a model for technological aspect of e-learning readiness ... · successful e-learning system;...

A model for technological aspect of e-learning readinessin higher education

Asma Ali Mosa Al-araibi1 & Mohd Naz’ri bin Mahrin1&

Rasimah Che Mohd Yusoff1 & Suriayati Binti Chuprat1

Received: 15 March 2018 /Accepted: 6 November 2018 /Published online: 26 November 2018# Springer Science+Business Media, LLC, part of Springer Nature 2018

AbstractThe rate of adoption of e-learning has increased significantly in most higher educationinstitutions in the world. E-learning refers to the use of electronic media, educationaltechnology, also; information and communication technology (ICT) in the educationalprocess. The aim for adopting e-learning is to provide students with educationalservices via the use of ICT. Thus, students can access educational resources fromanywhere and at any time. However, the successful implementation of e-learning relieson the readiness to be able to initiate this system because, without proper readiness, theproject will probably fail. E-learning readiness refers to the assessment of how ready aninstitution is to adopt and implement an e-learning project. One of the most importantaspects of e-learning readiness is the technological aspect, which plays an importantrole in implementing an effective and efficient e-learning system. There is currently alack of arguments concerning the factors that shape the technological aspect of e-learning readiness. The focus of this study is concentrated on the technological aspectof e-learning readiness. A model is proposed which includes eight technologicalfactors, specifically: Software; Hardware; Connectivity; Security; Flexibility of thesystem; Technical Skills and Support; cloud computing; and Data center. A quantitativestudy was conducted at six Malaysian public universities, with survey responses from374 Academic staff members who use e-learning. The empirical study confirmed thatseven of the technological factors have a significant effect on e-learning readiness,while one factor (cloud computing) has not yet had a significant impact on e-learningreadiness.

Keywords E-learning . E-learning readiness . Technological aspect . Higher education

Education and Information Technologies (2019) 24:1395–1431https://doi.org/10.1007/s10639-018-9837-9

* Asma Ali Mosa [email protected]

1 RAZAK Faculty of Technology and Informatics, Universiti Teknologi Malaysia (UTM), KualaLumpur, Malaysia

http://crossmark.crossref.org/dialog/?doi=10.1007/s10639-018-9837-9&domain=pdf

http://orcid.org/0000-0002-7985-3764

mailto:[email protected]

1 Introduction

ICT has caused a great global impact on the world in different ways. Hence, theprogress and prosperity of countries has been linked to the extent of progress andachievements in this area. Due to this impact, most countries in the world have begun todevelop their various institutions to keep pace with this scientific and technologicalrevolution. One of the most important institutions of the world is the higher educationinstitution. Since this has a greater impact on society, it subsequently has greaterresponsibility towards the entire education system as a whole (Naresh and Reddy2015). Most higher education institutions in the world have started to respond to thechanges as a result of the technological revolution in order to create new opportunities.These include improving the existing education systems and learning styles, where ithelps to develop and innovate new and effective teaching and learning methods. It hasalso helped many modern concepts in the field of education, such as e-learning, toemerge. E-learning has become one of the main innovations that are increasinglydiffusing in higher education institutions (Kituyi and Tusubira 2013). The main purposeof e-Learning adoption in higher education institutions is to increase accessibility to theeducational process (Doculan 2016) without restrictions to place and time, and tosubstantially improve the quality and content of the education (Olson et al. 2011). E-learning refers to the use of electronic media, educational technology, also; ICT such asInternet, e-mail, and computers in the educational process (Contreras and Hilles 2015).

The successful implementation of e-learning relies on the readiness to be able toinitiate this system because, without proper readiness, the project will probably fail.According to Alshaher (2013), a primary reason for the failure of e-learning adoption inmany organizations is the lack of assessment of organizational readiness for e-learning.To overcome the barriers and the challenges of e-learning, an organization must beready for e-learning by measuring its readiness and improve the weak points (Schreursand Al-Huneidi 2012). E-learning readiness is the physical preparedness for theinstitutions to apply the e-learning experience (Borotis and Poulymenakou 2004). E-learning readiness is also defined as Bthose factors that must be accomplished before e-learning implementation can be regarded as being successful^ (Odunaike et al. 2013).E-learning readiness helps higher education institutions to: measure their stages ofreadiness; identify any gaps; and then redesign their strategies in order to adopt the e-learning system. Different aspects affect e-learning readiness, Lopes (2007) states thatthe business, technology, content, culture, human resources and financial resourcesaffect the e-learning readiness. Aydin and Tasci (2005) identifies four areas thatdetermine the overall readiness to adopt e-learning in an organization, namely, tech-nology, innovation, people and self-development. Technological aspect is one of themost important aspects of e-learning readiness, because the definition of e-learningdepends on access to a computer and Intranet. Technological aspect plays an importantrole in implementing an effective and efficient e-learning system.

The technological aspect of e-learning refers to the use of different types oftechnologies to facilitate, enhance and support teaching and learning. These include:computers; Internet; mobile phones; audio/video; CDs; DVDs; video conferences;emails; as well as discussion forums (Nyandara 2012). Albarrak (2010) points out thatresearchers have made several attempts to investigate the influence of readiness factorson the outcomes of e-learning; in the light of these studies, it was found that

1396 Education and Information Technologies (2019) 24:1395–1431

technological readiness is one of the key factors that shape and affect the outcomes ofe-learning in an educational setting. For example, one of the technological aspects isInternet access: low Internet speeds and problems while using an e-learning systemmay result in dissatisfaction and cause students to drop out from the e-learning course(Keramati et al. 2011). Therefore, it is necessary to assess the issue of technologicalreadiness for e-learning before the implementation of an e-learning system in order torealize the benefits of e-learning and reduce challenges encountered during e-learningimplementation (Alshaher 2013). This research focuses on identifying factors thatshape the technological aspect of e-learning readiness based on the perspective ofacademic staff members.

1.1 Research problem

Bhuasiri et al. (2012) highlighted technological aspects as an important factor in asuccessful e-learning system; hence, the readiness of the technological aspects needs tobe thoroughly explored in order to analyse overall e-learning readiness. Although thereare studies on e-learning readiness, there is a lack of provision of comprehensiveinformation regarding the technological aspect as the key aspect of e-learning readi-ness. The lack is related to in-depth information about the factors that shape thetechnological aspect of e-learning readiness.

