Content uploaded by Ghaith Abdulraheem Ali Alsheikh
Author content
All content in this area was uploaded by Ghaith Abdulraheem Ali Alsheikh on Jun 01, 2021
Content may be subject to copyright.
Content uploaded by Ghaith Abdulraheem Ali Alsheikh
Author content
All content in this area was uploaded by Ghaith Abdulraheem Ali Alsheikh on Jun 01, 2021
Content may be subject to copyright.
Unleashing the role of e-learning
in student engagement practices
and accounting
professional competencies
Munther Al-Nimer
The Hashemite University, Zarqa, Jordan, and
Ghaith Alsheikh
Amman Arab University, Amman, Jordan
Abstract
Purpose –Presently, there is a need for graduate students to be well prepared with accounting professional
competencies (APCs) as the market is characterized by intensive activities and rare job opportunities. In
relation to this is the significant role of student engagement (SE) practices and e-learning. Thus, the present
study examined the mediating role of e-learning on the relationship between SE practices and APC, as well as
the moderating role of student’s demographics on the same relationship.
Design/methodology/approach –The study used a structured questionnaire distributed to 428 accounting
students enrolled in institutions in Jordan and the obtained response rate was 65.84%. The formulated
hypotheses were tested using structural equation modeling in PLS-SEM analysis Version 3.2.7.
Findings –On the basis of the results, there is a significant relationship between SE and e-learning and APC,
with e-learning partially mediating the SE-APC relationship. The results also showed that students’
demographics have a significant moderating relationship between the same.
Originality/value –The author recommends that universities employ advanced technologies with SE
practices for the mobilization of accounting graduate students, with the prerequisite APC skills so they will
become competitive and thrive in their professional and practical lives.
Keywords Accounting professional competencies, Student engagement, e-learning
Paper type Research paper
1. Introduction
Student engagement dedicated studies have increased since the 1990s with the works
attributed to Astin (1993),Csikszentmihalyi (1990),Nystrand and Gamoran (1990) and Owsen
(2000). In the face of vigorous global competition, business activities have turned complex
and added to this, technological developments necessitate accountants to be continuously
prepared to tackle challenges (Jaf et al., 2015;Murphy, 2018). In this background, the role of
education is highlighted in terms of providing the market with professionally prepared
accountants, armed with knowledge and accounting professional competencies (APCs) to
cope up and to tackle challenges in business (Low et al., 2008;Rufino et al., 2017;Small, 2018;
Tan and Laswad, 2018). A branch of studies dedicated to the topic.
Other related studies by Junco (2012),Kaciuba (2012),Schmidt et al. (2018),Stone et al.
(2014) and Wyness and Dalton (2018) stressed the importance of student engagement (SE),
with the majority of such studies focused on developed economies as their contexts (Bryson,
2016;Kahu and Nelson, 2018;Maskell and Collins, 2017). In terms of the under-examined
aspect of the topic, the SE in the education system has yet to be extensively examined in
emerging economies. To compound the shortcomings further, empirical findings on the
relationship between SE and e-learning facilities in terms of professional skills and
competencies are still few and far between (Hussain et al., 2018;Kim et al., 2019).
In the field of accounting, APC refers to the ability to illustrate and explain the required
technical and professional skills, values, ethics and attitudes at reasonable proficiency
Unleashing
the role of
e-learning
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2050-7003.htm
Received 31 October 2020
Revised 7 March 2021
7 April 2021
Accepted 9 April 2021
Journal of Applied Research in
Higher Education
© Emerald Publishing Limited
2050-7003
DOI 10.1108/JARHE-10-2020-0377
levels to achieve the professional accountant role in a way that satisfies the employers’
needs and expectations and those of clients,peersandthepublicasawhole(Borgonovo
et al., 2019,p.3).
Hence, in the present work, the primary objective is to examine the mediating role of
e-learning on the relationship between SE and APC. This study is unique and based on
several aspects; first, it conducts an assessment of the moderating role of e-learning on the
SE-APC relationship, which has been under-examined in literature. Second, the work extends
literature through empirical evidence gathered from the accounting students in Jordanian
universities to test the engagement theory’s underpinning theory (Kearsley and
Schneiderman, 1998). The study employs the theory’s conceptual framework to clarify the
importance of self-directed, meaningful involvement of the students with the applications/
materials based on motivation and cognitive challenge (O’Brien and Toms, 2008).
2. Theoretical background
According to Fredricks et al. (2004), SE has three dimensions: behavioral, emotional and
cognitive engagement. The SE theory posits that the basic underlying premise is that
students have to be engaged in learning activities in a meaningful manner via interacting
with their peers and conducting worthwhile activities. The SE theory presents a framework
for a more technology centered learning and teaching. SE theory defines the guidelines as to
how technology can be infused in the operating structures of institutions in order to promote
both the academic and career development of students. SE theory is also concerned with
enhancing the image and reputation of institutions. The more effective engagement and
efforts lead to more efficient and higher quality learning outcomes. SE refers to activities that
students take part in for the purpose of enhancing learning and career related skills. This
shows that, realistically, although engagement may take place even when technology is not in
use, technology greatly facilitates engagement in ways that are challenging to achieve
without it (Kearsley and Schneiderman, 2006). Studies of this caliber highlighted SE’s role in
students’performance achievements (Lee, 2014;Lei et al., 2018;Northey et al., 2018;Parsons
et al., 2018;Torsney and Symonds, 2019). The Student engagement theory focuses on the
utilization of time, efforts, and other resources by students and institutions in order to
maximize the learning outcomes. It looks for different ways that can incorporate newer
procedures and well developed technological techniques in order to enable and empower
institutions to compete with the digitally developing academic world and to make the
students hone their skills. Nevertheless, according to Rajaram and Singh (2018), APC as the
accounting education system outcome has yet to be extensively studied. In accounting, APC
refers to the ability to illustrate the required technical and professional values, skills, ethics
and attitudes at a proficient level to achieve a professional accounting role in a way that
satisfies the clients, peers and public expectations (Borgonovo et al., 2019, p. 17).
Moreover, APC contributes significantly to graduate accounting students in terms of their
effectiveness and enhancement of management performance and added value (Jaeger, 2003;
Promis, 2008;Watkin, 2000). Studies indicated that e-learning supports computer teaching
and learning through web technology for enhanced education quality (Kattoua et al., 2016;
Soni and Dubey, 2018;White, 2007). Thus, it is suggested that SE practices lead to enhanced
APC via supporting e-learning methods.
Based on this perspective, Astin’s (1993) student involvement theory and Kahn’s (1990)
employee engagement framework are formed based on four major components: emotional
engagement, physical engagement, cognitive class and cognitive out-of-class engagement.
The two mentioned theories are applied in this study to gauge the level of SE. Lastly, the
study assumes that SE activities are related to e-learning methods related to the students’
APC skills.
JARHE
3. Hypothesis development
3.1 Student engagement (SE) and accounting professional competencies (APCs)
Students must be the recipients of additional professional development opportunities to
enhance their knowledge and skills, obtain experience and critically reflect on their
engagement, as advocated by Doberneck et al. (2017). In the same study, Bullen et al. (2018)
contended that the accounting faculty needs to enhance teaching to prepare the students for
their future careers by increasing their co-curricular activities. Instructors have to urge
students to increase their engagement via presentations and academic research, and practical
activities. Engagement and relational goals as learning methods were evidenced to affect
APC learning (Daumiller and Dresel, 2020). Moreover, SE promotion makes students more
aware of their ELO competence with the relevant learning outcomes (Cydis et al., 2015). Also,
Kunter et al. (2013) illustrated that SE practices via the pedagogical content, knowledge,
teaching enthusiasm and self-regulatory skills on instructional quality of the instructions
influence the students’outcomes, specifically when it comes to APC. Hence, this study
proposes the following hypothesis for testing:
H1. SE has a positive effect on APC.
3.2 Student engagement (SE) and e-learning
Student engagement refers to the institutional efforts in both the academic and character
growth of students and e-learning is a beneficial technological platform that can aid in
boasting these efforts. E-learning offers a wide variety of characteristics and literature that
make it an ideal technological tool for incorporating SE. E-learning harnesses more interest
from student body and provides an easier and less stressful platform to them. Globalization
has brought about enhanced information and technologies, leading to the promotion of a
novel teaching and learning method (Rodgers, 2008).Intheliteratureconcerningthetopic,
several authors found a significant and practical relationship between e-learning and SE
(e.g., Dahalan et al., 2012;Hussain et al., 2018;Kahn et al., 2017;Rodgers, 2008). In Kahn
et al.’s (2017) study,theauthorsfoundthatSEinlearninghingesonthewaythestudents
engage in e-learning and such an environment, the engagement level is more significant
when the students sense a teaching presence of the actual instructors in the field (Jung and
Lee, 2018).
