survey methodology report

Project Survey Methodologie
Objective:
Analyze a survey and writing the report
(Using SPSS, or other software).
Plan:
1. Title
2. Abstract
3. Introduction to the Study
4. Review of Preliminary Research
5. Method of Research
6. Survey Research Findings
7. Conclusion
8. References
9. Appendixes (Questionnaires and responses)
Group
Student ID#
1
2
3
4
5
Student name
Quality & Quantity
https://doi.org/10.1007/s11135-020-01028-z
Effects of COVID‑19 in E‑learning on higher education
institution students: the group comparison between male
and female
Arfan Shahzad1 · Rohail Hassan1
Rab Nawaz Lodhi2
· Adejare Yusuff Aremu1 · Arsalan Hussain1 ·
© Springer Nature B.V. 2020
Abstract
In response to the emerging and ever solution to the COVID-19 outbreak. This study proposes a theoretical framework based on literature and model to determined E-learning portal success. The study compared males and females to E-learning portal usage. The study
objective is to check the difference between male and female E-learning portals’ accessibility among the students’ perspective. The study included service quality, system quality, information quality, user satisfaction, system use, and E-learning portal success. The
empirical data of 280 students participated from the different universities of Malaysia
through google surveys analyzed using the Partial Least Squares Structural Equation Modelling. The study further divided the full model into two domains, which are female and
male. In the male model, information quality and system quality have direct relationships
with user satisfaction. Information quality also supported the relationship with system use.
At the same time, there is a positive relationship between user satisfaction and E-learning portals. Likewise, in the female model, E-service quality and Information quality both
are supported by system use and user satisfaction. Similarly, system quality has a positive
relationship with user satisfaction, and user satisfaction has a positive relationship with
E-learning portals. The study will be further helpful for the Malaysian universities policy-makers such as top management, ministry of higher education, Malaysian universities
union in designing the policies and programs on E-learning Portal Success in the country.
The findings of the study reveal that males and females have a different level of in terms of
usage of towards E-learning portals in Malaysian Universities.
Keywords E-learning portal · System quality · Information quality · Higher education
institution · Gender
* Rohail Hassan
rohail.hassan@uum.edu.my
Extended author information available on the last page of the article
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A. Shahzad et al.
1 Introduction
In the 21st century, at the end of 2019 in Wuhan, the high technology business hubs of
China experience an epidemic of an entirely distinctive coronavirus appeared that had
killed a few thousand Chinese within the fifty days of spreads and thousands of other citizens are suffered. The novel virus was nominated as COVID-19 novel coronavirus by the
Chinese scientists (Shereen et al. 2020). Later on, in a shorter period, this COVID-2019
spread worldwide. Several country’s economies are severely affected due to COVID2019. Further, the outbreak has changed the operating conditions all over the globe within
a month. The consequences of a pandemic are unstoppable and uncontrollable for many
industries of the world. Later on, almost 120 countries have stopped face-to-face learning;
approximately a billion students’ education is effected worldwide with COVID-19. Most
of the higher education system is operating through the E-learning (Azzi-Huck and Shmis
2020; Shahzad et al. 2020a, b). Meanwhile, to tackle the COVID-19 pandemic, almost
all the world, and including Malaysian higher education ministry, has issued the ordered
to close the public school and higher education closure as an emergency measure to stop
spreading the infection.
Technologies have changed the traditional way of education to the modern way of learning, like artificial intelligence (Di Vaio et al. 2020a). Thus E-learning is covered under a
larger term of technology-based learning through websites, learning portals, video conferencing, YouTube, mobile apps, and thousand types of free available websites for blended
learning tools. Currently, E-learning is enhancing students’ knowledge, even the academic
staff and professional and industry people skills through the internet (Adams et al. 2018;
Chopra et al. 2019). Most of the higher education universities are providing online courses
for their students within and off campuses. In Malaysia, the Government is providing many
resources to higher education. Based on the news reports, the Malaysian universities, colleges, polytechnics are using Massive Open Online Courses (MOOCs). The growth of the
online education market is expected 16.4% annually over the forecast period, 2016–2023.
With the massive growth of the internet, maybe university teaching and learning models
will be changed in 10 to 15 years. Thus, it based on male and female students on the use of
the E-learning portal. This study focuses on comparisons between male and female counterparts on E-learning portal usage among university students during the COVID-2019
period.
Globally, due to COVID-19 outbreak universities closed and lockdown, most teachers and students are happy by the move online education. The faculty members of worldrenowned universities have begun to get online instructor certifications to deliver online
teaching to their students. At the same time, faculty and staff members are learning how to
use online learning platforms. Previous, they are using only the delivery through face-toface teaching. However, the shift to online mode has raised many queries on the quality of
education (Sahu 2020).