A research study was conducted which was performed to explore the gaps in theknowledge relating to the technological aspects of e-learning readiness through theconduct of a literature review (Mosa et al. 2016). In this study, the technological factorsof e-learning readiness were compared in ten studies. The result of the comparisonobserved that there are several technological factors that influence the overall readinessto adopt and implement e-learning. The comparison of these technological factorsindicates that the technological factors are not considered in every study and there isno single study that encompasses each of the identified factors. There are factors thatare missing from some studies but are reported in other studies. Thus, there is a lack ofagreement about the factors that shape the technological aspects of e-learning readiness;hence, a clear gap is identified in the knowledge on the technological aspects of e-learning readiness. Accordingly, two research questions have been formulated asfollows:

RQ1. What factors influence the technological aspect of e-learning readiness?RQ2. Is the model proposed to identify factors affecting e-learning readiness validand reliable??

1.2 Study objectives

The study aims to investigate the technological factors of e-learning readiness forhigher education institutions, as the key external stakeholder. Measurement modelswill be tested in this study to investigate the ability of technological factors to measuree-learning readiness. In addition, we explore the impact of technological factors on e-learning readiness in the context of the proposed model based on the perspectives ofacademic staff in higher education institutions.

Education and Information Technologies (2019) 24:1395–1431 1397

1.3 Study significance

The significance of this study comes from exploring the technological factors thatshape the technological aspect of e-learning readiness. This study will examinetechnological factors in the context of a measurement model to investigate itsvalidity and reliability in measuring e-learning readiness. Moreover, the perspec-tive of the academic staff will be gained in this study via a survey. The impact oftechnological factors on e-learning readiness is tested in the model of study. Thisstudy attempts to identify the technological factors which the institutions of highereducation should take into account when assessing e-learning readiness. Theresults of this study are significant because they provide the necessary technolog-ical requirements that will enable university management to receive useful knowl-edge about their e-learning readiness. Furthermore, the study results can begeneralized to provide educational institutions with a model and instrument. Themodel contains the technological factors that should be available for e-learningreadiness, where each of a factor should be taken into consideration during theassessment process. As for instrument enables stakeholders in higher educationinstitutions to assess e-learning readiness.

2 Technological aspect factors

The main objective of this study is to propose and test a model to investigate thetechnological factors that impact upon e-learning readiness. Therefore; we haveconducted a Systematic Literature Review (SLR) (AL-araibi et al. 2016) andDelphi technique (Al-araibi et al. 2018) to identify the factors that shape thetechnological aspect of e-learning readiness. First, SLR was conducted to reviewthe existing literature, for identifying the technological aspect factors. Thedatabase was searched for relevant e-learning readiness studies, with 159 studiesgathered from databases including IEEExplore, ScienceDirect, SpringerLink,Wiley InterScience and EdITLib. After applying inclusion and exclusion criteria,and quality assessment, a total of 32 final papers were used to identify factorsthat shape the technological aspect of e-learning readiness. These 32 papers werefrom different areas of applying e-learning, including higher education, highschools and companies. After applying coding techniques and removing dupli-cations, six technological factors have also been identified from SLR, includinghardware, software, connectivity, security, system flexibility, technical skills andsupport. Then, 3-Round Delphi technique were conducted to identify expertopinions on six technological factors which were identified from the SLR, forthe purposes of their naming, their description, the relationships between factors.The size of the Delphi Panel was 11 experts, with the selected experts special-izing in the field of e-learning, having knowledge of the technology aspect. Thefeedback of experts from round one (R1) to round three (R3) of the Delphitechnique were analysed. The results of round one (R1), round two (R2) andround three demonstrated that two new factors were added to the list of techno-logical factors are data centre and cloud computing. The final list of technolog-ical factors, with their descriptions as in Table 1.


2.1 Hardware

Most of the studies related to e-learning readiness mention that the term hardwaresignifies the availability of computers or access to computers (Oketch et al. 2014;Akaslan and Law 2011). Oliver and Towers (2000) mention that without appropriatehardware and easy access it is difficult if not impossible to implement any e-learningprocess. According to Akaslan and Law (2011), any assessment instrument shouldinclude identification of the available hardware. One of the most essential elements thatshould be acquired by higher education institutions intending to adopt e-learning is theuser having access to modern computers that support multi-media applications andhave the appropriate hardware (such as graphics card and sound card) (Chapnick 2000;Driscoll 2010). The suggested hypothesis is:

H1: Hardware has a significant impact on e-learning readiness in higher educationinstitutions.

2.2 Software

The implementation of e-learning by higher education institutions requires a set ofvariables relating to the kind of systems used. In this regard, software has beenidentified as one of the most important factors (Hussain 2016). Software consists of

Table 1 The technological factors of e-learning readiness

No Technologicalfactors

Description of factors

1 Hardware The physical equipment such as Computers/laptops; Printer;Printer/Scanner; Microphone/Speakers/Headset that must be availableto apply e-learning.

2 Software The programs and other operating information that enable computersystems to work. It includes libraries and related non-executabledata, such as online documentation or digital.

3 Connectivity The ability to link to and communicate with other computer systems,electronic devices, software or the Internet.

4 Security The protection of a computer system from data corruption, destruction,interception, loss or unauthorized access.

5 Flexibility of the System The ability of a system to engage with future changes in its requirementssuch as adaptability, changeability, agility and elasticity.

6 Technical skills and support The knowledge, understanding and abilities that are used to accomplishtasks related to maintenance and upgrading of the infrastructure ofcomputers, networks, communications, as well as providing supportto users when they face technical problems.

7 Data Center A large group of networked computer servers typically used byorganizations for the remote storage, processing, or distributionof large amounts of data

8 Cloud Computing The use of services and applications available on demand via the Internetand accessed by Internet protocols and networking standards


the information aspects that help the system to be used to perform certain tasks (Aydinand Tasci 2005). In order to set up e-learning, higher education institutions are requiredto have suitable software that helps to manage the overall e-Learning program(Chapnick 2000; Driscoll 2010). Similarly, to adopt e-learning, higher educationinstitutions should have at least the minimum hardware requirements and the softwarerequired to use that hardware (Aydin and Tasci 2005). The suggested hypothesis is:

H2: Software has a significant impact on e-learning readiness in higher educationinstitutions.

