81 0.72 0.75 0.65 0.74 0.64 0.80 0.91 −0.02 0.12 −0.08
G−0.05 −0.03 −0.04 −0.01 −0.04 −0.05 0.02 −0.02 1.00 0.02 0.01
AP 0.16 0.18 0.09 0.15 0.11 0.12 0.16 0.12 0.02 1.00 −0.26
SY −0.17 −0.02 −0.05 0.03 −0.10 −0.08 −0.06 −0.08 0.01 −0.26 1.00
Note: The bolded diagonal values are the square roots of the AVE values.
48 W. Terblanche et al.

stronger correlation for high-performing students than low and average performers. In
contrast, SY is negatively correlated to most of the constructs (except FC), indicating
that non-exit-level students have a stronger correlation with the constructs than those
at exit level.
Finally, the approximate model fit was calculated using the standardised root mean
square residual (SRMR) (untabulated). The SRMR for the models, 0.060 (model
without considering moderating variables) and 0.054 (model with moderating variables),
is below the threshold of 0.08 (Henseler et al., 2016) and therefore a good fit.
4.2. Structural model analysis and hypothesis testing
PLS software was used in the calculation of the model’s path coefficients and p-values. All
the interactions with p-values of less than 0.10 were considered significant, thus reported at
the 10% confidence level. Two separate models were fitted to test the hypotheses with BI as
the dependent variable. Model 1 tested hypotheses 1 to 7 regarding the influence that these
seven determinants have on BI; model 2 included moderating variables and tested hypoth-
eses 8 to 10. The structural model results are displayed in Figure 2. As noted in Table 6,
78% total variability in BI is explained by model 1. About 84% total variability in BI is
explained by model 2 after considering the effect of the moderating variables. The
results of R
2
suggest a good model as indicated by Hair et al. (2011) and Henseler et al.
(2016).
Table 6 indicates that the basic structure of UTAUT2 was confirmed. In model 1, for
the determinants from the initial UTAUT model (PE, EE, SI and FC), all path coefficients
were positive and statistically significant (p< 0.10). These results persist in model 2. Thus,
hypotheses 1, 2, 3, and 4 are supported.
Of the three additional determinants in UTAUT2 (HM, PV, H) only the relationship
between H and BI (β= 0.278, p< 0.001) is statistically significant in both models. HM,
which is positively related to BI at the 10% level (β= 0.031, p= 0.088) before considering
the moderating variables, no longer has a significant effect when the moderating variables
are added to the model (β=−0.004, p= 0.431). PV does not have a significant relationship
with BI either before (β= 0.010, p= 0.334) or after (β= 0.010, p= 0.34) considering the
moderating variables. Thus, hypothesis 7 (H7) is supported, but H5 and H6 are not
supported.
G had no significant interactions with any of the constructs, apart from a small inter-
acting effect with FC on BI, more strongly for females than for males (β= 0.035, p=
0.068). H8 is, therefore, not supported.
AP had no significant interactions with any of the constructs, apart from a small posi-
tive interacting effect with PV (β= 0.031, p= 0.092) and HM (β= 0.035, p= 0.063) on BI,
more strongly for high performers than for low and average performers. Overall, H9 is not
supported.
SY also had no significant interaction with most of the constructs, and H10 is, there-
fore, not supported. However, the interacting effect of SYon the relationship between PE
(β= 0.03, p= 0.094) and PV (β= 0.03, p= 0.097) on BI is stronger for exit-level students
than for non-exit-level students. The interaction of SY and EE is negative (β=−0.032, p=
0.085), which may warrant further investigation.
Based on the results, by including the effects of the moderating variables, a larger pro-
portion of the variance in BI can be accounted for (R
2
= 0.84). The strength and direction
(positive or negative) of the main path coefficients can be more adequately interpreted after
considering the effects of moderating variables.
South African Journal of Accounting Research 49

Figure 2. Structural model results.
50 W. Terblanche et al.

5. Discussion
By testing the UTAUT2 model in a cross-institutional context with a larger sample size,
using accounting students as participants, the above findings contribute to the literature
on technology acceptance. The consumer-specific determinants of UTAUT2 were not
strongly supported by this study, although there may be mediating reasons, which are dis-
cussed below.
The digital gap between male and female appears to be narrowing (Faqih & Jaradat,
2015; Lee et al., 2015). In line with previous studies, gender had no significant moderating
effect on any of the relationships, and future studies may consider removing this variable.
AP as a moderating variable was not significant. In so far as AP and SY proxy for experi-
ence, experience seemed to retain some validity in the model. Consideration should there-
fore be given to how universities assist students in gaining experience with e-learning
applications.
Table 6. Structural model path coefficients.
Dependent variable
–BI
Direct effects
(Model 1)
Direct effects and
moderating variables
(Model 2)
Hypothesis supported?