Furthermore, the quality of education and excellent infrastructures such as computers and IT modern equipment reception are now in massive demand and universities are
changing their teaching models with the use of intellectual capital (Alvino et al. 2020; Di
Vaio et al. 2020b). Thus, an unexpected shift from face-to-face learning to online, there are
few difficulties faced by students and lectures. Moreover, most of the countries significant
issues with technological infrastructure in rural areas; thus, the standard of online education may be a critical issue that needs essential focus. Therefore, based on the above-said
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Effects of COVID‑19 in E‑learning on higher education institution…
issues, this study tries to investigate the impact of male and female students on the use of
the E-learning portal.
In previous literature, the D&M model is tested on the overall population, like banks
and other financial sectors. In the current study, the whole population is divided into males
and female categories to hoped different theoretical contributions by having a division of
the population. Consequently, universities in Malaysia are offering an online course to the
students on campus and off-campus. The current study focuses on the male and female user
satisfaction and E-learning system use toward the E-learning portal success of the Malaysian universities. Based on many researchers (Cidral et al. 2018; Selvaraj 2019) claimed
that user satisfaction and E-learning system use have a significant impact on success. This
study will conduct on the students how are enrolled with Malaysian universities and using
the E-learning Portal for their learning. Based on the argument so far, there is a still gap
in literature the E-learning system among universities after the spread of the Covid-19
outbreak on higher education closure. The purpose of the present study to investigate the
effect of information quality, system quality, and service quality toward user satisfaction
and E-learning system use impact on the E-learning portal success. Therefore, the study
focuses on group comparison between male and female students on the E-learning portal
uses.
This paper contains five major sections. Section 2 briefly described the compiling
review of previous literature that how the learning curve has shifted towards online portals
by using DeLone and McLean Model. In Sect. 3, a description of the research method and
the overall process of data collection and analysis were the main focus. Section 4 relates to
the Multi-Group Analysis (MGA) and the interpretation of the result. Lastly, Sect. 5 concluded with a holistic discussion related to the male and female groups of the study.
2 Literature review
2.1 Learning shifting toward online
Toward the end of February, as alerts sounded on the increasing spread of the COVID19 infection, the World Bank built up a multi-sectoral worldwide task force team to help
nation reaction and adapting measures. At that point, just China and some other influenced
nations were upholding social distance through the closure of schools. In the meantime,
following fourteen days after the fact, 120 countries have closed schools impacting almost
a billion students across the world that have experience closures of their schools for the
period (Azzi-Huck and Shmis 2020). In this light, the COVID-19 pandemic has forced the
universities to close face-to-face education and send students home. This force the universities to introduce courses through online portals. Also, education industries are adopting
the technologies available such as digital Video conferencing platforms like Zoom, Microsoft platform, and Webex Blackboard and Google Classroom (Larry 2020). Therefore, this
will be enhancing E-learning globally (Chen 2010; Yengin et al. 2011; Larry 2020).
Therefore, the current study concentrating on the comparison between male and female
on E-learning. In this light, applying remote learning and education resources to mitigate
the loss of learning: In web-based technology, Electron Learning is well-knowns as well
as the earliest application (Azhari and Ming 2015). In today the E-learning is getting very
popular worldwide. E-learning is described as the delivery of learning through technology and the internet (Gros et al. 2016; Hong et al. 2017; Aljawarneh 2020). Almost all
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A. Shahzad et al.
the universities and colleges have developed the E-learning portal of their students and
faculties (Moore et al. 2011). In the 21st century, the E-learning creates a more significant
impact on all types of the student, much as the part-time and Full-time or distance learning
student in the higher education institution (Azhari and Ming 2015). Nowadays, the majority
of the postgraduate students are registered as a part-time student, because they are working
in the companies. E-learning helps them a lot because of their time constrain. The advancement in E-learning has been started through Massive Open Online Courses (MOOCs) for
students, society, and the industry as well since 2012 (Calisir et al. 2014; Margaryan et al.
2015). MOOCs are recognized as a significant development in higher education million of
the peoples and student are taking the benefits and uplifting the existing skill (Gupta and
Gupta 2020). Moreover, in recent decades, several Malaysian Universities have adopted the
E-learning portals (Hussin et al. 2009; Paechter and Maier 2010). Based on the research of
Azhari and Ming (2015) highlighted several issues related to the Learning Management
System (LMS) of the Malaysian universities such as the lack of trained lectures, slow down
of the internet speed, WIFI coverage, infrastructure, the interface of design, quality of content, system use and students’ adoption. In the present research, the comparison between
male and female students is measured based on E-learning portal success. Meanwhile, the
researchers will find out the importance of E-learning tools’ success in terms of male and
female Malaysian student perspectives.