2.3 Connectivity

Laohajaratsang (2009) observes that access to the Internet is one of the most importanttechnological factors that will determine the readiness to adopt e-learning; thus, accessto the Internet should be easy. Omoda-Onyait and Lubega (2011) state that access to theInternet is not the only issue related to the Internet: the speed and availability of theservice are other relevant factors. Borotis and Poulymenakou (2004) point out that, foran e-learning system to be successfully adopted, it is necessary that a reliable Internetservice with the required speed is available. The lack of appropriate Internet infrastruc-ture, speed and reliability affects the readiness of a country to adopt e-learning in itshigher education sector and does not allow students to benefit from the variousadvantages of e-learning (Aydin and Tasci 2005). The adoption of e-learning requiresa sufficient amount of bandwidth which is capable of transferring the video commu-nication (incl. Audio component) that are essential elements of e-learning (Schreurset al. 2008). The suggested hypothesis is:

H3: Connectivity has a significant impact on e-learning readiness in highereducation institutions.

2.4 Security

Another factor in assessing the readiness to adopt e-learning is that of security (Daraband Montazer 2011). It has been observed that the adoption of e-learning is heavilydependent on the security factor. Because of security concerns, many individuals arereluctant to adopt e-learning and thereby forego the benefits of the various advantagesof this new concept of education and training. Aydin and Tasci (2005) state that, amongthe various factors, security is one of the most important that will influence thereadiness to adopt e-learning. The lack of security prevents individuals from acceptingthis new technology and receiving the highest quality education on offer to students.Omoda-Onyait and Lubega (2011) add that the lack of security should be considered inassessing the readiness to accept e-learning. The suggested hypothesis is:

H4: Security has a significant impact on e-learning readiness in higher educationinstitutions.


2.5 Flexibility of the system

Aydin and Tasci (2005) point out that the role of flexibility of system is veryimportant in the overall readiness of adopting an e-learning system. It has beenobserved that flexibility of the system is defined as how flexible the system iswhen engaging with all the e-learning materials. In recent literature, flexibilityhas been identified as a key property that should be embedded in high-valueassets (Saleh et al. 2003). Intuitively, flexibility is understood as being theability to respond to change. Moreover, it is also observed by Akaslan and Law(2011) that flexibility is related to the use of technology or a similar aspectwhich plays an important role in the adoption of a new concept like that of e-learning. The suggested hypothesis is:

H5: Flexibility of the system has a significant impact on e-learning readiness inhigher education institutions.

2.6 Technical skills and support

Technical Skills is the knowledge, understanding and abilities that are used toproviding support to users when they face technical problems, also to accom-plish tasks related to maintenance of e-learning system. Thus, technical skillsand support includes IT support and maintenance. IT support is important in thesuccessful implementation of e-learning as, without this factor, the generalconsumers of this new technology would not be able to adopt it (Ghavamifaret al. 2008). IT support exists to help learners if something goes wrong and tosolve problems such as network issues (Engholm 2002). In the educationalsector, the lack of appropriate IT support results in challenges and barriers forstudents. This lack of IT support can complicate the entire process of attainingeducation and is likely to influence the overall readiness of individuals toaccept the new and innovative technology (Alshaher 2013). Similarly, mainte-nance is important in the successful implementation of e-learning: without thisfactor, the general consumers of this new technology would not be able toadopt it (Ghavamifar et al. 2008). Darab and Montazer (2011) also state that amaintenance team of technological experts is required for the successful imple-mentation and adoption of e-learning; for this purpose, it is extremely importantthat expert support is available and accessible to ensure that the process ofseeking education and learning runs smoothly and is not disrupted by minorglitches and technological barriers. Moreover, Omoda-Onyait and Lubega(2011) observe that maintenance must be available to facilitate the overallprocess of attaining education through electronic means and to manage thetechnological barriers experienced by education-seeking individuals. The sug-gested hypothesis is:

H6: Technical Skills and Support have a significant impact on e-learning readinessin higher education institutions.


2.7 Data centres

A data centre is a facility composed of networked computers and storage that organi-zations use to organize, process, store and disseminate large amounts of data(SearchDataCenter 2017). In other words, data center is physical or virtual infrastruc-ture used by enterprises to house computers, servers and networking systems inaddition to components for the institution’s information technology (IT) needs, whichtypically involve storing, processing and serving large amounts of data in serverarchitecture (Webopedia 2017). The higher education institutions need to assign a datacenter for processing or transmitting its data, with access control features, allowingentry only to authorized personnel and providing forced entry protection (Informatica2017). The suggested hypothesis is:

H7: Data Center has a significant impact on e-learning readiness in highereducation institutions.

2.8 Cloud computing

Many universities around the world are increasingly adopting cloud computing re-sources and services. The benefits of cloud computing for institutions and studentsinclude factors such as mobility, capability, security, availability, interoperability, andend user satisfaction in the use of software applications and other computing resources(Klug and Bai 2015). Cloud computing is not an exception; it has become a suitableplatform architecture for an E-learning system and education services (Sharma 2014),where the adoption of cloud computing for E-learning will pose numerous benefitswhich counter some constraints of e-learning systems in higher institutions. E-learningsystems usually require many hardware and software resources; there are many edu-cational institutions that cannot afford such investments, and cloud computing is oftenthe best solution (Pocatilu et al. 2009). The suggested hypothesis is:

H8: Cloud Computing has a significant impact on e-learning readiness in highereducation institutions.

Further, the experts in Delphi technique identified the relationship between technolog-ical factors (Al-araibi et al. 2018) as follows:

The experts stress that there is relationship between Hardware and Software.The justification from experts was BThese two factors always work with eachother .̂ It is can supported by Aydin and Tasci (2005) mention that to adopt e-learning, higher education Institutions should have at least the minimum hardwarerequirements and the software required to use that hardware. Similarly, Borotisand Poulymenakou (2004) mention that one of technology readiness requirementsis the existence of hardware with the appropriate software, where hardware andsoftware work together. Hardware and software are interconnected. Thus, weconcluded the following hypothesis:

H1a: There is relationship between hardware and software of e-learning readiness


The experts stress that there is relationship between Hardware and Security. Thejustification from experts was BSecurity is an important to product the hardware frommalware^. Thus, we concluded the following hypothesis:

H1b: There is relationship between hardware and security of e-learning readiness

As well as, experts stress that there is relationship between Hardware and Flexibility ofthe system. They mention that Bhardware need to flexibility to adapt to new devices^,and Bhardware need to flexibility for add new equipment^. Thus, we concluded thefollowing hypothesis:

H1c: There is relationship between hardware and flexibility of the system of e-learning readiness

Experts stress that there is relationship between Hardware and Technical Skills andSupport. The justification from experts was BFor maintenance and update^, and BToupgrades hardware with a newer or better version^. Thus, we concluded the followinghypothesis:

H1d: There is relationship between hardware and technical skills and support of e-learning readiness

The experts mention that there is relationship between Software and Security. Thejustification from experts was BTo protect the software we need security ,̂ and Bforprotection of software from hacking or malware^. Thus, we concluded the followinghypothesis:

H2a: There is relationship between software and security of e-learning readiness

Experts also states that there is relationship between Software and Technical Skills andSupport. The justification from experts is Bsoftware need technical support to Updatingof software with a newer version to meet changing information requirement^, and BToupdates software with newer versions^. Thus, we concluded the following hypothesis:

H2b: There is relationship between software and technical skills and support of e-learning readiness

Experts stated that there is relationship between Software and Flexibility of the System.The justification from experts was BTo adapt to possible or future changes in softwarerequirements^, and BTo adapted with some deviations that may occurs in the softwareenvironment as result of human error .̂ Thus, we concluded the following hypothesis:

H2c: There is relationship between software and flexibility of the system of e-learning readiness

Experts mention that there is relationship between Software and Cloud Computing. Thejustification from experts was BTo obtain the needed software applications through the


internet, such as; Google Docs, Salesforce.com CRM, Zoho Office^. Thus, weconcluded the following hypothesis:

H2d: There is relationship between software and cloud computing of e-learning readiness.

Experts mention that there is relationship between Connectivity and hardware;Connectivity and Software. The justification from experts is BTo connect any deviceto the internet you need hardware (equipment) and software^. Thus, we concluded thefollowing hypothesis:

H3a: There is relationship between connectivity and hardware of e-learning readinessH3b: There is relationship between connectivity and software of e-learning readiness

Experts mention that there is relationship between Connectivity and Security. Thejustification from experts was^ When we want to access to Net, other viruses canattach us^, and BTo protect our devices^. Thus, we concluded the following hypothesis:

H3c: There is relationship between connectivity and security of e-learning readiness

Experts mention that there is relationship between Connectivity and Technical Skillsand support. The justification from experts is BTo fix technical issues that mayarise^, and BFor maintenance and update^. Thus, we concluded the followinghypothesis:

H3d: There is relationship between connectivity and technical skills and support ofe-learning readiness

Experts mention that there is relationship between Security and Technical Skills andSupport. The justification from experts was BTo update protection programs^. Thus, weconcluded the following hypothesis:

H4a: There is relationship between security and technical skills and support of e-learning readiness

Experts mention that there is relationship between Data center and Security, thejustification from experts was BTo protect the data and privacy of Institution^. Thus,we concluded the following hypothesis:

H5a: There is relationship between data center and security of e-learning readiness

Experts also mention that there is relationship between Data center and CloudComputing, the justification from experts was BTo obtain the needed servers, storage^.Thus, we concluded the following hypothesis:

H5b: There is relationship between data center and cloud computing of e-learningreadiness


http://salesforce.com

3 Research methodology

A survey method is used in this study to enable a wide range of data to be collected.This study attempts to obtain results that can be generalized for educational institutionsso as to identify the technological factors which should be taken into account when theyare assessing their e-learning readiness. This section describes: the selection of thestudy model and approach; instruments used to collect data; research sample; and themethods adopted to analyze the data.

3.1 Study approach and model

This study adopts a causal approach by which to investigate the cause and impactamong the technological factors and e-learning readiness in the proposed model. Theintroduction of the causality approach has received considerable attention in socialscience and is frequently used in the information systems field. The main justificationfor using this approach is that it provides the ability to show causal relationships amongthe factors of the phenomena occurring in a physical system (Atoji et al. 2002). Thefocus of the model is to discover the impact of technological factors on the indicators ofe-learning readiness, and the relationship between technological factors as shown in theproposed model in Fig. 1.

3.2 Measurement instrument

A questionnaire was developed to collect the data from the study samples. Thequestionnaire is divided into three main parts as follows:

Part A: This part of the questionnaire identified the respondent’s personal infor-mation including: Gender; Academic Qualification; Current Job Position; Years ofexperience; Experience in e-learning teaching/establishing/maintaining.Part B: The second part of the questionnaire asked the respondents to rate the giventechnological factors. This part contains 49 items divided into 9 sections, com-prising: Software; Hardware; Connectivity; Security; Flexibility of the system;Technical Skills and Support; Data Center; as well as Cloud Computing and E-learning readiness. The 49 items in the questionnaire were adapted from previousstudies as presented in Table 2. The forced-choice scale was employed in the studyto overcome the problem of neutrality such as Bnot sure^ or Bdon’t know^responses (Zikmund 2003). It also sought to overcome the problem of too manyneutral responses, which are common among people when given the option tochoose (Hussein et al. 2007). A five-point Likert-type scale was used which tappedinto the individual’s perceptions for all questions related to statements of techno-logical factors, ranging from 1 = Strongly Disagree (SD), 2 = Disagree (D), 3 =Neutral (N), 4 = Agree (A), 5 = Strongly Agree (SA).Part C: The third part of the questionnaire invited the respondents to express anyadditional comments or thoughts (if any).

In order to check the questionnaire regarding the correct and comprehensive reflectionof the concept of research objectives, the content validity for the questionnaire was


assessed and reviewed. The content validity focusses on reviewing and checkingwhether the format, order, spacing, fonts, spelling, as well as Likert scale are appropriateor not. Two experts were chosen in the areas of e-learning and questionnaire design andasked to review the draft of the survey. In terms of academic qualifications, one experthas obtained a Doctoral degree (PhD) and is an academic member at HELP University,Malaysia.With regard to the type of employer, the second expert is AssistantManager inthe E-learning department, International Medical University, Malaysia. After reviewing,the required changes were attended to and the survey was ready for pilot testing. Thefinal version of the questionnaire is attached as Appendix Table 12.