(Yes/No) Coefficient
P-
values Coefficient
P-
values
R
2
0.78 0.84
Adj. R
2
0.78 0.84
PE H1 = Y 0.326 <0.001* 0.338 <0.001*
EE H2 = Y 0.036 0.061* 0.039 0.045*
SI H3 = Y 0.236 <0.001* 0.246 <0.001*
FC H4 = Y 0.096 <0.001* 0.080 <0.001*
HM H5 = N 0.031 0.088* −0.004 0.431
PV H6 = N 0.010 0.334 0.010 0.340
HH7 = Y 0.278 <0.001* 0.288 <0.001*
G*PE H8 = N 0.013 0.288
G*EE H8 = N 0.028 0.113
G*SI H8 = N 0.020 0.194
G*FC H8 = N 0.035 0.068*
G*HM H8 = N −0.020 0.191
G*PV H8 = N 0.010 0.334
G*H H8 = N 0.003 0.444
AP*PE H9 = N −0.002 0.473
AP*EE H9 = N 0.011 0.311
AP*SI H9 = N −0.017 0.235
AP*FC H9 = N 0.029 0.103
AP*HM H9 = N 0.035 0.063*
AP*PV H9 = N 0.031 0.092*
AP*H H9 = N −0.009 0.352
SY*PE H10 = N 0.030 0.094*
SY*EE H10 = N −0.032 0.085*
SY*SI H10 = N 0.014 0.280
SY*FC H10 = N −0.028 0.111
SY*HM H10 = N −0.002 0.462
SY*PV H10 = N 0.030 0.097*
SY*H H10 = N −0.009 0.347
*Significant at the 10% confidence level (p < 0.10)
South African Journal of Accounting Research 51

For blended or online learning models to succeed in higher education institutions all
students need to engage with online learning technologies. Understanding students’behav-
ioural intention to use e-learning applications and the factors that influence this intention
will be crucial towards achieving inclusive and successful learning models. Students who
participated in this study were mostly neutral or positive about their intention to use e-
learning applications, with 14% to 25% of students responding negatively to the questions
about their intention. Further investigation to understand these negative responses should
bear fruit towards an inclusive approach to online learning. Furthermore, after COVID-19,
universities need to revert to a traditional face-to-face environment again with minimal e-
learning technologies, to online learning, or to a blended learning approach –a decision
that will evolve in time once the pandemic is under control.
Students were generally positive about the ease of use of e-learning applications, the
clarity and understandability of their interaction with e-learning applications and the
ease with which they can become skilful in using e-learning applications. On average, stu-
dents agreed with all the statements regarding the availability of facilitating resources and
having the knowledge and assistance necessary to utilise e-learning applications. The
majority of students rated the PE questions as neutral to agree (61% to 75%), the EE ques-
tions as agree to strongly agree (52% to 64%), the SI questions as neutral to agree (56% to
59%), and the FC questions as agree to strongly agree (61% to 73%).
3
The reason for the
number of students who were negative regarding their chances of achieving higher marks
(34%) and being more productive (39%) may require further investigation. Reasons for
students’perceiving a negative social influence (27% to 31%) could also be of interest
for further studies.
Students were neutral to slightly negative regarding the fun and enjoyment of learning
using e-learning applications. On the other hand, students were neutral to somewhat posi-
tive regarding the price value of e-learning applications and generally responded that they
had developed a habit of using e-learning applications. The majority of students rated the
HM questions as disagree to neutral (48% to 57%), the PV questions as neutral to agree
(57% to 59%), and the H questions as agree to strongly agree (4.3% to 71%).
Whitton and Langan (2019) reported that students perceive learning as fun when it
stimulates engagement and is a shared experience in a safe learning space and low-
stress environment. These elements might not have been present during the emergency
remote teaching period and should be considered in developing blended and online learn-
ing models.
The four universities’provision of laptops and data to the students, as well as
arrangements made to accommodate students whose home environment was not condu-
cive to study during the COVID-19 lockdown period, may explain the relatively low
number of students who responded negatively regarding facilitating conditions (8% to
17%). This may also explain why students were generally neutral or agreed with the
price value of e-learning applications. The implications of providing these resources
to students in future to support blended or online learning will also need to be
considered.
6. Conclusion
Emergency remote teaching, which was necessitated by the worldwide COVID-19 out-
break, has changed the higher education environment and the way teaching and learning
take place. The “new normal”of online learning illuminated students’challenges in enga-
ging with online learning. The adoption and embracing of these technologies by students
52 W. Terblanche et al.

are fundamental for the future of higher education which is likely to continue moving
towards blended or online learning models, rendering the current study of crucial
importance.
The purpose of the study was to explore the variables that influence accounting
students’perceptions in accepting e-learning applications at residential universities.
The UTAUT2 model was adapted, and a tailored questionnaire was administered to
examine the relevance of the various determinants to establish students’intent to
engage with e-learning applications. Students at four purposively selected universities
in South Africa participated in this study, representing students at residential univer-
sities studying for an accounting degree which allows for further qualification
towards becoming a professional accountant. Participating students broadly rep-
resented South Africa’s diverse cultural and economic backgrounds. This enhanced
the uniqueness of this study.
The research questions focused on establishing the determinants (constructs) of user
acceptance of e-learning applications in an online learning environment and the moderat-
ing factors that have an effect on these determinants. Determinants were identified that
predict intention and behaviour of accounting students in an e-learning environment.