2.2 Model of DeLone and McLean model (D&M)
D & M model ISS success model has gained significant attention from researchers in the
field of information systems. In 1992, this model was initially developed by DeLone and
McLean to measure the dependent construct of IS success (DeLone and McLean 1992)
was primarily based on following three aspects: (Shannon and Weaver 1949) study on
communications, Taxonomy of (Mason 1978) measuring information output and Research
work on information system during that period. There are three levels of communication
per (Shannon and Weaver 1949) First Level: Technical: (accuracy of information system)
Second level: Semantic (success of right information conveyed to the right receiver) Third
level: Effectiveness (influence of information on the receiver). The information success
model (1992) has discussed the six dimensions, such as information quality, system quality, system use, user satisfaction, and organizational impact. After one decade, the author
modified the original model by adding service quality dimension and the end replaced the
individual impact and organizational impact with the net benefits. Figure 1 showed the
original D&M Model having Six interdependent dimensions (DeLone and McLean 1992;
Mohammadi 2015). In this model, the “system Quality” construct depicts “technical success.” In contrast, the “Information quality” variable demonstrate “semantic success,”
Fig. 1  Updated DeLone and
McLean IS Success Model
(D&MISSM) Source: Delone
and McLean (2003)
Information
Quality
System
Quality
Service
Quality
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Intention to
Use/Use
User
Satisfaction
Net benefits
Effects of COVID‑19 in E‑learning on higher education institution…
while the other four elements “use,” “user satisfaction,” “individual impact,” and “organizational impact” show “effectiveness success.” Therefore, this study focus on male students’ comparison with female students on the E-learning portal.
As time passed, the researcher used D&M Model to check the success of the information communication system. Many studies suggested to add the new dimensions of D&M
model, and some have recommended to include the such as “service quality,” “workgroup
impact,” “consultant/vendor quality” in the D & M model (Gable et al. 2003; Ifinedo
2006). While the few researchers criticize D & M Model “use” and “user satisfaction,”
dimensions. Many studies have changed the number of aspects according to their context,
but no study considers organization capabilities mediating role in the relation between
alone impact and organizational impact. In this light, a comparison of males and females
among university students to determine the E-learning portal.
The study of Ifinedo (2011) highlights the importance of the system quality creates a
great impact on the E-learning portal. The system quality of the E-learning will generate
query results more quickly. Moreover, system quality will increase the interest of the enduser. Also, the user-friendly interface and modern graphical interface increase the level of
user satisfaction. The study of Petter et al. (2013) highlighted that service provider should
adopt the new changes and modify time to time the system. Also, the system quality construct has been used in the context of the E-learning system, which depicts whether the
user is satisfied with the quality of the E-learning portal. “Perceived ease of use” is the
most used measure of “system quality” (Davis, 1989).
Furthermore, SQ has been identified as ease of learning, ease of use, the convenience of
access, the usefulness of system features, system complexity, system features, and response
time of information system (Beheshti and Beheshti 2010). Furthermore, as suggested by
Sedera et al. (2013), SQ means ease of learning, efficient, consistent, modifiable, efficient,
personalizable, meets the requirements, ease of use, and reliable. In addition, (DeLone and
McLean 2016) acknowledged system quality by measures: ease of use, availability, flexibility, reliability, usefulness, and response time. There are certain modifications in standards
of system quality that occurs over time. However, some measures remain the same and
consistently being applied and validated, which are as follows: the luxury of use, the comfort of learning, reliability, personalizabality, reply-time, availability, system Interactivity,
and system security. Moreover, the study concentrates on the male and female comparison.
The IQ depicts the precision as well as the accuracy of the information provided by
the E-learning system; timeliness is another important indicator of information quality that
information should be generated within time and latest. So, that higher management can
make quick decisions, sufficiency is another characteristic of information quality that it
should not be insufficient and must contain all information required to the user. Understandability is a very effective characteristic of the IQ construct that it should be easy to
understand and should not be complex that difficult to grasp. Conciseness is another vital
part of information quality produced by the E-learning system, system in any organization (Petter et al. 2013; Cai and Zhu 2015; Chiang et al. 2019). Additionally, Information
quality demonstrates the output characteristics of the information system that it is proving
the timely information to all the departments of the organization; the information should
be relevant to the particular user or the department. The required information is available
at the right time to the right person; the data’ provided by the information system should
be understandable to the users (Ifinedo, 2014; Muda and Erlina 2019; Pham et al. CR55).
Therefore, the comparison of male and female students on the E-learning portal is the target of this study.
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A. Shahzad et al.
Furthermore, the main purpose of information quality is to provide users online
knowledge with correct relevant information on 24/7. So, it must be considered active
for the E-learning portal success. Information quality in past literature considered being
part of user satisfaction measurement. It is not treated as a separate variable further
argued that information quality measures vary depending upon the type of information system success going to be evaluated. However, there are consistent measures of
information quality for E-learning success as follows: Relevance, Usefulness, Understandability, Accuracy, Reliability, Currency, Completeness, Timeliness. Based on the
comparison of male and female university students. Service quality refers to “responsiveness,” which is a very important indicator. It means how efficiently the technical
department responds to the queries of users. Also, empathy is another characteristic of
Service quality of how attentively they are helping the users (Petter et al. 2013; Haryaka
et al. 2017; Xu and Du 2018).
Moreover, in the D&M Model, service quality was neglected and updated in D&M
(2013). IS Success Model. Many items have been stated to measure E-learning system
service quality but mostly cited instrument developed by authors (Parasuraman et al.