3.3 Research sample and data collection

The research was conducted with academic staff of six Malaysian public universitieswhich offer working experience in e-learning whether in teaching/establishing/main-taining. The six Malaysian public universities are, namely: Universiti Malaya (UM);Universiti Islam Antarabangsa Malaysia (UIAM); Universiti Kebangsaan Malaysia(UKM); Universiti Teknologi MARA (UiTM); Universiti Putra Malaysia (UPM); andUniversiti Pertahanan Nasional Malaysia (UPNM). In addition, Universiti PendedikanSultan Idris (UPSI) from Perak state will be included in the population of the study. Themain reason for selection of the six public universities in Selangor and Kuala Lumpurwas that all of them offer e-learning programs. In this research, all the academic staff ofthe public universities in Selangor and Kuala Lumpur (some 14,362 people, according

The Relationships between the main constructs of technological aspect and e-learning readiness

The relationships between the constructs

Fig. 1 Research model for technological aspects of e-learning readiness


Table2

Summaryof

survey

questionnaire

Part

VariableNam

eQuestion

Code

Tota

lItem

sSo

urce

ARespondents’

Dem

ographicProfile

A1-A5

5Self-developed

andbasedon

severalexistinginstruments.Thispartof

thequestionnairewas

aimed

toidentifytherespondent’s

personalinform

ation:

Gender,theirlevelof

education,

theircurrentpositio

nin

educationalinstitu

tion,

theirexperience

ine-learning

(establishing/m

aintaining/teaching),andtheiryearsof

experience.

BHardw

are

AH1-AH4

4Njih

iaandOketch(2014);Darab

andMontazer(2011);Laohajaratsang(2009);Mercado

(2008);

Aydin

andTasci(2005);Saleh

etal.(2003)

Software

SO1-SO

55

Darab

andMontazer(2011);BorotisandPo

ulym

enakou

(2004);Mercado

(2008);Alshaher(2013);Oketch(2013)

Connectivity

CO1-CO5

5Alsabaw

yetal.(2013);Machado

(2007);BorotisandPo

ulym

enakou

(2004);Omoda-OnyaitandLubega(2011);

Alshaher(2013);Laohajaratsang(2009);Darab

andMontazer(2011)

Security

SE1-SE

99

Alsabaw

yetal.(2013);Darab

andMontazer(2011)

Flexibility

ofthesystem

FS1-FS

44

Aydin

andTasci(2005);Akaslan

andLaw

(2011),S

aleh

etal.(2003)

Cloud

Com

putin

gCC1-CC4

4KlugandBai(2015);Riahi

(2015);Gunjanetal.(2014)

DataCenters

DC1-DC5

5Chaliseetal.(2015);Palo

Alto

Networks

(2017);(Evans

2011)

Technicalskillsandsupport

TS1

-TS5

5Alsabaw

yetal.(2013);Azimi(2013);Darab

andMontazer(2011);Omoda-OnyaitandLubega(2011);

Alshaher(2013);Mercado

(2008);Aydin

andTasci(2005)

E-learningReadiness

ER1-ER8

8Parasuraman

andColby

(2007);Mercado

(2008);Alsabaw

yetal.(2013)

CAdditionalInput

C1

1Self-developed


to the Ministry of Higher education website), will be considered as the target popula-tion. To estimate the sample size in educational studies, the following formula has beensuggested by Cochran (1977):

Equation 1: Cochran Formula for Sampling

n ¼Zð Þ2p:qd2

1þ 1

NZð Þ2p:qd2

!−1

" # ð1Þ

Where:

n is required sample sizeZ is standard normal distribution which is 1.96p & q are the population proportion (assumed to be .50 since this would provide the

maximum sample size)d is the margin of error in the study which is 0.05N is the population size

¼1=96ð Þ2 � 0=5� 0=5

0=05ð Þ2

1þ 1

14362

1=96ð Þ2 � 0=5� 0=5

0=05ð Þ2 !

−1

" #≈374

Based on this formula (Cochran 1977), the adequate sample size in this study isindicated as being 374. Therefore, the sample size adopted for the study will be 374faculty members in six Malaysian public universities. In this study, the researcher usedthe random sampling model in which each faculty member had the same probability ofbeing chosen to participate in the study. The researcher selected the sample in tworandom steps. At the first step, based on the official website of each university, threefaculties will be randomly selected. Following this, in the second step, based on thestaff directory of universities’ official websites. Finally, systematic random samples ofeach university were chosen from the information provided in Table 3.

The number of the participants from each university was based on the proportion ofits population to the target population. The total population (14,372) was divided by thenumber of the sample size (374) and the rate was thereby calculated at around 39. Thus,the sample chosen for each university would be based on this rate. That means onerespondent per any 39 faculty members of any university would be considered. Toreach the required sample size (374), it was necessary to distribute around 30 % highernumber (500) of questionnaires.

3.4 Pilot study

A pilot study was conducted to test the survey instrument in two parts: face validity andreliability. The questionnaire was forwarded to 30 respondents at Malaysian public


universities, where the respondents are not involved in real study. For face validity, theresearcher asked the participants to complete the sample survey to identify areas in thesurvey instrument that could be improved. Areas of discussion included: confusion aboutwhat the survey consists of; confusion regarding thewording of some statements; the lengthof the survey; the ability to answer the questions; as well as the clarity of layout and style.

As a result of face validity, 95% (95%) of respondents indicated that they understoodthe statements and found them easy to answer, and 90 % (90%) stated that theappearance and layout would be acceptable to the intended target audience. Ninety-eight percent (98%) of respondents indicated that the length of the survey was appro-priate, while 96 % (96%) replied that the meaning of the survey was clear. Thus, wedetermined that the questionnaire was acceptable to the intended target audience.

In relation to reliability, a reliability test was carried out to determine how reliablethe survey was by using SPSS V24. In this research, Cronbach’s Alpha (CA) reliabilitytest were conducted for all statements related to assessing technological aspects of e-learning readiness; the results demonstrated very high overall reliability of 0.95 for allthe forty-nine statements. Table 4 shows the reliability results for all statements.

To assess whether the forty-nine statements that were part of the summation forassessing technological aspects of e-learning readiness formed a reliable scale,Cronbach’s Alpha (CA) was computed. According to Baars et al. (2005), the amountof high reliability for CA is the value of 0.70 and above. In this study, Cronbach’sAlpha (CA) for the statements was (0.70), which indicates that the statements from thescale possess reasonable internal consistency. Based on Table 5, Technical Skills andSupport as well as e-learning Readiness have the highest value of CA (0.88); while datacenter are the lowest value of CA (0.80). The CA for Security and Flexibility of thesystem scale (0.87) indicated good internal consistency, while the CA (0.84) forHardware scale also showed high reliability. Similarly, the CA for Software andCloud Computing scale (0.83) demonstrated good internal consistency. Finally, allstatements had Cronbach’s Alpha values above 0.70 which shows good consistency.