More importantly, to enhance teaching effectiveness for academic achievement, it is a
must for higher education to aim towards developing e-learning via teaching strategies that
boost higher levels of engagement (Rodgers, 2008). Added to this, students’learning occurs
after the realization of the occurrence of higher teaching levels is applied through ongoing
interaction in e-learning courses (Joo et al., 2011). Lastly, e-learning engagement can also be
reflected through behavioral characteristics in many ways; elimination of disturbances in the
environment during the class, learning management through online system usage, learning
schedule management via a lecture plan during an online class. This factor’s indicators vary
from behavioral activities used in face-to-face learning environments, as clarified by Lee et al.
(2019). A lot of efforts are required for homogenizing the experience of e-learning throughout
the institutions. There are a lot of online e-learning tools available for the institutions to
utilize. E-learning through its vast available literature can be of assistance in virtually any
subject and field. Online courses, if made available to students by the organizations, can help
them Polish their curricular and extra-curricular skills. The major benefits of e-learning in SE
are the characteristics of content, the available material to the student at any time and
elimination of stress element, which leads to more productive learning. This study
proposes that
H2. SE has a positive effect on e-learning.
Unleashing
the role of
e-learning
3.3 E-learning and accounting professional competencies (APCs)
The provided accounting information must cover essential APC skills for professional
careers (e.g., accounting specialists). This is because the modern accounting profession calls
for technical and soft skills, with businesses demanding accountants with current skill sets,
and as such, online education resources can assist in enhancing accounting APC skills
(Szadziewska et al., 2017). Various aspects should be increasingly covered in online learning,
explicitly establishing a closer learning-professional relationship (Batalla et al., 2014).
In recent times, universities have focused on the advantages of reaping from e-learning to
enhance campus-based students’learning performance to enhance learning quality,
computer skills and essential skills for professional accountants (Concannon et al., 2005).
In the same study caliber, Zhang and Liu (2019) gauged teacher’s learning engagement
through their learning behaviors and Palacios-Marques et al. (2013) revealed that employees
must conduct modifications of professional key competencies including pedagogical
management, technical and social competencies for the development of knowledge,
management and e-learning project success. In a recent study, Al-Fraihat et al. (2020)
focused on these e-learning factors: perceived usefulness, perceived satisfaction and
accounting materials use. Hence, this study proposes the following hypothesis for testing;
H3. E-learning positively affects APC.
3.4 Mediating role of e-learning between SE and APC
Like most professionals, accountants must maintain their APC through continuous
professional development (Ross and Anderson, 2013). SE practices were critical in
enhancing accountants’and entrepreneurs’professional development (Bullen et al., 2018).
Regarding SE and accounting competencies of students, they represent environment and
outcome, respectively, with SE mediating the influence of student accounting competencies
inputs (Yanto et al., 2011). Engagement activities can thus boost the students’coursework
completion successfully and result in fruitful communications among professionals, urging
them to work harder in their profession (Fredin et al., 2015).
With the current accountants’need to obtain APC to meet the increasing global demands
and expectations, the role of technology in teaching, learning and skills application in the
current times has become significant, specifically when it comes to e-learning tools (Joshi and
Chugh, 2009). This is why the Universality of Information Technology in Businesses
modified the nature and economies of accounting activities, leading to the demand for
advanced information technology skills among accountants (Pan and Seow, 2016). Lastly,
according to Lin et al. (2014), e-learning strategy is superior to the traditional method of
learning in terms of learning motivation, as it provides the students with an ease to revisit old
lectures to prepare more efficiently and allows them to relieve their stress of missing crucial
points or not understanding them, and as such, based on the above discussion, this study
proposes that
H4. E-learning has a mediating effect on the relationship between SE and APC.
3.5 Student demographics moderates the relationship between SE and APC
The demographic characteristics of students are crucial as determinants of their behaviors
and educational interactions as they possess varying perspectives based on their gender, age,
grades, employment and completion of degrees (Adewale, 2016;Gershenson et al., 2016;
Hanafi and Noor, 2016;Heidarian et al., 2015;Lester, 2014;Sansone, 2019).
Literature shows that demographic factors of college-age, grades, degree completion,
graduate school enrolment and employment significantly affected SE, which is the key to
successful student achievement (Abcioglu et al., 2019;Abbing, 2013;Boulton et al., 2019;
JARHE
Daumiller and Dresel, 2020;Duan et al., 2018;Reschly and Christenson, 2019;Shah and
Shah, 2018).
Meanwhile, in recent studies, demographic characteristics of students were examined in
terms of their effect on e-learning activities, and the findings showed that age and grades of
students significantly influence their SE and professional skills (e.g., Dahalan et al., 2012;
Duan et al., 2018;Jung and Lee, 2018;Thill et al., 2016;Zhang and Liu, 2019). Lastly, students’
demographic characteristics were also found to affect their APC and their accounting skill
development (Anjum, 2020;Hassan et al., 2020;Helm, 2015;Kirstein et al., 2019;Szadziewska
et al., 2017;Yanto et al., 2011). Hence, this study proposes the moderating effect of students’
demographic characteristics on the relationship between SE and APC and tests the following
hypothesis;
H5. Students’demographic characteristics moderate the relationship between SE
and APC.
H5a. Students’performance moderates the relationship between SE and APC.
H5b. Students’gender moderates the relationship between SE and APC.
H5c. Students’age moderates the relationship between SE and APC.
The above-formulated hypotheses and the variables are depicted in Figure 1.
4. Methodology
4.1 Sample and population
The study employed a structured questionnaire as a data collection instrument, distributed to
accounting students in Jordanian universities. From the 650 questionnaires distributed, 439
were retrieved, and after dropping the incomplete questionnaires, 428 questionnaire copies
were deemed useable, indicating a response rate of 65.84%. The Structural
Equation Modeling (SEM) in PLS-SEM analysis was adopted for data analysis. The
purpose of using a questionnaire was to firstly, get a good quality data in short amount of
Student Engagement
E-learning
Student’s Demographic
Professional
Competence
Behavioral Engagement
Cognitive Engagement
Academic Engagement
Emotional Engagement
Intensity Engagement
Performance
Gender
Age
H3
H2
H3
H1
H5(H5a, H5b, H5c)
Figure 1.
Theoretical framework
Unleashing
the role of
e-learning
time, secondly, questionnaire helped in getting authentic information from people directly
involved and in getting their thoughts and experiences. Moreover, it saved the time and
resources of individual interviews and collected a sufficient amount of data from a versatile
audience. Questionnaire also helped in audience evaluation and the relative gravity of each
participant’s opinion regarding a certain scenario. Questionnaire is a very authentic way of
gathering both qualitative and quantitative data and is one of the most frequently sought
after ways to gather data.
4.2 Measurement of variables
Practices of SE were measured in prior literature using various dimensions, but based on the
objectives of this study, it is pertinent to consider the significant dimensions of SE, and thus,
the study depends on the measurements proposed by various studies including, Buckless and
Krawczyk (2016),Kahu and Nelson (2018),Shernof et al. (2017),Taylor et al. (2018),Thomas
et al. (2019) and Yanto et al. (2011), which employed five different SE practices dimensions
(cognitive engagement, behavioral engagement, educational/academic engagement,
emotional engagement and intensity engagement). In terms of APC, the study stressed
technical skills, organizational skills, people skills and conceptual skills as mentioned in prior
studies by Damasiotis et al. (2015),Okoro (2013),Rufino et al. (2017) and Yanto et al. (2011).
This study also adopted the e-learning tools and facilities measurement used in Ameen et al.
(2019),Buzzetto-More (2008),Cole et al. (2014) and Mohammadyari and Singh (2015) studies.
The questionnaire items were measured along a five-point Likert scale.