The findings demonstrated that variables such as gender, academic performance, and
study year (experience) generally have no practically significant moderating effect on
these determinants. PE, SI, FC and H indicated the most significant relationship with
BI. It is therefore recommended that lecturers pay specific attention to these determinants
when designing an online learning environment.
The value of the study lies in the fact that it indicates the relationships between various
technology acceptance determinants and students’behavioural intention to use these tech-
nologies to learn. The paper contributes to technology acceptance research by reporting on
the testing of the UTAUT2 model in a cross-institutional context with a larger sample size.
The larger sample size and the purposive selection of universities may make the findings
more generalisable than previous studies to the higher education environment in South
Africa.
The study further provides a deeper understanding of the moderating factors that influ-
ence online learning, which will, in turn, assist with the design of blended or online learn-
ing environments. It has practical value for higher education policymakers, institutions,
and lecturers in adopting blended and online learning models in the future. This contri-
bution will remain significant even if the world enters a post-COVID-19 era, as more
and more universities are adopting hybrid or blended teaching and learning strategies in
a 4IR world.
6.1. Limitations
One limitation of the paper is that the study was performed at a time when higher edu-
cation was forced to adopt emergency remote teaching. Further, the study was con-
ducted during a single academic year and the response rate is relatively low (18%).
Repeating this study in the near future, when students are more familiar with e-learning
and/or at a larger sample of institutions, is recommended. In addition, students enrolled
for traditional face-to-face learning at residential universities had been forced to learn
online during COVID-19, which could have had an effect on their acceptance of the
technologies. However, the insights obtained from this study emphasise the challenges
associated with e-learning in higher education, irrespective of the discipline and location
of the participants.
South African Journal of Accounting Research 53

6.2. Recommendations for further research
Recommendations for future research include a longitudinal study of accounting students
being exposed to e-learning for more than one year and linking this with actual usage.
Further investigation of the reasons why accounting students responded positively or nega-
tively to various questions to inform practical implementation of blended or online learn-
ing models should be highly beneficial.
Acknowledgements
The authors are grateful to the participating students, the statistician, Mr Tendai Makoni for the
analysis of the data, and Prof. Gillian Bartlett for setting up the universities under review.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or
not-for-profit sectors.
Disclosure statement
The authors report there are no competing interests to declare.
Notes
1. Although the number of respondents is high (n= 1 864) the response rate is low (18%). Khe-
chine et al. (2014) and Leow et al. (2021) report similarly low response rates with lower
numbers of respondents, and report this as a limitation of their studies. The target population
consisted of a homogenous group, being accounting students at SA universities, reducing the
risk of significant non-response bias.
2. The untabulated eigenvalues are not close to 0 (all above 0.01) and the condition index values
are all below the threshold value of 15, further indicating that there is no multicollinearity.
3. The 28 individual questions addressing the eight constructs and the related distribution of
answers on a five-point Likert scale are included as Appendix 1.
Availability of data and materials
The data for each participating institution are owned by that institution and cannot be
shared without permission of that institution.
ORCID iDs
Wendy Terblanche http://orcid.org/0000-0002-0011-6228
Ilse Lubbe http://orcid.org/0000-0002-3603-4718
Elmarie Papageorgiou http://orcid.org/0000-0001-9356-6123
Nico van der Merwe http://orcid.org/0000-0003-4237-5495
References
Agyei, C., & Razi, Ö. (2021). The effect of extended UTAUT model on EFLs’adaptation to flipped
classroom. Education and Information Technologies, 27, 1865–1882. https://doi.org/10.1007/
s10639-021-10657-2
54 W. Terblanche et al.

Ain, N. U., Kaur, K., & Waheed, M. (2016). The influence of learning value on learning management
system use: An extension of UTAUT2. Information Development, 32(5), 1306–1321. https://doi.
org/10.1177/0266666915597546
Al-Emran, M., & Granić,A.(2021). Is it still valid or outdated? A bibliometric analysis of the tech-
nology acceptance model and its applications from 2010 to 2020. In M. Al-Emran & K. Shaalan
(Eds.), Recent advances in technology acceptance models and theories (pp. 1–12). Springer.
https://doi.org/10.1007/978-3-030-64987-6_1
Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018). Technology acceptance model in M-learning
context: A systematic review. Computers & Education, 125, 389–412. https://doi.org/10.1016/j.
compedu.2018.06.008
Alfadda, H. A., & Mahdi, H. S. (2021). Measuring students’use of Zoom application in language
course based on the technology acceptance model (TAM). Journal of Psycholinguistic
Research, 50, 883–900. https://doi.org/10.1007/s10936-020-09752-1 PMID:33398606
Altalhi, M. (2021). Toward a model for acceptance of MOOCs in higher education: The modified
UTAUT model for Saudi Arabia. Education and Information Technologies, 26(2), 1589–1605.
https://doi.org/10.1007/s10639-020-10317-x
Ameri, A., Khajouei, R., Ameri, A., & Jahani, Y. (2020). Acceptance of a mobile-based educational
application (LabSafety) by pharmacy students: An application of the UTAUT2model. Education
and Information Technologies, 25(1), 419–435. https://doi.org/10.1007/s10639-019-09965-5
Anderson, V. (2020). A digital pedagogy pivot: Re-thinking higher education practice from an HRD
perspective. Human Resource Development International, 23(4), 452–467. https://doi.org/10.