3.5 Questionnaire distribution

After completing the pilot study, the survey was sent to the target audience. A driveform survey was developed to gather the data from academic staff at the six Malaysian

Table 3 Detail of sampling

No University State Population Sample

1 UiTM Selangor 6001 154

2 UKM Selangor 2136 56

3 UM KL 2035 53

4 UPM Selangor 1920 50

5 IIUM Selangor 1706 44

6 UPNM Selangor 72 2

7 UPSI Perak 564 15

Sum 14,372 374


public universities irrespective of their geographical location. We made sure that therespondents all had working experience in e-learning through a review of their profileson websites. The questionnaire was distributed in January 2017 in order to collect thedata. After a period of four months we received 394 responses, resulting in a final totalof 374 complete responses to be used in the data analysis, while 20 responses were notusable due to incomplete answers provided.

3.6 Data analysis

The survey involved 374 respondents, so we used descriptive analysis to analyze thegeneral questions of the designed questionnaire which are related to respondentcharacteristics. A frequency table is useful to provide a holistic view of the frequencyof each variable of the questionnaire (especially for the characteristics of respondentswhich contain frequency), and a percentage of each Item. Table 5 summarized thedemographic profiles of the respondents as follows.

The collected data set from the questionnaire relating to technological factors wereanalyzed using Structural Equation Modelling (SEM). Using Amos, the analysis will beconducted as inferential statistics to analyze the collected data. The reason for usingSEM is because it is a more suitable tool to analyze the data which the inter-variablespecifies in priory based on the theory (Karami 2011). According to Byrne (2010),structural equation modeling is Ba statistical methodology that takes a confirmatory(hypothesis-testing) approach to the analysis of a structural theory bearing on somephenomenon^. In correlational studies, SEM (which is state-of-the-art and a powerfulstatistical tool) (Iacobucci 2008) is used to assess direct and indirect relationshipsamong variables. According to Pallant (2010), SEM is applied into the research whenthe researcher wants to understand the relationship among the independent variables,and there is at least one a dependent variable. Therefore; SEM is applicable in this studybecause the study wants to measure the relationship between the independent variables(Technological aspect factors), also; measure the relationship between the independentvariables (Technological aspect factors) and dependent variable (E-learning readiness).Amos 22.0 (SPSS Inc. 2012) was used to perform these analyses.

Table 4 Reliability test of the questionnaire

Technological Factors Number of Statements Cronbach’s Alpha

Hardware 4 0.84

Software 5 0.83

Connectivity 5 0.81

Security 9 0.87

Flexibility of the system 4 0.87

Cloud Computing 4 0.83

Data Centers 5 0.80

Technical skills and support− 5 0.88

E-learning Readiness 8 0.88

Overall 49 0.95


SEM is used to test the measurement model and structural model. The measurementmodel is essential as it provides a test for the validity of the observed variablesemployed to measure the latent variables. A structural model is looking for the bestgoodness-of-fit and hypothesized relationships among endogenous and exogenousvariables by path analysis.

There are several indices for Goodness-of-Fit (GOF) indices (which are thecriteria for assessing model fit in measurement and structural models). Amongthem Chi-Square (x^2), Goodness of Fit Indicator (GFI), Comparative fit index(CFI), Root Mean Square Error of Approximation (RMSEA), AdjustedGoodness of Fit Indicator (AGFI), Normed fit index (NFI) and Tucker-Lewisindex (TLI) are popular and generally used for fitting of the model. Oke et al.(2012), after a review of the literature, provided the recommended cut-offvalues of various fit indices based on the recommendations of various researchworks as shown in Table 6.

In SEM, latent variables are used, thus; it needs to assess the accuracy of ameasurement (Hair et al. 2010). Construct validity actually assesses if a set of measureditems explains the theoretical latent construct. In order to assess construct validity, itshould examine convergent and discriminant validity.

Table 5 Demographic profiles of survey respondents (n = 374)

Variable Options Frequency Percentage (%)

Gender Male 211 56.4%

Female 163 43.6%

Academic Qualification Doctoral degree (PhD) 228 61%

Master’s degree 132 35.3%

Bachelor’s degree 14 3.7%

Current Job Position Lecturer 152 40.6%

Senior Lecturer 82 21.9%

Professor 63 16.8%

Associate Professor 52 13.9%

Dean of Faculty 5 1.3%

Assistant Dean 6 1.6%

Researcher 14 3.9%

Years of experience 5–1 years 173 46.3%

5–10 years 114 30.5%

10–15 years 53 14.2%

15–20 years 21 5.6%

More than 20 years 13 3.4%

Experience in e-learning Teaching 167 44.7%

Establishing 86 22.9%

Maintaining 72 19.2%

Designing 35 9.3%

Researcher 14 3.9%


In this study, convergent validity was assessed by factor loading, CompositeReliability (CR), and Average Variance Extracted (AVE) (Fornell and Larcker 1981).Confirmatory Factor Analysis (CFA) is conducted to estimate factor loading of variablesand a factor loading presents the level of a regression path from a latent to its indicators.In the current study all of latent variables had at least three indicators, (the questionnaireitems). According to Hair et al. (2010) factor loading of more than 0.5 is acceptable andwhen it is equal to 0.7 and above it is considered good for an indicator. The level of CR isanother guideline to review convergent validity. According to Hair et al. (2010) theacceptable level of CR is 0.7 and above. The third method to check construct validity isapplying AVE. It measures the level of variance captured by a construct versus the levelof the measurement error and a value more than 0.7 is considered very good, whereas,the level of 0.5 and above is acceptable (Hair et al. 2010).

Discriminant validity is a test to ensure there is no significant variance amongdifferent variables that could have the same reason. Discriminant validity indicates todifferentiate one construct from the other constructs in the same model. In this study,discriminant validity can be tested by evaluation based on comparison of the squareroot of AVE for each construct against correlations in the model. Fornell and Larcker(1981) state that to check the discriminate validity, the level of square root of AVEsshould be greater than the correlation involving the constructs.

4 Results

4.1 Measurement model

The measurement model has been conducted on the faculty members sample. Theintegrated measurement model was evaluated using all technological aspect factorsaccording to the research model in Fig. 1. It was necessary to define a measurementmodel to verify that the 49 measurement variables written to reflect the nine constructs,specifically: hardware; software; connectivity; security; flexibility; technical skills andsupport; cloud computing; data center and e-learning readiness. The overall fit of ameasurement model was determined by Confirmatory Factor Analysis (CFA). Thismeasurement model with the nine variables was portrayed in Fig. 2.