1986; Parasuraman et al. 1988; Parasuraman et al. 2002). Initially, ten dimensions were
used to measure service quality, which later transformed into five named; tangibles,
reliability, responsibility, assurance, and empathy. This updated model of D&M Model
found very useful for evaluating the success of different types of technologies related
application. Most of the studies used this model of an information system, ERP system
success, e-procurement application, e-government application on the user perspective,
E-banking application use success, and also several other online application successes
business (Hsu et al. 2015; Almarashdeh 2016; Aparicio et al. 2017; Ojo 2017; Cidral
et al. 2018; Aparicio et al. 2019). E-learning involves the usage of modern technology
to impart learning; thus, the present study needs to investigate how the end-user accepts
much E-learning portal success. Therefore, the present study highlighted the comparison between male and female students on the E-learning portal. Thus, Fig. 2 has shown
the research framework of the present study and hypothesis.
H1 E-service quality has a significant positive impact between males and females on the
System USE.
H2 E-service quality has a significant positive impact between males and females on
user satisfaction.
System
Quality
System USE
Information
Quality
Service
Quality
E-learning
Portal
User
Satisfaction
Fig. 2  The research Framework of the present study
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Effects of COVID‑19 in E‑learning on higher education institution…
H3 Information quality has a significant positive impact between males and females on
System USE.
H4 Information quality has a significant positive impact between males and females on
user satisfaction.
H5 System quality has a significant positive impact between males and females on System USE.
H6 System quality has a significant positive impact between males and females on user
satisfaction.
H7 System USE has a significant positive impact between males and females on the
E-learning portal success.
H8: User satisfaction has a significant positive impact between males and females on the
E-learning portal success.
3 Methodology
3.1 Instrument development
Based on previous research, the current study has developed a survey instrument. The
questionnaire was adopted/adapted and reworded in the form of E-learning. All items used
a 5point Likert scale ranging from 1 to 5 from “strongly disagree” to “strongly agree.”
The questionnaire items of system quality, information quality, system usage, satisfaction
user was adopted from (McGill, Hobbs and Klobas, 2003; Rai, Lang and Welker, 2002).
The E-learning portal success survey instrument items were adopted from (Freeze et al.
2010). All items of E-learning portal success were used a 5-point Likert scale ranging from
1—”Poor,” 2—”Fair,” 3—”Satisfactory,” 4—”Very good” to 5—” Excellent.”
3.2 Data collection
The research used a quantitative approach; the survey was conducted through the
GOOGLE form; the links were shared with the student through the WhatsApp group of
lecturers. All the Malaysian University student is participating in the E-learning portal survey. The convenience sampling technique was used. The study employed a cross-sectional
survey method. In Table 1, the students of undergraduate 212 (75.7%) and Postgraduate 68
(24.3%) have participated in the online survey conducted from August to September 2019.
Table 1  Student Participated in
the Survey based on the Program
enrolled
Program enrolled
Total
Percent
Postgraduate
68
24.2
Undergraduate
Total
212
280
75.7
100
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A. Shahzad et al.
Table 2  Demographic Profile of
Respondents (N = 280)
Demographics respondents percentage (%)
Frequency
Percentage
90
190
32.1
67.9
18
212
36
14
6.4
75.7
12.9
5.0
30
151
99
10.7
53.9
35.0
Gender
Male
Female
Age-Group
Less than 20 years
21-30 years
31-40 Years
More than 41 years
Experience Using the E-learning portal
Less than 1 year
More than 1 to 2 years
More the 3
3.3 Data analysis
In Table 2, In the demographic analysis, the ‘gender’ factor showed that female participants
have a more response rate of 67.9% as compared to 32.1% who were male. In Malaysian
Universities is more enrolment of female as compared to male. Regarding the ‘age group’
of respondents, data revealed that the majority was having the age-group of respondents
21–30 years 212 (75.7%). Also, regarding the “Experience Using the E-learning portal,”
data showed that most of the students who participated in the survey they have experienced
using the E-learning portal is more than two years.
4 Data analysis in SmartPLS
To achieve the research objectives, the study has employed Partial Least Squares (PLS)
version 3.3.0 to facilitate data analysis. Based on the context of inferential analyses, Partial
Least Squares-Structural Equation Modelling (PLS-SEM) application has been applied in
several disciplines (Hair et al. 2016). These developments contribute to the growth of PLSSEM that is generally used as a research instrument in the field of management information
systems as well as the social sciences (Hair et al. 2016). Also, confirm the importance of
PLS’s ability to analyze variables in complex models, simultaneously. Smart PLS evaluates
two models, mainly, which are measurement and structural (Hair et al. 2016).