5. Data results
This study adopted the partial least square–structural equation modeling (PLS-SEM) method
for data analysis in light of various methods and approaches used in past studies. PLS-SEM is
an extensively utilized technique over SEM’s other techniques (e.g., LISREL, AMOS) (Hair
et al., 2016). The PLS modeling origin can be traced back to Herman (1982), who brought it
forward as the computational aspect of LVPLS software. The method’s developments have
been related to Ong and Puteh’s (2017) study, wherein the new diagrammatical interface
(referred to as PLS-Graph) was utilized for an increased justification of the methods. The PLS
modeling method estimates the causal relationships in the path models consisting of
constructs indirectly evaluated by several variables, and the PLS modeling description is
obtained through two means; measurement model connecting the manifest variables to the
corresponding latent variables and the structural model connecting the entire endogenous
latent variables to the corresponding other latent variables in the model. The former is called
the inner model, and the latter is called the outer model.
More importantly, PLS was used in this study owing to various reasons, among which the
top reason was that structural equation models have been established to be efficient in
estimation over other regression models (Tarka, 2018). Also, in actual real-world situations
and complex model applications, PLS is more advantageous, and as such, it is more
appropriate than its counterparts (Afthanorhan, 2013).
The fundamental assumption behind PLS modeling is the ability to dynamically create
and measure complicated models with superior qualities capable of evaluating complex and
large models (Akter et al., 2017). Thus, this study used PLS to examine the variables. In prior
social science studies, data normality is a common issue (Ong and Puteh, 2017;Shaheen et al.,
2017), while this issue is non-existent in the PLS method (Jannoo et al., 2014). Notably, PLS
handles all data, even non-normal data, and in this view, the PLS modeling technique is used
in estimating the study process in anticipation of any potential normality issue that may arise
during the analysis process.
JARHE
5.1 Multicollinearity test
The latent independent variables correlation is significant as it shows the level to which a
variable can be explained by its counterparts in the process of analysis (Hair et al., 2010).
Table 1 tabulates the values of the multicollinearity test results. From the table, no issue
exists as the unobserved latent independent constructs values did not exceed r50.9 –the
rule of thumb established by Hair et al. (2010) and Pallant (2020) and thus, the study
proceeded towards data analysis as presented in detail in the following sections.
5.2 Measurement model
PLS-SEM was employed for the model estimation, particularly with SmartPLS Version 3.2.7
software application suggested by Ringle et al. (2015). There are two primary multivariate
techniques used in PLS-SEM: factor analysis and multiple regressions (Hair et al., 2010). The
first step involves the measurement of model assessment, where the two primary criteria
used by PLS are the validity and reliability of the model. Discriminant validity was also
measured with the help of cross-loading and Fornell-Larcker’s method. The PLS
measurement is displayed in Figure 2.
There are three types of values in the above figure –the ones directed by the long arrow
lines are the path coefficients, those by the yellow box are the item loadings, and the circle
contents are the coefficient of determination (R2) values.
5.2.1 Reliability of the individual measurement items. The reliability of items is generally
measured through their item loadings (Byrne, 2010), where item loadings lower than 0.4 are
generally dropped, and those higher than 0.7 are kept to increase the AVE. In this study, the
dropped items were from APC and E-learning (PC8 and EL4). As Table 2 shows the item
loadings results, and all the item loadings exceeded 0.40, which indicates items reliability.
The variable reliability was kept under check and so loadings lower than 0.4 were dropped in
order to scrutinize the data and clean it so that efficient pre-processing could be achieved.
5.2.1.1 Internal consistency reliability. This type of reliability shows level two in which the
items measuring a construct are correlated with a higher correlation indicating higher
reliability (Kline, 2015). To establish and confirm internal consistency and reliability in this
study, the author used composite reliability (CR) estimates, and they were found to be all
higher than 0.70. This shows that the constructs have sufficient internal consistency
reliability (refer to Table 2).
5.2.2 Convergent validity. This type of validity evaluates how a specific measure truly
measures what it is meant to, correlating positively with other measures of the same
construct (Hair, 2007). AVE measures convergent validity, and AVE value of 0.50 and above
indicates convergent validity (Chin, 1998;Hair et al., 2011). Such AVE value (0.50) indicates
that the latent construct managed to explain half of the item’s variance (Hair et al., 2016).
Thus, to establish the convergent validity of each latent variable, their AVE should be higher
than 0.50 (Chin, 1998). All the AVE values exceeded the 0.50 recommendation on their
respective variables in the present work, confirming their convergent validity. The AVE
values of the variables are tabulated in Table 2.
5.2.3 Discriminant validity. In this type of validity, the construct’s level that differs from
other constructs is presented (Hair et al., 2010). It illustrates that the undamaged items that
EL PC SE
EL 1.000
PC 0.673 1.000
SE 0.464 0.553 1.000
Table 1.
Inter-constructs
correlations for
multicollinearity test
Unleashing
the role of
e-learning
Figure 2.
LS measurement model
JARHE
measure a construct are related more to the one being measured than all the other model
constructs (Hair et al., 2016). In PLS-SEM, there are three assessment measurements for
discriminant validity, and they are; Fornell-Larcker criterion by Fornell and Larcker (1981),
cross-loadings by Chin (1998) and the heterotrait-monotrait ratio of correlations by Henseler
et al. (2016).Table 3 displays each item’s cross-loading values for each construct, and the
values confirm the presence of discriminant validity.
5.3 Assessment of structural model (inner model)
The next step in PLS analysis is to assess the inner model or the structural model. Structural
model assessment involves examining the relationships among the hypothetical model’s
latent constructs (Hair et al., 2016). Such a relationships showed the formulated hypotheses in
the structural model that needed testing. More specifically, the structural model examines the
inter-relationships between SE (exogenous variable) and the endogenous variable (APC),
student’s demographics including age, gender and performance (mediating variable).
Accordingly, the study assessed the structural model using path coefficient significance and
level of coefficient of determination (R
2
), effect size (f
2
) and predictive relevance (Q
2
).
5.3.1 Effect size (f2). Aside from assessing the exogenous latent constructs combined
effects on their endogenous counterpart, the impact of each of the former constructs on the
Constructs Items Loading
Cronbach’s
alpha
Composite
reliability
Average
variance extracted (AVE)
Student
Engagement
0.764 0.839 0.511
Behavioral 0.677
Engagement
(BE)
Cognitive 0.714
Engagement
(CE)
Academic 0.734
Engagement
(AE)
Effective 0.715
Emotional
Engagement
(EEE)
Intensity 0.734
Engagement
(IE)
E-Learning 0.708 0.810 0.563
EL1 0.708
EL2 0.680
EL3 0.666
EL5 0.765
EL6 0.566
APC 0.796 0.764 0.526
PC1 0.508
PC2 0.537
PC3 0.759
PC4 0.487
PC5 0.722
PC6 0.408
PC7 0.486
Table 2.
Summary of items
loading, composite
reliability and average
variance
extracted (AVE)
Unleashing
the role of
e-learning
latter construct was measured through the use of effect size (f
2
). This, according to Hair et al.
(2013), establishes the relative effect of a specific exogenous latent variable on a
corresponding latent endogenous variable. Moreover, it is a measurement of the change in
the value of (R
2
) owing to the dropping of a construct, and it examines if the deleted
exogenous construct makes a difference in the endogenous construct coefficient
determination (R
2
), as explained by Shanmugapriya and Subramanian (2016). Meanwhile,
effect size (f
2
) indicates the practical contribution of the path that relates the two constructs
together (exogenous and endogenous) (Preacher and Kelley, 2011).
The (f
2
) values of the exogenous constructs obtained from PLS-SEM, using survey data
are presented in Table 4. From the table, it is evident that the independent variables had
significant positive effects on the dependent variable, ranging from medium to significant.
5.3.2 Predictive relevance (Q2). The predictive relevance or Stone-Geisser’sQ
2
was
obtained using the blindfolding procedure to establish the model’s predictive quality.
According to Tenenhaus (1999), a positive Q
2
value shows that the model possesses
predictive validity. On the other hand, a negative one shows that the model lacks validity.
Therefore, the study used a blindfolding procedure on the endogenous construct using Smart
PLS Version 3.2.7, having an omission distance of 7 to calculate Stone-Geisser Q
2
.
The cross-validated redundancy values of 0.096 and 0.86 were obtained, confirming the
model’s small predictive quality (see Table 5).