1080/13678868.2020.1778999
Arain, A. A., Hussain, Z., Rizvi, W. H., & Vighio, M. S. (2019). Extending UTAUT2 toward accep-
tance of mobile learning in the context of higher education. Universal Access in the Information
Society, 18(3), 659–673. https://doi.org/10.1007/s10209-019-00685-8
Attuquayefio, S. N., & Addo, H. (2014). Using the UTAUT model to analyse students’ICT adoption.
International Journal of Education and Development Using Information and Communication
Technology, 10(3), 75–86.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision
Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Azizi, S. M., Roozbahani, N., & Khatony, A. (2020). Factors affecting the acceptance of blended
learning in medical education: Application of UTAUT2 model. BMC Medical Education, 20,
Article 367. https://doi.org/10.1186/s12909-020-02302-2
Baard, R. S., Steenkamp, L. P., Frick, B. L., & Kidd, M. (2010). Factors influencing success in first-
year accounting at a South African university: The profile of a successful first-year accounting
student. South African Journal of Accounting Research, 24(1), 129–147. https://doi.org/10.1080/
10291954.2010.11435150
Bergdahl, N., Fors, U., Hernwall, P., & Knutsson, O. (2018). The use of learning technologies and
student engagement in learning activities. Nordic Journal of Digital Literacy, 13(2), 113–130.
https://doi.org/10.18261/issn.1891-943x-2018-02-04
Black, K. (2019). Business statistics: Contemporary decision making (10th ed.). Hoboken: John
Wiley & Sons.
Bond, M., Marín, V. I., Dolch, C., Bedenlier, S., & Zawacki-Richter, O. (2018). Digital transform-
ation in German higher education: Student and teacher perceptions and usage of digital media.
International Journal of Educational Technology in Higher Education, 15, Article 48. https://
doi.org/10.1186/s41239-018-0130-1
Brown, C. A., Danvers, K., & Doran, D. T. (2016). Student perceptions on using guided reading
questions to motivate student reading in the flipped classroom. Accounting Education, 25(3),
256–271. https://doi.org/10.1080/09639284.2016.1165124
Bryman, A., & Bell, E. (2012). Business research methods (3rd ed.). Oxford: Oxford University
Press.
Camilleri, M. A., & Camilleri, A. C. (2022). Learning from anywhere, anytime: Utilitarian motiv-
ations and facilitating conditions for mobile learning. Technology, Knowledge and Learning.
Advance online publication. https://doi.org/10.1007/s10758-022-09608-8
Carcavallo, A. S. (2020). Accountancy higher education: Evolution in digital technologies adoption.
In P. Kommers, A. B. N. Viana, T. Issa, & Pedro Isaías (Eds.), Proceedings of the 7
th
International
Conference on Educational Technologies (pp. 122–124). São Paulo: IADIS Press.
South African Journal of Accounting Research 55

Chen, B., Bastedo, K., & Howard, W. (2018). Exploring design elements for online STEM courses:
Active learning, engagement & assessment design. Online Learning Journal, 22(2), 59–76.
https://doi.org/10.24059/olj.v22i2.1369
Cranfield, D. J., Tick, A., Venter, I. M., Blignaut, R. J., & Renaud, K. (2021). Higher education stu-
dents’perceptions of online learning during COVID-19 –A comparative study. Education
Sciences, 11(8), 403. https://doi.org/10.3390/educsci11080403
Cummings, C., Mason, D., Shelton, K., & Baur, K. (2015). Active learning strategies for online and
blended learning environments. In J. Keengwe & J. J. Agamba (Eds.), Models for improving and
optimizing online and blended learning in higher education (pp. 58–82). IGI Global. https://doi.
org/10.4018/978-1-4666-6280-3.ch004
Czerniewicz, L., Agherdien, N., Badenhorst, J., Belluigi, D., Chambers, T., Chili, M., De Villiers, M.,
Felix, A., Gachago, D., Gokhale, C., Ivala, E., Kramm, N., Madiba, M., Mistri, G., Mgqwashu,
E., Pallitt, N., Prinsloo, P., Solomon, K., Strydom, S., …Wissing, G. (2020). A wake-up call:
Equity, inequality and Covid-19 emergency remote teaching and learning. Postdigital Science
and Education, 2(3), 946–967. https://doi.org/10.1007/s42438-020-00187-4
Dexter, S., Clement, D., Moraguez, D., & Watson, G. S. (2020). (Inter)Active learning tools and ped-
agogical strategies in educational leadership preparation. Journal of Research on Leadership
Education, 15(3), 173–191. https://doi.org/10.1177/1942775120936299
Du Plessis, A., Müller, H., & Prinsloo, P. (2005). Determining the profile of the successful first-year
accounting student. South African Journal of Higher Education, 19(4), 684–698.