4.1 Internal consistency reliability
Internal consistency mentions the degree to which all indicators are different then each
other (sub) scale is evaluating an equivalent concept (Hair et al. 2016). Inline thereupon,
the composite reliability score value must be higher than 0.70, and the AVE score value to
quite 0.50 (Hair et al. 2013, 2016). Therefore, it explained in Table 3 below, all the constructs included in the present study have AVE, and composite reliability (CR) is above the
criteria above said, which may be a suggestion of measurement model reliability. While
Table 3 stated that the average variance extracted (AVE) and CR values of all variables are
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Effects of COVID‑19 in E‑learning on higher education institution…
Table 3  Assessment Result for the measurement model
Assessment Result for the measurement model
Construct/Associated Items
Loading
CR
Reflective
Full model Female Male Full Model Female Male Full Model Female Male
E-Learning Portal Success
ELP1
ELP2
ELP3
ELP4
E Service Quality
ESQ1
ESQ2
ESQ3
ESQ4
ESQ7
ESQ8
Information
Quality
IQ1
IQ3
IQ4
IQ5
System Quality
SQ1
SQ2
SQ3
SQ4
SQ5
System USE
SU1
SU2
SU3
User Satisfaction
USAT1
USAT2
USAT3
USAT4
0.942
0.941
0.943
0.932
0.940
0.943
0.942
0.943
0.944
0.935
0.942
0.906
0.885
0.886
0.867
0.885
0.871
0.892
0.880
0.879
0.854
0.869
0.844
0.866
0.895
0.901
0.892
0.916
0.911
0.937
0.891
0.895
0.916
0.903
0.892
0.916
0.913
0.903
0.894
0.862
0.919
0.904
0.865
0.918
0.892
0.887
0.879
0.853
0.919
0.888
0.890
0.883
0.889
0.914
0.896
0.888
0.874
0.910
0.884
0.614
0.924
0.887
0.577
0.872
0.875
0.689
0.905
0.907
0.896
0.937
0.877
0.898
0.892
0.931
0.945
0.919
0.905
0.949
AVE
0.968
0.969
0.963 0.883
0.888
0.868
0.954
0.947
0.966 0.776
0.749
0.826
0.945
0.948
0.942 0.812
0.821
0.801
0.949
0.949
0.951 0.789
0.787
0.796
0.851
0.848
0.856 0.662
0.658
0.667
0.951
0.945
0.962 0.830
0.810
0.864
in an appropriate range. It shows that all the constructs of the female and male groups have
AVE’s and CR value are above the threshold values, which proved the reliability of the
measurement model (Figs. 3, 4, 5).
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0.595
0.858
System USE_
User Satisfaction
E Service Quality
0.838
0.516
0.840
Information Quality
System Quality
System USE_
User Satisfaction
0.754
0.438
0.774
0.932
0.727
0.506
0.806
0.551
0.895
0.852
Information
Quality
0.830
0.666
0.906
0.729
Information
Quality
0.857
0.625
0.901
0.772
Information
Quality
0.860
0.892
0.513
System Quality
0.765
0.887
0.558
System Quality
0.797
0.888
0.540
System Quality
0.710
0.565
0.817
System USE_
0.811
System USE_
0.655
0.814
System USE_
The bolded diagonal shown in Table 4 represent the square route of average whilst those of the diagonal represent latent variable correlations
0.762
0.820
E-Learning Portal Success
0.909
E-Learning Portal
Success
0.820
User Satisfaction
E Service Quality
0.706
0.649
System USE_
0.674
0.746
System Quality
0.942
0.617
0.818
0.624
E-Learning Portal
Success
Information Quality
0.865
E Service Quality
0.720
0.484
0.704
0.940
0.658
E-Learning Portal
Success
E-Learning Portal Success
E Service Quality
0.777
System Quality
Female
0.663
0.817
Information Quality
0.881
E-Learning Portal Success
E Service Quality
E Service Quality
Table 4  Discriminant Validity full model
0.930
User Satisfaction
0.900
User Satisfaction
0.911
User Satisfaction
A. Shahzad et al.
Effects of COVID‑19 in E‑learning on higher education institution…
4.2 Discriminant validity
In the present study, also check the discriminant validity criterion, which measures
the degree to which a variable is not equivalent to other constructs (Hair et al. 2016).
According to Fornell & Lacker criterion explained that the higher level of discriminant validity proposes that constructs are differents than the respective variables and
not explaining some phenomena. The present study performed the discriminant validity
taking the square root of AVE of the constructs. Thus, the values are higher than the
correlations among latent constructs (Hwang and Min 2015). Therefore, in Table 4, the
present study of male and female and full model having no issues with discriminant
validity (Naala et al. 2017).
4.3 Assessment of measurement invariance
The measure two sets of university students, male and female, the study applies an
invariance test. At the same, it is vital to conduct the invariance test before conducting a
multi-group analysis. Thus, the purpose is to determine “whether, under different conditions of observing and studying phenomena, measurement models yield measures of the
same attribute” (Henseler et al. 2016). Afterward, the study follows three steps, namely,
Configural invariance, Compositional invariance, and Equality of composite means values and variances to test measurement invariance (Henseler et al. 2016).
Firstly, since the measurement models have the same number of constructs for both
groups, therefore the Male and female group data is established for configural invariance
(see Tables 3, 4). secondly, compositional invariance was measured engaging a permutation test. This assures that the composite scores are the same between the groups.
Lastly, Equality of composite variances and means values of groups were assessed.