5.4 Direct path coefficients
Both direct and indirect path coefficient assessment using the bootstrapping method was
recommended by prior studies (Chin, 1998;Hair et al., 2013). Thus, the study used
SE EL PC
AE 0.734 0.345 0.369
BE 0.677 0.235 0.396
CE 0.714 0.269 0.310
EEE 0.715 0.336 0.351
IE 0.734 0.428 0.504
EL1 0.352 0.708 0.514
EL2 0.263 0.680 0.550
EL3 0.327 0.666 0.431
EL5 0.331 0.765 0.477
EL6 0.316 0.566 0.265
PC1 0.134 0.069 0.508
PC2 0.184 0.180 0.537
PC3 0.723 0.481 0.759
PC4 0.036 0.152 0.487
PC5 0.268 0.716 0.722
PC6 0.031 0.148 0.408
PC7 0.045 0.115 0.486
Constructs E-Learning APC Effect size
E-learning 0.469 Large
Student engagement 0.156 Medium
SE 0.274 Small
Table 3.
Cross loadings
(explanations of
pp. 10, 11)
Table 4.
Constructs effect size
JARHE
bootstrapping to assess the direct and indirect path coefficient statistical significance (Hayes,
2012). According to Hair et al. (2013) and Hair et al. (2016), the direct path coefficients are
estimated to determine the relationship significance between the structural model variables.
Accordingly, the study employed the bootstrap sample of 428, following the recommendation
of Hair et al. (2017). Refer to Figure 3 for the bootstrapped model of the direction relationships.
In the structural model, the direct relationships between the independent and dependent
variables are tabulated in Table 6.
From the above table, the hypothesis testing results for the proposed direct relationships
are displayed. The detailed discussions are provided in the following paragraphs.
Hypothesis 1: SE has a positive effect on APC. This positive effect is displayed by the path
that links SE to APC in Table 6, which shows that the path estimates of the SE-APC linkage
with the beta of (β50.306), t-statistic of (t52.631) and p-value (p50.009). This shows that
SE has a positive and significant effect on APC, supporting hypothesis 1.
Hypothesis 2: SE has a positive effect on E-learning. This is shown by the path that links
SE to e-learning in Table 6, whereby the path estimates for the relationship in terms of beta is
(β50.464), tstatistics are (t56.158) and the p-value is (p50.000). These show the significant
relationship between the two variables, confirming SE’s positive and significant effect on
e-learning at the significance level of 1%, supporting hypothesis 2.
Hypothesis 3: E-learning has a positive effect on APC. This is shown by the path that links
e-learning to APC with path estimates of beta being (β50.531), t-statistics of (t55.209) and
p-value of (p50.000) (refer to Table 6). The result shows a positive and significant effect of
e-learning on APC at the significance level of 1%, confirming and supporting hypothesis 3.
5.5 Analysis of the effect of e-learning as a mediator
Hypothesis 4: E-learning has a mediating effect on SE, and APC’s relationship and the results
support this effect.
The level to which the IV directly explains the DV variance is determined by obtaining
how much of the former is explained by the indirect relationship through the mediating
variable. Therefore, the Value of Variance Accounted for (VAF) was used to determine the
mediating effect strength as follows;
VAF 5(P
a
•P
b
)/(P
a
•P
b
þP
c
)
In the above equation, P
a
denotes the path from IV to MV P
b
denotes the path from MV to
DV, and P
c
denotes the indirect path from IV to DV.
VAF values that are lower than 20% indicate no mediating relationship, those that range
between 20–80% indicates partial mediating relationship, and those that exceed 80%
indicate entire mediating relationship (Hair et al., 2016).
In terms of this study, e-learning has a partial mediating effect on the SE-APC relationship
(see Table 7).
5.5.1 Coefficient of determination (R2). Part of the structural model assessment is the
coefficient of determination (R
2
), and it refers to the amount of variance in the dependent
variable that is jointly explained by the independent variables (Hair et al., 2013). In this study,
SE was found to explain 21.5% of the e-learning variance (moderate range), while e-learning
and SE explained 52.7% of the SE variance.
Construct SSO SSE Q
2
(1-SSE/SSO
EL 390.000 356.641 0.086
SP 546.000 493.637 0.096
Table 5.
Predictive Relevance of
the Model
Unleashing
the role of
e-learning
Figure 3.
Structural model of
direct path
JARHE
5.6 Moderating path coefficient
The interaction term concept is utilized to facilitate the moderating variable inclusion in the
statistical model. Based on literature (e.g., Dawson, 2013;Henseler and Chin, 2010;Henseler
and Fassot, 2010;Rigdon et al., 2010;Hair et al., 2017), product indicator, two-stage and
orthogonalizing methods are methods used to examine the interaction effects among
independent and moderating variables in the structural model. In the present work, the two-
stage method was used to develop interactions between performance, gender and age as
moderating variables, and SE as the independent variable (refer to Figure 3).
Table 8 presents the tested moderating effects in the structural model on the relationships
between independent and dependent variables.
Hypothesis 5: Students’demographic characteristics moderate the relationship between
SE and APC.
Hypothesis 5: Students’performance moderates the relationship between SE and APC.
Based on the results in Table 8, performance does not moderate the SE-APC relationship,
with t-statistics (t50.526) and p-value (p50.599), indicating that hypothesis 5 is rejected.
Hypothesis 5b: Students’gender moderates the relationship between SE and APC. The
moderating role of gender on the SE-APC relationship was insignificant with t-statistics
(t50.931) and p-value (0.352). Therefore, the results rejected the proposed hypothesis 5b.
Hypothesis 5c: Students’age moderates the relationship between SE and APC. From
Figure 4 and Table 8, the result of the moderating relationship of age between SE and APC
appears to be low with t-statistics of (t51.639) and p-value of (p50.102). This result rejects
Hypothesis 5c.
6. Discussion
Because of the increasing interest in SE practices in accounting APC, the present work
examined the SE practices’role on the students’APC, with e-learning mediating the
relationship and the student demographics moderating. Empirical evidence was collected
from the students of Jordanian universities, and data was analyzed using PLS-SEM.
Paths Beta STDEV Tstatistics pvalues Remark
SE →PC 0.306 0.116 2.631 0.009 Accepted
SE →EL 0.464 0.075 6.158 0.000 Accepted
EL →PC 0.531 0.102 5.209 0.000 Accepted
SE →EL →PC 0.247 0.068 3.15 0.000 Accepted
Path a Path b Path c Indirect effect Total effect VAF Mediation
0.464 0.531 0.553 0.246 0.799 0.31 Partial
Mediation
Paths Beta Sample mean SD Tstatistics pvalues
(Age*SE) →APC 0.182 0.181 0.111 1.639 0.102
(Gender*SE) →APC 0.098 0.084 0.105 0.931 0.352
(Performance*SE) →APC 0.045 0.044 0.086 0.526 0.599
Table 6.
Direct and indirect
path coefficients
Table 7.
Result in the indirect
relationship between
SE and APC via
e-learning as mediator
Table 8.
Moderating path
coefficient
Unleashing
the role of
e-learning
Figure 4.
Structural model of
moderating path
JARHE
According to the empirical findings, SE practices significantly influence the students’APC
(supporting H1). This result is aligned with that reported by prior studies by Almarghani and
Mijatovic (2017),Chong et al. (2018),Marriott (2017) and Yanto et al. (2011). These studies
examined the SE practices-APC relationship among accounting students in the markets
characterized by formal policies and ERM practices. In other words, e-learning is a novel
teaching approach involving student engagement, and universities need to adapt it for
enhanced APC among accounting graduates (Faidley, 2018). This result is aligned with
Yanto’s (2016) argument that SE has a significant and positive relationship with APC.
The present study’s result revealed a significant effect of SE practices on e-learning,
indicating support for H2 –a result that supports prior studies results like Czerkawski and
Lyman (2016), who found e-learning to be effective instructors that increase the engagement
of students.
Furthermore, e-learning has a significant favorable influence on the APC of the accounting
students, indicating that H3 is supported. The findings are consistent with those reported by
Szadziewska et al. (2017), who found the accounting profession to need online education
resources to enhance their accounting APC skills.