Faqih, K. M., & Jaradat, M. I. R. M. (2015). Assessing the moderating effect of gender differences
and individualism-collectivism at individual-level on the adoption of mobile commerce technol-
ogy: TAM3 perspective. Journal of Retailing and Consumer Services, 22,37–52. https://doi.org/
10.1016/j.jretconser.2014.09.006
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable vari-
ables and measurement error. JMR, Journal of Marketing Research, 18(1), 39–50. https://doi.
org/10.1177/002224378101800104
Gabriel, D. (2013). Inductive and deductive approaches to research. Accessed at https://
deborahgabriel.com/2013/03/17/inductive-and-deductive-approaches-to-research/
Gefen, D., & Straub, D. W. (1997). Gender differences in the perception and use of e-mail: An exten-
sion to the technology acceptance model. Management Information Systems Quarterly, 21, 389–
400. https://doi.org/10.2307/249720
Graham, C. R., Woodfield, W., & Harrison, J. B. (2013). A framework for institutional adoption and
implementation of blended learning in higher education. The Internet and Higher Education, 18,
4–14. https://doi.org/10.1016/j.iheduc.2012.09.003
Granić, A., & Marangunić,N.(2019). Technology acceptance model in educational context: A sys-
tematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://
doi.org/10.1111/bjet.12864
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th
ed.). Prentice Hall.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). SEM: Confirmatory
factor analysis. In J. F. Hair, W. C. Black, B. J. Babin, R. E. Anderson, & R. L. Tatham (Eds.),
Multivariate data analysis (pp. 770–842). London: Pearson Education.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data analysis. Upper
Saddle, NJ: Prentice Hall.
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
Henderson, M., Selwyn, N., & Aston, R. (2017). What works and why? Student perceptions of
‘useful’digital technology in university teaching and learning. Studies in Higher Education,
42(8), 1567–1579. https://doi.org/10.1080/03075079.2015.1007946
Henseler, J., Hubona, G., & Ray, P. A.(2016). Using PLS path modeling in new technology research:
Updated guidelines. Industrial Management & Data Systems, 116,2–20. Advance online publi-
cation. https://doi.org/10.1108/IMDS-09-2015-0382
Hwang, Y., Al-Arabiat, M., & Shin, D. H. (2016). Understanding technology acceptance in a man-
datory environment: A literature review. Information Development, 32(4), 1266–1283. https://
doi.org/10.1177/0266666915593621
56 W. Terblanche et al.

Ilie, V., Van Slyke, C., Green, G., & Lou, H. (2005). Gender differences in perceptions and use of
communication technologies: A diffusion of innovation approach. Information Resources
Management Journal, 18(3), 13–31. https://doi.org/10.4018/irmj.2005070102
International Association of Universities. (2020). COVID-19: Higher Education challenges and
responses.https://www.iau-aiu.net/COVID-19-Higher-Education-challenges-and-responses
Kabilan, M. K., Ahmad, N., & Abidin, M. J. Z. (2010). Facebook: An online environment for learn-
ing of English in institutions of higher education? The Internet and Higher Education, 13(4),
179–187. https://doi.org/10.1016/j.iheduc.2010.07.003
Khan, A., Egbue, O., Palkie, B., & Madden, J. (2017). Active learning: Engaging students to maxi-
mize learning in an online course. Electronic Journal of e-Learning, 15(2), 107–115.
Khechine, H., Lakhal, S., Pascot, D., & Bytha, A. (2014). UTAUT model for blended learning: The
role of gender and age in the intention to use webinars. Interdisciplinary Journal of e-Skills and
Lifelong Learning,10,33–52.
Koehler, M. J., Mishra, P., Kereluik, K., Shin, T. S., & Graham, C. R. (2014). The technological ped-
agogical content knowledge framework. In J. M. Spector, M. D. Merrill, J. Elen, & M. J. Bishop
(Eds.), Handbook of research on educational communications and technology (pp. 101–111).
Springer. https://doi.org/10.1007/978-1-4614-3185-5_9
Kline, R. B. (2011). Convergence of structural equation modeling and multilevel modeling. In M.
Williams & W. P. Vogt (Eds.), The SAGE handbook of innovation in social research methods
(pp. 562–589). Sage., https://doi.org/10.4135/9781446268261.n31
Krasodomska, J., & Godawska, J. (2021). E-learning in accounting education: The influence of stu-
dents’characteristics on their engagement and performance. Accounting Education, 30(1), 22–
41. https://doi.org/10.1080/09639284.2020.1867874
Kumar Basak, S., Wotto, M., & Belanger, P. (2018). E-learning, M-learning and D-learning:
Conceptual definition and comparative analysis. E-Learning and Digital Media, 15(4), 191–
216. https://doi.org/10.1177/2042753018785180
Lakhal, S., Khechine, H., & Mukamurera, J. (2021). Explaining persistence in online courses in
higher education: A difference-in-differences analysis. International Journal of Educational
Technology in Higher Education, 18, Article 19. https://doi.org/10.1186/s41239-021-00251-4
Lee, H., Park, N., & Hwang, Y. (2015). A new dimension of the digital divide: Exploring the relation-
ship between broadband connection, smartphone use and communication competence.