Table 6 Recommended level of goodness-of-fit (GOF) measure (Chinda and Mohamed 2008; Singh 2009;Doloi et al. 2010; Bagozzi and Yi 2012; Oke et al. 2012)

Fit Indices Recommended Value

χ2/Degree of freedom (x2/DF) ≤ 3

Goodness of Fit Indicator (GFI) 0.00 to 1.00

Comparative fit index (CFI) 0.90 to 1.00

Root Mean Square Error of Approximation (RMSEA) < 0.07

Adjusted Goodness of Fit Indicator (AGFI) 0.00 to 1.00

Normed fit index (NFI) 0.60 to 1.00

Tucker-Lewis index (TLI) 0.95 to 1.00


From Figure 2, the results showed fit of the measurement model with x2 (372) =2192.39, p = 0.000, x2/DF = 3.15, GFI = 0.889; AGFI = 0.899, CFI = 0.919; IFI =0.911, RMSEA = 0.071 respectively. In addition, the RMSEA met the cut-off point0.071, which fell below the recommended range of acceptability. The results of CFAfor testing the integrated measurement model including all research variables confirmedthat the measurement model had a good fit. In other words, the goodness of fit statisticstherefore confirmed that the model adequately fitted the data.

According to the result of measurement model, convergent validity can be tested byfactor loading, Composite Reliability (CR), and Average Variance Extracted (AVE).Composite Reliability (CR) is between 0.799 and 0.940. In addition, in this study,Average Variance Extracted (AVE) measures approximately above 0.5 as highlighted inTable 7. Furthermore, all factor loadings are above 0.5 for this construct. Further,

Fig. 2 Final integrated measurement model based on all constructs


Table 7 The result of convergent validity for integrated measurement model

Construct Item Loading Factor CR AVE

Hardware Hardware 1 0.958 0.803 0.520

Hardware 2 0.672

Hardware 3 0.451

Hardware 4 0.713

Software Software 1 0.794 0.883 0.603

Software 2 0.812

Software 3 0.839

Software 4 0.783

Software 5 0.640

Connectivity Connectivity 2 0.968 0.940 0.797

Connectivity 3 0.827



Security Security 1 0.508 0.864 0.522

Security 5 0.657

Security 6 0.732

Security 7 0.705

Security 8 0.911

Security 9 0.760

Flexibility of the system Flexibility 1 0.915 0.865 0.65

Flexibility 2 0.893

Flexibility 3 0.774

Flexibility 4 0.517

Cloud Computing Cloud Computing 1 0.613 0.871 0.632




Data Centers Data Center 1 0.822 0.906 0.708

Data Center 3 0.800

Data Center 4 0.869

Data Center 5 0.872

Technical Skills and Support Technical Skills and Support 1 0.843 0.799 0.576

Technical Skills and Support 2 0.826

Technical Skills and Support 5 0.579

E-learning Readiness E-learning Readiness 1 0.825 0.914 0.640







Maximum Shared Variance (MSV) and Average Shared Variance (ASV) should bebelow AVE. In this study, MSV and ASV are below AVE. AVE for each construct ismore than each of the squared correlation between constructs. Thus, the results provethat convergent validity (AVE) and Construct Reliability (CR) exist for the constructsof this study which confirmed that all research variables met the convergent validity.This ultimately means that measures of constructs that theoretically should be related toeach other are, in fact, observed to be related to each other.

Discriminant validity can be tested by evaluation based on comparison of the squareroot of AVE for each construct against correlations in the model. A construct will haveadequate discriminant validity if the square root of AVE exceeds the correlation amongthe constructs (Fornell and Larcker 1981; Hair et al. 2016). Based on Table 8, thesquare root of AVE for each variable (bolded numbers on diagonal) is more than eachof the correlation between variables including data center, hardware, software, connec-tivity, security, flexibility, cloud computing, technical support, e-learning readinessrespectively. Therefore, discriminant validity is considered to be adequate for the entiremodel.

4.2 Structural model

After the modified measurement model has been confirmed, the fit of the structuralmodel can be evaluated (Ho 2006). The structural model can be applied by specifyingthe relationships among the variables. The structural model provides details on the linksbetween the variables. It shows the specific information of the association between theindependent or exogenous variables and dependent or endogenous variables (Hair et al.2006; Ho 2006). Evaluation of the structural model emphases firstly on the overallmodel fit, followed by the size, direction and significance of the hypothesized param-eter estimates, (Hair et al. 2006). The structural model is presented in Fig. 3; the fitindices of this model were computed based on the Maximum Likelihood method (ML).The chi-square was significant (χ2 = 372.604, p < 0.001). The GFI was 0.845, more

Table 8 Correlation of latent variables and discriminant validity for integrated measurement model

DC HA SA CO SE FL CC TS ER

DC 0.841

HA 0.419 0.771

SA 0.335 0.563 0.777

CO 0.421 0.373 0.436 0.893

SE 0.71 0.45 0.338 0.565 0.722

FL 0.581 0.756 0.58 0.565 0.663 0.791

CC 0.138 0.344 0.161 0.17 0.146 0.204 0.795

TS 0.622 0.362 0.042 0.163 0.669 0.657 0.032 0.759

ER 0.234 0.263 0.429 0.222 0.416 0.535 −0.068 0.678 0.778

DC Data Center, HA Hardware, SA Software, CO Connectivity, SE Security, FL Flexibility, CC CloudComputing, TS Technical Skills and Support, ER E-learning Readiness

(Bolded numbers are square root of AVE)


than the cut-off 0.8. The CFI and IFI were 0.914 and 0.913 respectively, more than thecut-off 0.9. The RMSEAwas 0.074, less than the threshold 0.08 and χ2/df was 28.629,below the threshold of 5.