Moving on to the mediating effect, e-learning was supported to have a partial mediating
effect on the SE-APC relationship, partially supporting the proposed H4. This is in contrast
with Yanto et al.’s (2011) study, which contended that SE has an effective mediating influence
on the APC inputs and Bullen et al.’s (2018) study, which showed that SE practices improved
professional advancement in accounting and business professions by students who
participated more in co-curricular activities. Lastly, students’demographic characteristics
were found to have an insignificant moderating effect on SE and APC’s relationship, rejecting
H5 of the study. This contrasts with prior studies that found SE to influence educational
outcomes, such as Shernof et al. (2017), and those that presented a negative relationship
between engagement and academic outcomes. The study result is inconsistent with that of
Khalida et al. (2016) study, which revealed that gender influences the accounting students’
perceptions of the accounting profession.
6.1 Contributions and implications
In the field of SE practices, prior studies supported a significant e-learning-APC relationship.
Previous studies that investigated the direct SE influence on APC provided inconsistent
results, and thus, this study contributes to the literature in many ways. First, the study
examined the mediating effect of e-learning on SE and APC’s relationship that has not been
extensively examined in accounting. The study also delved into the moderating effect of
student demographics on the same relationship –a moderating effect that has been under-
examined. The majority of studies of this caliber focused on developed countries rather than
developing nations, like Jordan.
The present study examined the relationship among the study variables in the context of
an emerging economy, Jordan using the Structural Equation Modeling in PLS-SEM analysis.
The study findings support the universities need to apply and use e-learning tools where SE
practices are enhanced to acquire accounting APC. Prior studies mainly focused on students’
cognitive engagement practices, while the present one focused on SE’s five dimensions
(cognitive engagement, behavioral engagement, educational/academic engagement,
emotional engagement and intensity engagement). The research is aligned with prior
studies that argued that SE influences e-learning tools and students’demographics on the
accounting students’APC.
6.2 Limitations and future research directions
There are several implications for the study’s practice, particularly for instructors and
accounting curriculum designers in universities to focus on SE practices and e-learning tools
Unleashing
the role of
e-learning
to enhance APC acquisition. Every institution of higher learning is urged towards achieving
quality learning outcomes. Therefore, each university is exerting efforts toward obtaining
advanced education technologies in the field for optimum outcomes, especially when it comes
to APC. Universities may achieve this goal by obtaining resources that could lead to high-risk
levels. Based on this viewpoint, Jordanian universities own limited resources and are actively
searching for less risky endeavors. The study recommends that instructors and universities
focus on SE practices in their quest to enhance the participation and classwork advantages of
accounting students. It is also recommended that advanced technologies are adopted by
universities to promote SE practices for the essential APC acquisition of students, as this
assists their competitiveness and survival in their future careers. The implications cover both
developing and developed nations to leverage the research findings equally.
7. Conclusions
In this study, the effect of SE practices on the APC of accounting students was examined, with
e-learning as a mediating variable and students’demographics as the moderating variable.
The data was gathered from 650 questionnaires distributed to students in Jordanian
Universities out of which only 439 were retrieved, and after checking for incomplete ones only
428 questionnaires were considered useable, suggesting a response rate of approximately
65.84%. The scrutiny of data lead to a cleaner spread of data and better quality so that there
were no redundant or empty variables. The formulated research hypotheses were tested
using SEM in PLS-SEM analysis. According to the results, SE has a positive and significant
effect on APC, SE has a positive and significant effect on e-learning, and e-learning has a
positive and significant effect on APC. The study findings also revealed e-learning to have a
partial mediating effect on the SE-APC relationship. As for demographic characteristics (age,
gender and performance), they were found to have no significant moderating effect on the SE-
APC relationship. The study recommends that university accounting departments adopt
advanced technologies and promote SE practices for the optimum APC acquisition by
accounting students. The outcomes of this paper can be widely used by institutions in
incorporating SE practices and e-learning. The paper suggests institutions to upgrade and
revolutionize their technological approach in providing SE and e-learning. E learning when
incorporated under the framework of SE theory will increase the efficiency of both teaching
and learning. Institutions can utilize the versatile tools available on the Internet and provide
them to students. E-learning can aid in achieving the objectives of SE and enable
organizations to develop and compete in a rapidly digitalizing academic market. The APC
acquisition by accounting students can be greatly eased and imparted more effectively
through the incorporation of SE.
References
Abacioglu, C.S., Zee, M., Hanna, F., Soeterik, I.M., Fischer, A.H. and Volman, M. (2019), “Practice what
you preach: the moderating role of teacher attitudes on the relationship between prejudice
reduction and student engagement”,Teaching in Higher Education, Vol. 86 November 2019,
pp. 1-10, doi: 10.1016/j.tate.2019.102887.
Abbing, J. (2013), Effect of Students’Engagement on Academic Achievement in Different Stages of
Their Academic Career from a Dropout Perspective, The University of Twente.
Adewale, A.A. (2016), “Effect of demographic factors on entrepreneurial culture: a study of university
students in metropolitan Kano”,American Journal of Social Sciences and Humanities, Vol. 1
No. 1, pp. 10-34.
Afthanorhan, M.A. (2013), “A comparison of partial least square structural equation modeling (PLS-
SEM) and covariance-based structural equation modeling (CB-SEM) for confirmatory factor
JARHE
analysis”,International Journal of Engineering Science and Innovative Technology, Vol. 2 No. 5,
pp. 198-205.
Akter, S., Wamba, S.F. and Dewan, S. (2017), “Why PLS-SEM is suitable for complex modeling? An
empirical illustration in big data analytics quality”,Production Planning and Control, Vol. 28
Nos 11-12, pp. 1011-1021, doi: 10.1080/09537287.2016.1267411.
Al-Fraihat, D., Joy, M., Masa’deh, R. and Sinclair, J. (2020), “Evaluating E-learning systems success: an
empirical study”,Computers in Human Behavior, Vol. 102, pp. 67-86, doi: 10.1016/j.chb.2019.
08.004.
Almarghani, E.M. and Mijatovic, I. (2017), “Factors affecting student engagement in HEIs - it is all
about good teaching”,Teaching in Higher Education, Vol. 22 No. 8, pp. 940-956, doi: 10.1080/
13562517.2017.1319808.
Ameen, N., Willis, R., Abdullah, M.N. and Shah, M. (2019), “Towards the successful integration of
e-learning systems in higher education in Iraq: a student perspective”,British Journal of
Educational Technology, Vol. 50 No. 3, pp. 1434-1446, doi: 10.1111/bjet.12651.
Anjum, S. (2020), “Impact of internship programs on the professional and personal development of
business students: a case study from Pakistan”,Future Business Journal, Vol. 6 No. 1, pp. 1-13.
Astin, A. (1993), What Matters in College? Four Critical Years Revisited, Jossey-Bass. Publishers,
San Francisco.
Batalla, J.M., Rimbau, E. and Serradell, E. (2014), “E-learning in Economics and business”,RUSC.
Universities and Knowledge Society Journal, Vol. 11 No. 2, p. 3, doi: 10.7238/rusc.v11i2.2168.
Borgonovo, A., Friedrich, B. and Wells, M. (2019), Competency-Based Accounting Education, Training,
and Certification: An Implementation Guide, The World Bank, Washington.
Boulton, C.A., Hughes, E., Kent, C., Smith, J.R. and Williams, H.T.P. (2019), “Student engagement
and wellbeing over time at a higher education institution”,PloS One, Vol. 14 No. 11, p. e0225770,
doi: 10.1371/journal.pone.0225770.
Bryson, C. (2016), Engagement through Partnership: Students as Partners in Learning and Teaching in
Higher Education, Taylor & Francis.
Buckless, F. and Krawczyk, K. (2016), “The relation of student engagement and other admission
metrics to master of accounting student performance”,Accounting Education, Vol. 25 No. 6,
pp. 519-533, doi: 10.1080/09639284.2016.1218778.
Bullen, M.L., Kordecki, G.S. and Capener, E.D. (2018), “Student engagement activities to enhance
professional advancement in accounting and business careers”,Journal of Instructional
Pedagogies, Vol. 20, p. 20.
Buzzetto-More, N.A. (2008), “Student perceptions of various E-learning components”,Interdisciplinary
Journal of e-Skills and Lifelong Learning, Vol. 4 No. 1, pp. 113-135, doi: 10.28945/370.
Byrne, B. (2010), Multivariate Applications Series. Structural Equation Modeling with AMOS: Basic
Concepts, Applications, and Programming, Routledge/Taylor & Francis Group, New York, NY.
Chin, W.W. (1998), “The partial least squares approach to structural equation modeling”,Modern
Methods for Business Research, Vol. 295 No. 2, pp. 295-336.