Telematics and Informatics, 32(1), 45–56. https://doi.org/10.1016/j.tele.2014.02.001
Lento, C. (2016). Promoting active learning in introductory financial accounting through the flipped
classroom design. Journal of Applied Research in Higher Education, 8(1), 72–87. https://doi.
org/10.1108/JARHE-01-2015-0005
Leow, L. P., Phua, L. K., & Teh, S. Y. (2021). Extending the social influence factor: Behavioural
intention to increase the usage of information and communication technology-enhanced
student-centered teaching methods. Educational Technology Research and Development, 69,
1853–1879. https://doi.org/10.1007/s11423-021-10017-4
Lu, J., Yu, C., & Liu, C. (2009). Mobile data service demographics in urban China. Journal of
Computer Information Systems, 50(2), 117–126.
Lubbe, I., & Coetzee, S. (2018). Accounting education in Africa. Accounting Education, 27(5), 453–
455. https://doi.org/10.1080/09639284.2018.1514789
Maatuk, A. M., Elberkawi, E. K., Aljawarneh, S., Rashaideh, H., & Alharbi, H. (2022). The COVID-
19 pandemic and E-learning: Challenges and opportunities from the perspective of students and
instructors. Journal of Computing in Higher Education, 34(1), 21–38. https://doi.org/10.1007/
s12528-021-09274-2 PMID:33967563
Maiorana, V. P. (1991). The road from rote to critical thinking. Communication Review, 11,53–64.
Marinoni, G., Van’t Land, H., & Jensen, T. (2020). The impact of Covid-19 on higher education
around the world: IAU Global Survey Report. Paris: International Association of Universities.
Marshall, C., & Rossman, G. B. (2014). Designing qualitative research. Atlanta: Sage.
Moorthy, K., Yee, T. T., T’ing, L. C., & Kumaran, V. V. (2019). Habit and hedonic motivation are the
strongest influences in mobile learning behaviours among higher education students in Malaysia.
Australasian Journal of Educational Technology, 35(4). https://doi.org/10.14742/ajet.4432
Mtshali, N. (2020, August 8). How Covid-19 could change the higher education sector permanently.
Independent Online. https://www.iol.co.za/news/opinion/how-covid-19-could-change-the-
higher-education-sector-permanently-438ed3fc-617c-43ab-8fda-d962ee009266
South African Journal of Accounting Research 57

Müller, H., Prinsloo, P., & Du Plessis, A. (2007). Validating the profile of a successful first year
accounting student. Meditari Accountancy Research, 15(1), 19–33. https://doi.org/10.1108/
10222529200700002
Ng’ambi, D., Brown, C., Bozalek, V., Gachago, D., & Wood, D. (2016). Technology enhanced teach-
ing and learning in South African higher education –A rearview of a 20 year journey. British
Journal of Educational Technology, 47(5), 843–858. https://doi.org/10.1111/bjet.12485
Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2020). Acceptance of mobile phone by university
students for their studies: An investigation applying UTAUT2 model. Education and
Information Technologies, 25, 4139–4155. https://doi.org/10.1007/s10639-020-10157-9
Nunnally, J. C., & Bernstein, I. H. (1978). Psychometric theory. McGraw-Hill.
Ohei, K. N., & Brink, R. (2020). The use of instructional ICT web technologies in higher education
systems to support students’academic success. Mousaion, 37(2), 1–27. https://doi.org/10.25159/
2663-659X/5975
Oliveira, W., Hamari, J., Shi, L., Toda, A., Rodrigues, L., Palomino, P., & Isotani, S. (2022). Tailored
gamification in education: A literature review and future agenda. Education and Information
Technologies. Advance online publication. https://doi.org/10.1007/s10639-022-11122-4
Oye, N. D., Iahad, N. A., & Rahim, N. A. (2014). The history of UTAUT model and its impact on
ICT acceptance and usage by academicians. Education and Information Technologies, 19(1),
251–270. https://doi.org/10.1007/s10639-012-9189-9
Pandey, J. (2019). Deductive approach to content analysis. In M. Gupta, M. Shaheen, & K. P. Reddy
(Eds.), Qualitative techniques for workplace data analysis (pp. 145–169). IGI Global. https://doi.
org/10.4018/978-1-5225-5366-3.ch007
Papageorgiou, E. (2017). Accounting students’profile versus academic performance: A five-year
analysis. South African Journal of Higher Education, 31(3), 209–229. https://doi.org/10.
20853/31-3-1064
Papageorgiou, E. (2019). Lecture attendance versus academic performance and prior knowledge of
accounting students: An exploratory study at a South African university. South African Journal
of Higher Education, 33(1), 262–282. https://doi.org/10.20853/33-1-2695
Papageorgiou, E., & Callaghan, C. W. (2018). Parental style and gendered student performance in the
South African context of accounting studies. Accounting Education, 27(5), 456–478. https://doi.
org/10.1080/09639284.2017.1340167
Papageorgiou, E., & Carpenter, R. (2019). Prior accounting knowledge of first-year students at two
South African universities: Contributing factor to academic performance or not? South African
Journal of Higher Education, 33(6), 249–264. https://doi.org/10.20853/33-6-3032
Patton, M. Q. (2002). Qualitative research and evaluation methods (3rd ed.). Sage.