Thus, the difference of the composite’s mean and variance ratio results, as shown in
Table 5, must fall within the 95% confidence interval. As indicated in Table 5, the result
reveals that each of the composite constructs has non-significant differences regarding
the composite mean and variances ratio. Moreover, in Table 5, full measurement invariance is depicted for Male and Female. Therefore, it is often deduced that the various
model estimations of Male and female groups students are not distinct in terms of content or usage of the E-learning portal.
4.4 Assessment of group differences
In the current study, the researchers have applied PLS-MGA in order to calculate the
differences by using Welch-Satterthwait Test on Male and female groups (Sarstedt et al.
2011). Furthermore, Table 6 is depicting the path coefficient and difference of the composite’s means. Meanwhile, several paths are found to be different in terms of male and
female data sets and significantly different. The present study found the E-service quality toward the system use male and female have a difference. On the otherwise information quality toward the system use having a difference between the male and female.
4.4.1 Assessment of female group
As per the female group assessment concerned in Table 6, hypothesis 1 predicted that ESQ
is positively significantly related to System USE with Path Coefficient of (0.285**), while
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A. Shahzad et al.
(p value > 0.05) not supporting hypothesis 1. Thus, hypothesis 2, demonstrate the significant
positive association among ESQ and US (β = 0.503, p value < 0.05) hypothesis 2 supported. Hypothesis 3 articulated that Information Quality is significant positively linked with System USE (β = 0.388, p value > 0.05), not supporting hypothesis 3. Equally, hypothesis 4, demonstrates a significant positive association with IQ and US (β = 0.287, p value < 0.05) supporting hypothesis 4. Furthermore, Hypothesis 5 predicted that System Quality is insignificant negatively associated with System USE (β = 0.063, p value > 0.05), not supporting hypothesis 5. Results
of hypothesis 6 demonstrate a significant positive association between System Quality and
User Satisfaction (β = 0.181, p value > 0.05), not supporting hypothesis 6. Hence, Hypothesis
7 articulated that system USE is significant positively linked with E-learning portal success
(β = 0.008, p value > 0.05), not supporting hypothesis 7. Finally, hypothesis 8, demonstrates
an insignificant negative association with User Satisfaction and E-learning portal success
(β = 0.701, p value > 0.05), not supporting hypothesis 8. Therefore, based on the above evidence, it can be concluded that female students more understand the important usage of
E-learning portal success in the content of the university. This implies that female students
hold the dominant positions on the usage of the E-learning portal.
4.4.2 Assessment of male group
Regarding male group assessment, Table 6 hypothesized that ESQ is negatively insignificantly
related to System USE with (β = 0.165, p value > 0.05) not supporting hypothesis 1. Next, with
hypothesis 2, that demonstrates an insignificant negative association between ESQ and US
(β = 0.204, p value < 0.05) hypothesis 2 supporting. While hypothesis 3 articulated that IQ is significant positively linked with System USE (β = 0.354, p value > 0.05), not supporting
hypothesis 3. Additionally, hypothesis 4, demonstrates a significant positive association with
Information Quality and User Satisfaction (β = 0.551, p value < 0.05) supporting hypothesis 4. Fig. 3  Full Measurement Model 13 Effects of COVID‑19 in E‑learning on higher education institution… Fig. 4  Female measurement Model Fig. 5  Male measurement Model Consequently, hypothesis 5 revealed that system quality is insignificant negatively associated with System USE (β = 0.074, p value > 0.05), not supporting hypothesis 5. Results of
hypothesis 6 demonstrate a significantly positive association between SQ and US (β = 0.220,
p value > 0.05), not supporting hypothesis 6. Thus, Hypothesis 7 articulated that System USE
is significant positively linked with E-learning Portal success (β = 0.017, p value > 0.05) not
13
A. Shahzad et al.
Fig. 6  Structure model
Fig. 7  Female Structure model
supporting hypothesis 7. Lastly, hypothesis 8, demonstrates an insignificant negative association with User Satisfaction and E-learning Portal success (β = 0.744, p value > 0.05) not
supporting hypothesis 8. Meanwhile, this implies that male students are low concerned
or understand the usage of E-learning portal success based on Path Coefficient results
(Table 7) and (Figs. 6, 7, 8).