Chong, W.H., Liem, G.A.D., Huan, V.S., Kit, P.L. and Ang, R.P. (2018), “Student perceptions of self-
efficacy and teacher support for learning in fostering youth competencies: roles of affective and
cognitive engagement”,Journal of Adolescence, Vol. 68, pp. 1-11, doi: 10.1016/j.adolescence.2018.
07.002.
Cole, M.T., Shelley, D.J. and Swartz, L.B. (2014), “Online instruction, e-learning, and student
satisfaction: a three-year study”,The International Review of Research in Open and Distributed
Learning, Vol. 15 No. 6, pp. 111-131.
Concannon, F., Flynn, A. and Campbell, M. (2005), “What campus-based students think about the
quality and benefits of e-learning”,British Journal of Educational Technology, Vol. 36 No. 3,
pp. 501-512, doi: 10.1111/j.1467-8535.2005.00482.x.
Unleashing
the role of
e-learning
Csikszentmihalyi, M. (1990), Flow. The Psychology of Optimal Experience, HarperPerennial, New York,
NY, 1990.
Cydis, S., Galantino, M.L., Hood, C., Padden, M. and Richard, M. (2015), “Integrating and assessing
essential learning outcomes: fostering faculty development and student engagement”,
The Journal of the Scholarship of Teaching and Learning, Vol. 15 No. 3, pp. 33-52, doi: 10.1007/
s11423-016-9484-z.
Czerkawski, B.C. and Lyman, E.W. (2016), “An instructional design framework for fostering student
engagement in online learning environments”,TechTrends, Vol. 60 No. 6, pp. 532-539, doi: 10.
1007/s11528-016-0110-z.
Dahalan, N., Hassan, H. and Atan, H. (2012), “Student engagement in online learning: learners attitude
toward e-mentoring”,Procedia-Social and Behavioral Sciences, Vol. 67, pp. 464-475.
Damasiotis, V., Trivellas, P., Santouridis, I., Nikolopoulos, S. and Tsifora, E. (2015), “IT competencies
for professional accountants. A review”,Procedia - Social and Behavioral Sciences, Vol. 175,
pp. 537-545, doi: 10.1016/j.sbspro.2015.01.1234.
Daumiller, M. and Dresel, M. (2020), “Researchers’achievement goals: prevalence, structure, and
associations with job burnout/engagement and professional learning”,Contemporary
Educational Psychology, Vol. 61, pp. 1-27, 101843.
Dawson, G. (2013), Soldier Heroes: British Adventure, Empire and the Imagining of Masculinities,
Routledge, Oxfordshire.
Doberneck, D.M., Bargerstock, B.A., Mcnall, M., Egeren, L.V. and Zientek, R. (2017), “Community
engagement competencies for graduate and professional students: Michigan state University’s
approach to professional development”,Michigan Journal of Community Service Learning,
Vol. 24 No. 1, pp. 122-142.
Duan, Y., Berger, E., Kandakatla, R., Deboer, J., Stites, N. and Rhoads, J.F. (2018), “The relationship
between demographic characteristics and engagement in an undergraduate engineering online
forum”,Paper Presented at the 2018 IEEE Frontiers in Education Conference (FIE).
Faidley, J. (2018), Comparison of Learning Outcomes from Online and Face-To-Face Accounting
Courses), Doctor of Philosophy, East Tennessee State University.
Fornell, C. and Larcker, D.F. (1981), Structural Equation Models with Unobservable Variables and
Measurement Error: Algebra and Statistics, Sage Publications Sage CA, Los Angeles,
California, CA.
Fredin, A., Fuchsteiner, P. and Portz, K. (2015), “Working toward more engaged and successful
accounting students: a balanced scorecard approach”,American Journal of Business Education,
Vol. 8 No. 1, pp. 49-62, doi: 10.19030/ajbe.v8i1.9016.
Fredricks, J.A., Blumenfeld, P.C. and Paris, A.H. (2004), “School engagement: potential of the concept,
state of the evidence”,Review of Educational Research, Vol. 74 No. 1, pp. 59-109, doi: 10.3102/
00346543074001059.
Gershenson, S., Holt, S.B. and Papageorge, N.W. (2016), “Who believes in me? The effect of the
student-teacher demographic match on teacher expectations”,Economics of Education Review,
Vol. 52, pp. 209-224, doi: 10.1016/j.econedurev.2016.03.002.
Hair, J.F. (2007), “Research methods for business”,Education þTraining, Vol. 49 No. 4, pp. 336-337,
doi: 10.1108/et.2007.49.4.336.2.
Hair, J., Black, W., Babin, B., Anderson, R. and Tatham, R. (2010), in Hair, J.F. (Ed.), Cluster Analysis.
Multivariate Data Analysis, 7th ed., Prentice Hall, Upper Saddle River, New Jerssey, NJ.
Hair, J.F., Ringle, C.M. and Sarstedt, M. (2011), “PLS-SEM: Indeed a silver bullet”,Journal of Marketing
Theory and Practice, Vol. 19 No. 2, pp. 139-152.
Hair, J.F., Ringle, C.M. and Sarstedt, M. (2013), “Partial least squares structural equation modeling:
rigorous applications, better results and higher acceptance”,Long Range Planning, Vol. 46
Nos 1-2, pp. 1-12, doi: 10.1016/j.lrp.2013.01.001.
JARHE
Hair, J., Hult, T., Ringle, C. and Sarstedt, M. (2016), A Primer on Partial Least Squares Structural
Equation Modeling, Sage publications, PLS-SEM.
Hair, J.F., Sarstedt, M., Ringle, C.M. and Gudergan, S.P. (2017), Advanced Issues in Partial Least
Squares Structural Equation Modeling, Sage Publications, CA.
Hanafi, Z. and Noor, F. (2016), “Relationships between demographic factors and emerging Adult^
aSs
academic achievement”,International Journal of Academic Research in Business and Social
Sciences, Vol. 6 No. 6, pp. 291-303, doi: 10.6007/ijarbss/v6-i6/2198.
Hassan, H., Mohamad, R., Ali, R.H.R.M., Talib, Y.Y.A. and Hsbollah, H.M. (2020), “Factors affecting
students’academic performance in higher education: evidence from accountancy degree
programme”,International Business Education Journal, Vol. 13 No. 1, pp. 1-16.
Hayes, A.F. (2012), Process: A Versatile Computational Tool for Observed Variable Mediation,
Moderation, and Conditional Process Modeling, University of Kansas, KS.
Heidarian, A.R., Kelarijani, S.E.J., Jamshidi, R. and Khorshidi, M. (2015), “The relationship between
demographic characteristics and motivational factors in the employees of social security
hospitals in Mazandaran”,Caspian Journal of Internal Medicine, Vol. 6 No. 3, p. 170.
Helm, C. (2015), “Determinants of competence development in accounting in upper secondary
education”,Empirical Research in Vocational Education and Training, Vol. 7, p. 10.
Henseler, J. and Chin, W.W. (2010), “A comparison of approaches for the analysis of interaction effects
between latent variables using partial least squares path modeling”,Structural
Equation Modeling, Vol. 17 No. 1, pp. 82-109.
Henseler, J. and Fassot, G. (2010), “Testing moderating effects in PLS models: an illustration of
available procedures”, in VinziV, E., ChinW, W. and HenselerJ, W.H. (Eds), Handbook of Partial
Least Squares: Concepts, Methods and Applications in Marketing and Related Fields, pp. 195-218.
Henseler, J., Hubona, G. and Ray, P.A. (2016), “Using PLS path modeling in new technology
research: updated guidelines”,Industrial Management and Data Systems,Vol.116No.1,
pp. 2-20, doi: 10.1108/imds-09-2015-0382.
Herman, W. (1982), Systems under Indirect Observation Using PLS, North-Holland, Vol. 2.
Hussain, M., Zhu, W., Zhang, W. and Abidi, S.M.R. (2018), Student Engagement Predictions in an
E-Learning System and Their Impact on Student Course Assessment Scores, Computational
intelligence and Neuroscience, London, 2018.
Jaeger, A.J. (2003), “Job competencies and the curriculum: an inquiry into emotional intelligence
in graduate professional education”,Research in Higher Education, Vol. 44 No. 6, pp. 615-639,
doi: 10.1023/a:1026119724265.