Raman, A., & Thannimalai, R. (2021). Factors impacting the behavioural intention to use e-learning
at higher education amid the Covid-19 pandemic: UTAUT2 model. Psychological Science and
Education, 26(3), 82–93. https://doi.org/10.17759/pse.2021260305
Ramírez-Correa, P., Rondán-Cataluña, F. J., Arenas-Gaitán, J., & Martín-Velicia, F. (2019).
Analysing the acceptation of online games in mobile devices: An application of UTAUT2.
Journal of Retailing and Consumer Services, 50,85–93. https://doi.org/10.1016/j.jretconser.
2019.04.018
Riahi, G. (2015). E-learning systems based on cloud computing: A review. Procedia Computer
Science, 62, 352–359. https://doi.org/10.1016/j.procs.2015.08.415
Salloum, S. A., Alhamad, A. Q. M., Al-Emran, M., Monem, A. A., & Shaalan, K. (2019). Exploring
students’acceptance of e-learning through the development of a comprehensive technology
acceptance model. IEEE Access: Practical Innovations, Open Solutions, 7, 128445–128462.
https://doi.org/10.1109/ACCESS.2019.2939467
Sava, R. (2016). Using interactive methods in teaching accounting. Studies in Business and
Economics, 11(2), 130–139. https://doi.org/10.1515/sbe-2016-0027
Sheppard, B. H., Hartwick, J., & Warshaw, P. R. (1988). The theory of reasoned action: A meta-
analysis of past research with recommendations for modifications and future research. The
Journal of Consumer Research, 15(3), 325–343. https://doi.org/10.1086/209170
Sitar-Taut, D. A., & Mican, D. (2021). Mobile learning acceptance and use in higher education
during social distancing circumstances: An expansion and customization of UTAUT2. Online
Information Review, 45(5), 1000–1019. https://doi.org/10.1108/OIR-01-2021-0017
South African Government. (2021). South Africa at a glance. https://www.gov.za/about-sa/south-
africa-glance
58 W. Terblanche et al.

Steenkamp, L. P., Baard, R. S., & Frick, B. L. (2009). Factors influencing success in first-year
accounting at a South African university: A comparison between lecturers’assumptions and stu-
dents’perceptions. South African Journal of Accounting Research, 23(1), 113–140. https://doi.
org/10.1080/10291954.2009.11435142
Sun, H., & Zhang, P. (2006). The role of moderating factors in user technology acceptance.
International Journal of Human-Computer Studies, 64(2), 53–78. https://doi.org/10.1016/j.
ijhcs.2005.04.013
Terblanche, W., Fakir, D., Chinyamurindi, W., & Mishi, S. (2021). Impact of self-esteem and student-
and-lecturer interaction on academic performance in a chartered accounting programme. Journal
of Further and Higher Education, 45(4), 464–480. https://doi.org/10.1080/0309877X.2020.
1781801
Universities South Africa. (2021). Public universities in South Africa. https://www.usaf.ac.za/public-
universities-in-south-africa/
Vallerand, R. J. (1997). Toward a hierarchical model of intrinsic and extrinsic motivation. Advances
in Experimental Social Psychology, 29, 271–360. https://doi.org/10.1016/S0065-2601
(08)60019-2
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model:
Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/
mnsc.46.2.186.11926
Venkatesh, V., & Morris, M. G. (2000). Why don’t men ever stop to ask for directions? Gender, social
influence, and their role in technology acceptance and usage behavior. Management Information
Systems Quarterly, 24,115–139. https://doi.org/10.2307/3250981
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information
technology: Toward a unified view. Management Information Systems Quarterly, 27(3), 425–
478. https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information tech-
nology: Extending the unified theory of acceptance and use of technology. Management
Information Systems Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
Vonderwell, S., & Turner, S. (2005). Active learning and preservice teachers’experiences in an
online course: A case study. Journal of Technology and Teacher Education, 13(1), 65–84.
Wang, Y. H. (2020). Design-based research on integrating learning technology tools into higher edu-
cation classes to achieve active learning. Computers & Education, 156, 103935. https://doi.org/
10.1016/j.compedu.2020.103935
Weston, R., & Gore, P. A., Jr. (2006). A brief guide to structural equation modeling. The Counseling
Psychologist, 34(5), 719–751. https://doi.org/10.1177/0011000006286345
Whitton, N., & Langan, M. (2019). Fun and games in higher education: An analysis of UK student
perspectives. Teaching in Higher Education: Critical Perspectives,24(8), 1000–1013. https://
doi.org/10.1080/13562517.2018.1541885
Yang, S. (2013). Understanding undergraduate students’adoption of mobile learning model: A per-
spective of the extended UTAUT2. Journal of Convergence Information Technology, 8(10), 969–
979. https://doi.org/10.4156/jcit.vol8.issue10.118
South African Journal of Accounting Research 59

Appendix 1: Questionnaire
Strongly
disagree Disagree Neutral Agree
Strongly
agree Mean
Std
Dev.