13
Effects of COVID‑19 in E‑learning on higher education institution…
Fig. 8  Male Structure model
Table 5  Measurement invariance test using MICOM
Composite
C value (= 1)
95% confidence
Compositional
invariance
E Service Quality
1.000
[1.000, 1.000]
Yes
E-Learning Portal Sucess
Information Quality
System Quality
System USE_
User Satisfaction
1.000
1.000
1.000
0.992
1.000
[1.000, 1.000]
[1.000, 1.000]
[0.999, 1.000]
[0.996, 1.000]
[1.000, 1.000]
Yes
Yes
Yes
Yes
Yes
Composite
Difference of the composite’s
mean value (= 0)
95% confidence
Equal Mean
Values
E Service Quality
− 0.060
[− 0.209, 0.195]
Yes
E-Learning Portal Sucess
Information Quality
System Quality
System USE_
User Satisfaction
− 0.270
− 0.107
− 0.048
− 0.136
− 0.115
[− 0.219, 0.203]
[− 0.214, 0.201]
[− 0.216, 0.195]
[− 0.211, 0.212]
[− 0.210, 0.209]
No
Yes
Yes
Yes
Yes
Composite
Difference of the composite’s
mean value (= 0)
95% confidence
Equal variances
E Service Quality
− 0.336
[− 0.338, 0.368]
Yes
E-Learning Portal Sucess
Information Quality
System Quality
System USE_
User Satisfaction
0.011
− 0.166
− 0.068
0.054
− 0.320
[− 0.213, 0.252]
[− 0.305, 0.342]
[− 0.293, 0.344]
[− 0.325, 0.356]
[− 0.336, 0.334]
Yes
Yes
Yes
Yes
Yes
13
13
0.204
0.354**
0.551***
0.074
0.220**
0.017
0.744***
0.503***
0.388***
0.287***
0.063
0.181**
0.008
0.701***
E Service Quality → User
Satisfaction
Information Quality →
System USE_
Information Quality →
User Satisfaction
System Quality → System
USE_
System Quality → User
Satisfaction
System USE_ → E-Learning Portal Success
User Satisfaction →
E-Learning Portal
Success
0.165
0.285**
Path Coefficients Original
(Male)
E Service Quality →
System USE_
Path Coefficients Original
(Female)
Table 6  Assessment of group differences
10.429
0.099
2.997
0.662
3.700
3.337
6.925
2.253
9.783
0.192
2.127
0.413
5.171
2.381
1.608
1.090
− 0.043
− 0.010
− 0.038
− 0.011
− 0.264
0.034
0.298
0.120
0.318
0.476
0.383
0.487
0.031
0.431
0.029
0.268
p value
t-Value (Female) t-Value (Male) Path coefficient differ- Henseler’s
MGA
ences
0.348
0.471
0.368
0.477
0.025
0.431
0.015
0.284
p value
Permutation test
0.335
0.468
0.375
0.479
0.023
0.428
0.022
0.271
p value Welch
Satterthwaite
test
No/no
No/no
No/no
No/no
Yes/yes
No/no
Yes/yes
No/no
Supported
A. Shahzad et al.
0.229 **
0.389***
0.392***
0.388***
0.074
0.194**
0.023
0.705***
E Service Quality → System USE_
E Service Quality → User Satisfaction
Information Quality → System USE_
Information Quality → User Satisfaction
System Quality → System USE_
System Quality → User Satisfaction
System USE_ → E-Learning Portal Success
User Satisfaction → E-Learning Portal Success
p < 0.1, *p < 0.05, **p < 0.01*** Full Model Result Relationship Table 7  Comparison Analysis Supported Supported Supported Not Supported Supported Not Supported Supported Supported Decision 0.503*** 0.388*** 0.287*** 0.063 0.181** 0.008 0.701*** 0.285** Female Supported Supported Supported Not Supported Supported Not Supported Supported Supported Decision 0.204 0.354** 0.551*** 0.074 0.220** 0.017 0.744*** 0.165 Male Not Supported Supported Supported Not Supported Supported Not Supported Supported Not Supported Decision Effects of COVID‑19 in E‑learning on higher education institution… 13 A. Shahzad et al. 5 Results comparison In regard to the hypothesis testing on the female group, six (6) variables were, directly and indirectly, significant and supported to E-learning Portal Success. In the female students model, E-service quality and Information quality both are supported with system use and user satisfaction. Similarly, system quality has a positive relationship with user satisfaction, and user satisfaction has a positive relationship with E-learning portals. On the other hand, in male students, four (4) variables are significant. More precisely, information quality and system quality have direct relationships with user satisfaction. Information quality also supported the relationship with system use. At the same time, there is a positive relationship between user satisfaction and E-learning portals. In the full model, user satisfaction is significant in terms of information quality and service quality. Consequently, user satisfaction is also significant in terms of E-learning portals. Thus, E-learning portal usage is more towards female students in Malaysian Universities. 5.1 Theoretical implication Regarding theoretical implication, the adoption studies are numerously studied phenomena both at an individual (customer) as well as organizational (management) levels. Although, technology adoption studies like E-learning portals success among students is a rarely researched phenomenon, particularly in Malaysian universities. The present study is considered a pioneer in terms of E-portals implementation in universities to test empirical, theoretical relationships in terms of males and females. Mainly, it elaborated on the relationship between the variables such as E-learning Portal Success, ESQ, IQ, SQ, SU has a significant impact on US. This present study proposes and examined the factors that affect the E-learning portal by extending D & M theory to incorporate elements that are more associated with the IS Success and E-learning among university students. Additionally, D & M model is taken into account because the IS tangible factors that would help universities strengthen and enhance their services. The significant contribution of the present study is the behavior of model changes if we divide the full model into parts (Male and Females). The proposed that D and M models provide misleading out in terms of the full model. Thus, its theoretical relationship changes as the model divided into different subgroups. This confirms the effectiveness of the E-learning portals in different for male and female students of Malaysian Universities. 