Jaf, R., Sabr, S.A. and Nader, K.A. (2015), “Impact of management accounting techniques on achieve
competitive advantage”,Research Journal of Finance and Accounting, Vol. 6 No. 4, pp. 84-99.
Jannoo, Z., Yap, B., Auchoybur, N. and Lazim, M. (2014), “The effect of nonnormality on CB-SEM and
PLS-SEM path estimates”,Computational, Physical and Quantum Engineering, Vol. 8 No. 2,
pp. 285-291.
Joo, Y.J., Lim, K.Y. and Kim, E.K. (2011), “Online university students’satisfaction and persistence:
examining the perceived level of presence, usefulness and ease of use as predictors in a
structural model”,Computers and Education, Vol. 57 No. 2, pp. 1654-1664.
Joshi, M. and Chugh, R. (2009), “New paradigms in the teaching and learning of ac- counting: use of
educational blogs for reflective thinking”,International Journal of Education and Development
Using ICT, Vol. 5 No. 3, pp. 6-18.
Junco, R. (2012), “The relationship between frequency of Facebook use, participation in Facebook
activities, and student engagement”,Computers and Education, Vol. 58 No. 1, pp. 162-171,
doi: 10.1016/j.compedu.2011.08.004.
Jung, Y. and Lee, J. (2018), “Learning engagement and persistence in massive open online courses
(MOOCS)”,Computers and Education, Vol. 122, pp. 9-22.
Unleashing
the role of
e-learning
Kaciuba, G. (2012), “An instructional assignment for student engagement in auditing class: student
movies and the AICPA Core Competency Framework”,Journal of Accounting Education, Vol. 30
No. 2, pp. 248-266, doi: 10.1016/j.jaccedu.2012.08.003.
Kahn, W.A. (1990), “Psychological conditions of personal engage-ment and disengagement at work”,
Academy of Management Journal, Vol. 33 No. 4, pp. 692-724, doi: 10.2307/256287.
Kahn, P., Everington, L., Kelm, K., Reid, I. and Watkins, F. (2017), “Understanding student
engagement in online learning environments: the role of reflexivity”,Educational Technology
Research and Development, Vol. 65 No. 1, pp. 203-218, doi: 10.1007/s11423-016-9484-z.
Kahu, E.R. and Nelson, K. (2018), “Student engagement in the educational interface: understanding the
mechanisms of student success”,Higher Education Research and Development, Vol. 37 No. 1,
pp. 58-71, doi: 10.1080/07294360.2017.1344197.
Kattoua, T., Al-Lozi, M. and Alrowwad, A. (2016), “A review of literature on E-learning systems in
higher education”,International Journal of Business Management and Economic Research,
Vol. 7 No. 5, pp. 754-762.
Kearsley, G. and Schneiderman, B. (1998), “Engagement theory: a framework for technology-based
teaching and learning”,Educational Technology, Vol. 38 No. 5, pp. 20-23.
Kearsley, G. and Schneiderman, B. (2006), “Engagement theory: a framework for technology-based
learning and teaching (1999)”,Educational Technology, Vol. 11, pp. 20-23.
Khalida, F.M., Saranib, N.S.M., Hisamc, N.N. and Syaida, H. (2016), “Students’perception of the
accounting profession”,International Symposium and Exhibition on Business and Accounting.
Kim, H.J., Hong, A.J. and Song, H.D. (2019), “The roles of academic engagement and digital readiness
in students’achievements in university e-learning environments”,International Journal of
Educational Technology in Higher Education, Vol. 16 No. 1, p. 21.
Kirstein, M., Coetzee, S. and Schmulian, A. (2019), Differences in Accounting Students’Perceptions of
Their Development of Professional Skills, Higher Education, Skills and Work-Based Learning.
Kline, R.B. (2015), Principles and Practice of Structural Equation Modeling, Guilford Publications,
New York.
Kunter, M., Klusmann, U., Baumert, J., Richter, D., Voss, T. and Hachfeld, A. (2013), “Professional
competence of teachers: effects on instructional quality and student development”,Journal of
Educational Psychology, Vol. 105 No. 3, pp. 805-820, doi: 10.1037/a0032583.
Lee, J.S. (2014), “The relationship between student engagement and academic performance: is it a
myth or reality?”,The Journal of Educational Research, Vol. 107 No. 3, pp. 177-185.
Lee, J., Song, H.D. and Hong, A.J. (2019), “Exploring factors, and indicators for measuring students’
sustainable engagement in e-learning”,Sustainability, Vol. 11 No. 4, p. 985.
Lei, H., Cui, Y. and Zhou, W. (2018), “Relationships between student engagement and academic
achievement: a meta-analysis”,Social Behavior and Personality: An International Journal,
Vol. 46 No. 3, pp. 517-528, doi: 10.2224/sbp.7054.
Lester, D. (2014), “College student stressors, depression, and suicidal ideation”,Psychological Reports,
Vol. 114 No. 1, pp. 293-296.
Lin, H.M., Chen, W.J. and Nien, S.F. (2014), “The study of achievement and motivation by e-learning-a
case study”,International Journal of Information and Education Technology, Vol. 4 No. 5, p. 421.
Low, M., Davey, H. and Hooper, K. (2008), “Accounting scandals, ethical dilemmas and educational
challenges”,Critical Perspectives on Accounting, Vol. 19 No. 2, pp. 222-254, doi: 10.1016/j.cpa.
2006.05.010.
Marriott, P. (2017), Accounting Education–Dedicated Edition, Taylor & Francis, London.
Maskell, E.C. and Collins, L. (2017), “Measuring student engagement in UK higher education: do
surveys deliver?”,Journal of Applied Research in Higher Education, Vol. 9 No. 2, pp. 226-241,
doi: 10.1108/jarhe-11-2015-0082.
JARHE
Mohammadyari, S. and Singh, H. (2015), “Understanding the effect of e-learning on individual
performance: the role of digital literacy”,Computers and Education, Vol. 82, pp. 11-25, doi: 10.
1016/j.compedu.2014.10.025.
Murphy, V.A. (2018), “Commentary: socio-economic status, young language learning, and the weapon
to change the world”,System, Vol. 73, pp. 89-93.
Northey, G., Govind, R., Bucic, T., Chylinski, M., Dolan, R. and van Esch, P. (2018), “The effect of ‘here
and now’learning on student engagement and academic achievement”,British Journal of
Educational Technology, Vol. 49 No. 2, pp. 321-333, doi: 10.1111/bjet.12589.
Nystrand, M. and Gamoran, A. (1990), Student Engagement: When Recitation Becomes the
Conversation, Office of Educational Research and Improvement (ED), Washington.
O’Brien, H.L. and Toms, E.G. (2008), “What is user engagement? A conceptual framework for defining
user engagement with technology”,Journal of the American Society for Information Science and
Technology, Vol. 59 No. 6, pp. 938-955, doi: 10.1002/asi.20801.
Okoro, J. (2013), “Assessment of accounting competencies possessed by postgraduate university
business education students to handle entrepreneurship business challenges in Nigeria”,World
Journal of Education, Vol. 4 No. 1, pp. 1-10, doi: 10.5430/wje.v4n1p1.
Ong, M.H.A. and Puteh, F. (2017), “Quantitative data analysis: choosing between SPSS, PLS and
AMOS in social science research”,International Interdisciplinary Journal of Scientific Research,
Vol. 3 No. 1, pp. 14-25.
Owsen, D. (2000), “Commentary on: ’Popular television formats, the student-as- consumer metaphor,
acculturation and critical engagement in the teaching of ac- counting”,Accounting Education,
Vol. 9 No. 4, pp. 389-393, doi: 10.1080/09639280010025591.
Palacios-Marqu
es, D., Cort
es-Grao, R. and Carral, C.L. (2013), “Outstanding knowledge competencies
and web 2.0 practices for developing successful e-learning project management”,International
Journal of Project Management, Vol. 31 No. 1, pp. 14-21, doi: 10.1016/j.ijproman.2012.08.002.
Pallant, J. (2020), SPSS Survival Manual: A Step by Step Guide to Data Analysis Using IBM SPSS,
Routledge, London.
Pan, G. and Seow, P.S. (2016), “Preparing accounting graduates for digital revolution: a critical review
of information technology competencies and skills development”,Journal of Education for
Business, Vol. 91 No. 3, pp. 166-175, doi: 10.1080/08832323.2016.1145622.