PE1: I find the e-learning
applications are useful for my
learning
220
(12%)
253
(14%)
504
(27%)
538
(29%)
350
(19%)
3.29 1.249
PE2: Using the e-learning
applications increases my
chances of achieving higher
marks
338
(18%)
298
(16%)
493
(26%)
399
(21%)
337
(18%)
3.05 1.348
PE3: Using e-learning applications
assists me to accomplish things
more quickly
292
(16%)
291
(16%)
454
(24%)
473
(25%)
355
(19%)
3.17 1.330
PE4: Using the e-learning
applications increases my
productivity
382
(20%)
350
(19%)
413
(22%)
389
(21%)
331
(18%)
2.97 1.387
EE5: Learning how to use the e-
learning applications is easy for
me
104
(6%)
158
(8%)
398
(21%)
680
(36%)
525
(28%)
3.73 1.125
EE6: My interaction with the e-
learning applications is clear and
understandable
137
(7%)
236
(13%)
523
(28%)
614
(33%)
355
(19%)
3.44 1.150
EE7: I find the e-learning
applications easy to use
136
(7%)
169
(9%)
456
(24%)
659
(35%)
445
(24%)
3.59 1.157
EE8: It is easy for me to become
skilful at using the e-learning
applications
138
(7%)
205
(11%)
442
(24%)
651
(35%)
429
(23%)
3.55 1.172
SI9: People that are important to
me think that I should use e-
learning applications
265
(14%)
304
(16%)
646
(35%)
401
(22%)
249
(13%)
3.03 1.217
SI10: People who influence my
behaviour think that I should use
e-learning applications
259
(14%)
321
(17%)
679
(36%)
373
(20%)
233
(12%)
3.00 1.195
SI11: People whose opinions that I
value, think that I should use e-
learning applications
205
(11%)
306
(16%)
666
(36%)
429
(23%)
259
(14%)
3.12 1.173
FC12: I have the resources
necessary to use e-learning
applications
108
(6%)
173
(9%)
370
(20%)
666
(36%)
548
(29%)
3.74 1.147
FC13: I have the knowledge
necessary to use e-learning
applications
64
(3%)
94
(5%)
347
(19%)
733
(39%)
627
(34%)
3.95 1.015
FC14: e-Learning applications are
compatible with the other
technologies that I use
78
(4%)
126
(7%)
433
(23%)
752
(40%)
476
(26%)
3.76 1.038
FC15: I can get help from others
when I have difficulties using e-
learning applications
105
(6%)
212
(11%)
399
(21%)
694
(37%)
455
(24%)
3.63 1.134
HM16: Using e-learning
applications are fun
338
(18%)
385
(21%)
667
(36%)
265
(14%)
209
(11%)
2.80 1.217
HM17: Using e-learning
applications are enjoyable
300
(16%)
333
(18%)
613
(33%)
364
(20%)
254
(14%)
2.97 1.250
HM18: Using e-learning
applications makes learning
more interesting
363
(19%)
404
(22%)
483
(26%)
348
(19%)
266
(14%)
2.87 1.318
(Continued)
60 W. Terblanche et al.

Appendix 1: Continued.
Strongly
disagree Disagree Neutral Agree
Strongly
agree Mean
Std
Dev.
PV19: Using e-learning
applications is reasonably priced
239
(13%)
310
(17%)
628
(34%)
423
(23%)
265
(14%)
3.09 1.211
PV20: Using e-learning
applications is good value for
money
234
(13%)
288
(15%)
654
(35%)
422
(23%)
267
(14%)
3.11 1.202
PV21: At current pricing e-
learning applications provide
good value
202
(11%)
279
(15%)
631
(34%)
475
(25%)
278
(15%)
3.19 1.183
H22: The use of e-learning
applications is becoming a habit
to me
143
(8%)
196
(11%)
415
(22%)
652
(35%)
458
(25%)
3.58 1.186
H23: I am in favour of using e-
learning applications
272
(15%)
297
(16%)
463
(25%)
442
(24%)
390
(21%)
3.20 1.333
H24: I feel the need to use e-
learning applications
259
(14%)
266
(14%)
539
(29%)
469
(25%)
331
(18%)
3.19 1.275
H25: Using e-learning applications
has become part of my daily life
78
(4%)
128
(7%)
331
(18%)
698
(37%)
629
(34%)
3.90 1.075
BI26: I intend to continue using e-
learning applications in the
future
216
(12%)
239
(13%)
494
(27%)
534
(29%)
381
(20%)
3.34 1.259
BI27: I will always try to use e-
learning applications in my daily
learning
94
(5%)
159
(9%)
515
(28%)
686
(37%)
410
(22%)
3.62 1.072
BI28: I plan to continue to use e-
learning applications frequently
176
(9%)
251
(13%)
503
(27%)
579
(31%)
355
(19%)
3.37 1.204
Source: Adapted from Ramírez-Correa et al. (2019)
South African Journal of Accounting Research 61