5.2 Practical implications The current study, in the context of Malaysian as well as other countries’ universities, has numerous implications related to E-learning Portals technology. The study will be further helpful for the Malaysian Government and university policy-makers such as top management, ministry of higher education, Malaysian universities union in designing the policies and programs on E-learning Portal Success in the country. At the same time, university top management/dean of faculty and HOD of the department need to concentrate on the importance of enhancing university quality education. The study further implies that females students of Malaysian universities are more focused on the E-learning portal as compare to male students. In the COVID-19 outbreak, the operational habits have changed around the globe within a short period, and mainly the education sector affected worldwide. In the 13 Effects of COVID‑19 in E‑learning on higher education institution… future, most of the universities will be offering online courses to the students. The COVID19 will create a long-term impact on higher education institutions. If the pandemic remains longer, it might change education from face to face to online. Based on that, the quality of the E-learning system, quality of information that will create an impact on user satisfaction and system use that will lead toward the E-learning portals. It will decrease the education cost, and education will reach outside the border as well. Education will be borderless in 5 to 10 years. This study recommends some of the suggestion to higher education institution such accessibility of the E-learning portal 24/7, error-free information, quality of information, content quality, the robustness of the server, training module materials related to E-learning portal use for new users, updated information, well-organized data, userfriendly design of the portal, and time to time feedback from the user will increase the durability and acceptability of the E-learning portal. 6 Conclusion This study focuses on comparisons between male and female counterparts on E-learning portal usage among university students. E-learning portal services adaptability in the among the student perspective of male and female. The present study highlight difference between the male and female success-fully use on the E-learning portal in Malaysia. While future studies will have to consider some variables like (Technology infrastructure support, system change perceived of use, perceived usefulness, user’s perception, and technical expertise), moreover, the further studies can be conducted by applying longitudinal design to empirically test the theoretical constructs by analyzing other countries’ universities. In the end, top management of universities is provided with recommendations for developing an understanding of the implication of E-service quality, information quality, system quality, system USE, and user satisfaction concerning the E-learning portal success. Acknowledgements The authors would like to thank the anonymous referees for providing helpful comments and suggestions which lead to the improvement of the paper. Funding This research received no external funding. Compliance with ethical standards Conflicts of interest The authors declare no conflicts of interest. 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Affiliations Arfan Shahzad1 · Rohail Hassan1 Rab Nawaz Lodhi2 · Adejare Yusuff Aremu1 · Arsalan Hussain1 · Arfan Shahzad arfan@uum.edu.my Adejare Yusuff Aremu akogunadejare@yahoo.com Arsalan Hussain arsalanhussaink9@gmail.com Rab Nawaz Lodhi rabnawaz.lodhi@ucp.edu.pk 1 Othman Yeop Abdullah Graduate School of Business (OYAGSB), Universiti Utara Malaysia (UUM), 06010 Sintok, Kedah Darul Aman, Malaysia 2 UCP Business School, University of Central Punjab, Lahore, Pakistan 13 Students’ perception of online learning during the COVID-19 pandemic: a survey study of Polish medical students Michał Bączek (  michal.baczek@ujk.edu.pl ) Institute of Medical Sciences, Jan Kochanowski University in Kielce, Kielce, Poland https://orcid.org/0000-0002-7652-2759 Michalina Zagańczyk-Bączek Institute of Medical Sciences, Jan Kochanowski University in Kielce, Kielce, Poland Monika Szpringer Institute of Medical Sciences, Jan Kochanowski University in Kielce, Kielce, Poland Andrzej Jaroszyński Institute of Medical Sciences, Jan Kochanowski University in Kielce, Kielce, Poland Beata Wożakowska-Kapłon Institute of Medical Sciences, Jan Kochanowski University in Kielce, Kielce, Poland Research Article Keywords: online learning, e-learning, COVID-19, pandemic, skills, knowledge DOI: https://doi.org/10.21203/rs.3.rs-41178/v1 License:   This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/14 Abstract Background The COVID-19 pandemic has disrupted teaching in a variety of institutions, especially in medical schools. Electronic learning (e-learning) became the core method of teaching the curriculum during the pandemic. After eight weeks of only online learning, a survey was conducted to investigate perception of this type of learning amongst medical students. Methods A survey was conducted by distributing an online questionnaire to Polish medical students. Data gathered from the survey was analyzed with routine statistical software. Results 804 students answered the questionnaire. According to respondents’ answers, the main advantages of online learning were the ability to stay at home (69%), continuous access to online materials (69%), learning at your own pace (64%), and comfortable surroundings (54%). The majority of respondents chose lack of interactions with patients (70%) and technical problems with IT equipment (54%) as the main disadvantages. There was no statistical difference between face-to-face and online learning in terms of opinions on the ability of the learning method to increase knowledge (p=.46). Elearning was considered less effective than face-to-face learning in terms of increasing skills (p