Parsons, S.A., Malloy, J.A., Parsons, A.W., Peters-Burton, E.E. and Burrowbridge, S.C. (2018), “Sixth-
grade students’engagement in academic tasks”,The Journal of Educational Research, Vol. 111
No. 2, pp. 232-245.
Preacher, K.J. and Kelley, K. (2011), “Effect size measures for mediation models: quantitative strategies
for communicating indirect effects”,Psychological Methods, Vol. 16 No. 2, pp. 93-115, doi: 10.
1037/a0022658.
Prom
ıs, P. (2008), “Are employers asking for the right competencies? A case for emotional
intelligence”,Library Leadership and Management, Vol. 1, p. 22.
Rajaram, R. and Singh, A.M. (2018), “Competencies for the effective management of legislated
business rehabilitations”,South African Journal of Economic and Management Sciences, Vol. 21
No. 1, pp. 1-9, doi: 10.4102/sajems.v21i1.1978.
Reschly, A.L. and Christenson, S.L. (2019), “The intersection of student engagement and families: a
critical connection for achievement and life outcomes handbook of student engagement
interventions”,Elsevier, pp. 57-71.
Rigdon, E.E., Ringle, C.M. and Sarstedt, M. (2010), “Structural modeling of heterogeneous data with
partial least squares”,Review of Marketing Research.
Ringle, C.M., Wende, S. and Becker, J.M. (2015), available at: http://www.smartpls.com.
Rodgers, T. (2008), “Student engagement in the e-learning process and the impact on their grades”,
International Journal of Cyber Society and Education, Vol. 1 No. 2, pp. 143-156.
Unleashing
the role of
e-learning
Ross, K. and Anderson, T. (2013), “Deciding what kind of course to take: factors that influence
modality selection in accounting continuing professional development”,Knowledge
Management and E-Learning, Vol. 5 No. 2, pp. 137-152.
Rufino, H., Payabyab, R.G. and Lim, G.T. (2017), “Competency requirements for professional
accountants: basis for accounting curriculum enhancement”,SSRN Electronic Journal, Vol. 7,
doi: 10.2139/ssrn.3172508.
Sansone, D. (2019), “LGBT students: new evidence on demographics and educational outcomes”,
Economics of Education Review, Vol. 73, p. 101933.
Schmidt, J.A., Rosenberg, J.M. and Beymer, P.N. (2018), “A person-in-context approach to student
engagement in science: examining learning activities and choice”,Journal of Research in Science
Teaching, Vol. 55 No. 1, pp. 19-43, doi: 10.1002/tea.21409.
Shah, V. and Shah, A. (2018), “Relationship between student perception of school worthiness and
demographic factors”,Frontiers in Education, Vol. 3, p. 45, Frontiers.
Shaheen, F., Ahmad, N., Waqas, M., Waheed, A. and Farooq, O. (2017), “Structural equation modeling
(SEM) in social sciences and medical research: a guide for improved analysis”,International
Journal of Academic Research in Business and Social Sciences, Vol. 7 No. 5, pp. 132-143, doi: 10.
6007/ijarbss/v7-i5/2882.
Shanmugapriya, S. and Subramanian, K. (2016), “Developing a PLS path model to investigate the
factors influencing safety performance improvement in construction organizations”,KSCE
Journal of Civil Engineering, Vol. 20 No. 4, pp. 1138-1150, doi: 10.1007/s12205-015-0442-9.
Shernof, D.J., Ruzek, E.A., Sannella, A.J., Schorr, R.Y., Sanchez-Wall, L. and Bressler, D.M. (2017),
“Student engagement as a general factor of classroom experience: associations with student
practices and educational outcomes in a university gateway course”,Frontiers in Psychology,
Vol. 8, p. 994, doi: 10.3389/fpsyg.2017.00994.
Small, R. (2018), “Equipping accountants with the skills that are needed in a digitally disruptive
world”,Professional Accountant, Vol. 33, pp. 18-19.
Soni, S. and Dubey, S. (2018), “Towards systematic literature review of E-learning”,International
Journal of Scientific Research in Computer Science, Engineering and Information, Vol. 3 No. 3,
pp. 1389-1396.
Stone, G., Fiedler, B.A. and Kandunias, C. (2014), “Harnessing facebook for student engagement in
accounting education: guiding principles for accounting students and Educators”,Accounting
Education, Vol. 23 No. 4, pp. 295-321, doi: 10.1080/09639284.2014.908730.
Szadziewska, A., Spigarska, E. and Januszewski, A. (2017), “Analysis of the curricula at economic
colleges and universities in Poland in finance and accounting”,Paper Presented at the 10th
International Conference of Education, Research and Innovation.
Tan, L.M. and Laswad, F. (2018), “Professional skills required of accountants: what do job
advertisements tell us?”,Accounting Education, Vol. 27 No. 4, pp. 403-432, doi: 10.1080/
09639284.2018.1490189.
Tarka, P. (2018), “An overview of structural equation modeling: its beginnings, historical
development, usefulness and controversies in the social sciences”,Quality and Quantity,
Vol. 52 No. 1, pp. 313-354, available at: https://dx.doi.org/10. 1007/s11135-017-0469-8.
Taylor, M., Marrone, M., Tayar, M. and Mueller, B. (2018), “Digital storytelling and visual metaphor
in lectures: a study of student engagement”,Accounting Education, Vol. 27 No. 6, pp. 552-569,
doi: 10.1080/09639284.2017.1361848.
Tenenhaus, M. (1999), “L’approche pls”,Revue de Statistique Appliqu
ee, Vol. 47 No. 2, pp. 5-40.
Thill, M., Rosenzweig, J.W. and Wallis, L.C. (2016), “The relationship between student demographics
and student engagement with online library instruction modules”,Evidence Based Library and
Information Practice, Vol. 11 No. 3, pp. 4-15, doi: 10.18438/B8992D.
Thomas, C.L., Pavlechko, G.M. and Cassady, J.C. (2019), “An examination of the mediating role of
learning space design on the relation between instructor effectiveness and student
JARHE
engagement”,Learning Environments Research, Vol. 22 No. 1, pp. 117-131, doi: 10.1007/s10984-
018-9270-4.
Torsney, B.M. and Symonds, J.E. (2019), “The professional student program for educational resilience:
enhancing momentary engagement in classwork”,The Journal of Educational Research,
Vol. 112 No. 6, pp. 676-692, doi: 10.1080/00220671.2019.1687414.
Watkin, C. (2000), “Developing emotional intelligence”,International Journal of Selection and
Assessment, Vol. 8 No. 2, pp. 89-92, doi: 10.1111/1468-2389.00137.
White, S. (2007), “Critical success factors for e-learning and institutional change-some organizational
perspectives on campus-wide e-learning”,British Journal of Educational Technology, Vol. 38
No. 5, pp. 840-850.
Wyness, L. and Dalton, F. (2018), “The value of problem-based learning in learning for sustainability:
undergraduate accounting student perspectives”,Journal of Accounting Education, Vol. 45,
pp. 1-19, doi: 10.1016/j.jaccedu.2018.09.001.
Yanto, H. (2016), “Internationalizing the accounting graduates’competencies through the
improvement of student engagement”, Available at SSRN 2913206, pp. 527-537.
Yanto, H., Mula, J.M. and Kavanagh, M.H. (2011), “Developing student’s accounting competencies
using Astin’s IEO model: an identification of key educational inputs based on Indonesian
student perspectives”,Proceedings of the RMIT Accounting Educators’Conference.
Zhang, S. and Liu, Q. (2019), “Investigating the relationships among teachers’motivational beliefs,
motivational regulation, and their learning engagement in online professional learning
communities”,Computers and Education, Vol. 134, pp. 145-155.
Further reading
Beatson, N., Gabriel, C.A., Howell, A., Scott, S., van der Meer, J. and Wood, L.C. (2020), “Just opt-in: how
choosing to engage with technology impacts business students’academic performance”,
Journal of Accounting Education, Vol. 50, 100641, doi: 10.1016/j.jaccedu.2019.100641.
Corresponding author
Munther Al-Nimer can be contacted at: muntheralnimer@hu.edu.jo
For instructions on how to order reprints of this article, please visit our website:
www.emeraldgrouppublishing.com/licensing/reprints.htm
Or contact us for further details: permissions@emeraldinsight.com
Unleashing
the role of
e-learning