Path coefficients were tested to ensure validity of the structural model. According tothe results in Table 9, only seven independent variables showed a significant effect one-learning readiness among respondents. Hardware significantly affected e-learningreadiness (β = 0.261, p = 0.000); while software had a substantially positive effect on

Fig. 3 Structural model (standardized path coefficients)

Table 9 Test of the total effects of independent variables (IVs) on e-learning readiness

Path b β S.E. C.R. P value

HA---------- > ER 0.423 0.261 0.088 4.833 0.000

SA---------- > ER 0.387 0.285 0.080 4.868 0.000

CO---------- > ER −0.399 0.372 0.090 3.096 0.027

SE---------- > ER 0.474 0.289 0.070 1.249 0.021

FL---------- > ER 0.161 0.098 0.082 1.966 0.049

TS---------- > ER 0.420 0.337 0.072 5.812 0.000

DC---------- > ER 0.212 0.377 0.079 4.421 0.015

CC---------- > ER 0.052 0.034 0.069 0.747 0.455

HA Hardware, SA Software, CO Connectivity, SE Security, FL Flexibility of the System, TS Technical Skillsand Support, DC Data Center, CC Cloud Computing, ER E-learning Readiness


e-learning readiness (β =0.258, p = 0.000). Connectivity had a positive and significanteffect on e-learning readiness (β =0.372, p < 0.027). Security also had a positive andsignificant effect on e-learning readiness (β =0.289, p < 0.021). Further, Flexibility hada positive and significant effect on e-learning readiness (β =0.098, p < 0.049). Inaddition, Technical Support had a positive and significant effect on e-learning readiness(β =0.337, p = 0.000). Data Center had a positive and significant effect on e-learningreadiness (β =0.77 p < 0.015). However, Cloud Computing did not have a positive andsignificant effect on e-learning readiness (β =0.034, p < 0.455).

4.3 Testing of hypotheses

The first set of hypotheses included eight relationships between the main constructs oftechnological aspects and e-learning readiness described as follows:

Hypothesis 1

The first hypothesis explored the relationship between hardware and e-learning read-iness. The hypothesized model predicted that hardware has a significant impact on e-learning readiness in higher education institutions. The results showed a positivesignificant path between the two constructs. Consistent with e-learning readiness, βwas −0.261 with p = 0.000. As Table 10 shows, hardware has a significant impact on e-learning readiness in higher education institutions. Therefore, the first hypothesis issupported.

Hypothesis 2

The second hypothesis explored the relationship between software and e-learningreadiness. The hypothesized model predicted that software has a significant impactupon e-learning readiness in higher education institutions. The results showed apositive significant path between the two constructs. Consistent with e-learning read-iness, β was 0.285 with p = 0.000. As Table 10 shows, software has a significant

Table 10 Testing the hypothesis

Hypothesis Path β P value EmpiricalEvidence

H1 Hardware ------------ > E-learning Readiness −0.261 0.000 Supported

H2 Software ------------- > E-learning Readiness 0.285 0.000 Supported

H3 Connectivity ------------ > E-learning Readiness 0.372 0.027 Supported

H4 Security ----------------- > E-learning Readiness 0.289 0.021 Supported

H5 Flexibility of the system -------- > E-learning Readiness 0.098 0.049 Supported

H6 Technical skills and support ------- > E-learning Readiness 0.337 0.000 Supported

H7 Data Center ----------------- > E-learning Readiness 0.377 0.015 Supported

H8 Cloud Computing ------------ > E-learning Readiness 0.034 0.455 Not Supported


impact on e-learning readiness in higher education institutions. Therefore, the secondhypothesis is supported.

Hypothesis 3

The third hypothesis explored the relationship between connectivity and e-learningreadiness. The hypothesized model predicted that connectivity has a significant impactupon e-learning readiness in higher education institutions. The results showed apositive path between the two constructs. Consistent with e-learning readiness, β was0.372 with p = 0.027. As Table 10 shows, connectivity has a significant relationshipwith e-learning readiness in higher education institutions. Therefore, the third hypoth-esis is supported.

Hypothesis 4

The fourth hypothesis explored the relationship between security and e-learningreadiness. The hypothesized model predicted that security has a significant impacton e-learning readiness in higher education institutions. The results showed apositive path between the two constructs. Consistent with e-learning readiness,β was 0.289 with p = 0.021. As Table 10 shows, security has a significantrelationship with e-learning readiness in higher education institutions. Therefore,the fourth hypothesis is supported.

Hypothesis 5

The fifth hypothesis explored the relationship between flexibility and e-learning read-iness. The hypothesized model predicted that flexibility of the system has a significantimpact upon e-learning readiness in higher education institutions. The results showed apositive significant path between the two constructs. Consistent with e-learning read-iness, β was 0.098 with p = 0.049. As Table 10 shows, flexibility of the system has asignificant impact on e-learning readiness in higher education institutions. Therefore,the fifth hypothesis is supported.

Hypothesis 6

Hypothesis number six explored the relationship between technical skills and e-learning readiness. The hypothesized model predicted that Technical Skills andSupport have a significant impact on e-learning readiness in higher education institu-tions. The results showed a positive significant path between the two constructs.Consistent with e-learning readiness, β was 0.337 with p = 0.000. As Table 10 shows,technical skills and support both have a significant impact on e-learning readiness inhigher education institutions. Therefore, the sixth hypothesis is supported.

Hypothesis 7

The seventh hypothesis explored the relationship between data center and e-learningreadiness. The hypothesized model predicted that data center has a significant impact


on e-learning readiness in higher education institutions. The results showed a positivepath between the two constructs. There was no consistency with e-learning readiness; βwas 0.377 with p = 0.015. As Table 10 shows, data center has a significant relationshipwith e-learning readiness in higher education institutions. Therefore, the seventhhypothesis is supported.

Hypothesis 8

Hypothesis number eight explored the relationship between cloud computing and e-learning readiness. The hypothesized model predicted that cloud computing has asignificant impact on e-learning readiness in higher education institutions. The resultsshowed a positive path between the two constructs. There was no consistency with e-learning readiness; β was 0.034 with p = 0.455. As Table 10 shows, cloud computingdoes not have a significant impact on e-learning readiness in higher education institu-tions. Therefore, hypothesis number eight is not supported.

4.4 Secondary hypothesis

The second set of hypotheses covered the relationships between the constructs.

Hypothesis 1a:

Hypothesis number 1a explored the relationship between hardware and software. Thehypothesized model predicted that there is a relationship between hardware andsoftware of e-learning readiness. The results showed a positive path between the twoconstructs (β = 0.507, p = 0.000). As Table 11 shows, there was a significant

Table 11 Testing the secondary hypothesis

Hypothesis Path β P value Empirical Evidence

H1a Hardware ------------- > Software 0.507 0.000 Supported

H1b Hardware ------------- > Security 0.135 0.001 Supported

H1c Hardware -------------- > Flexibility of the system 0.141 0.003 Supported

H1d Hardware -------------- > Technical skills and support 0.171 0.000 Supported

H2a Software --------------- > Security 0.243 0.000 Supported

H2b Software --------------- > Technical skills and Support −0.083 0.126 Not Supported

H2c Software --------------- > Flexibility of the system 0.425 0.000 Supported

H2d Software --------------- > Cloud computing 0.216 0.000 Supported

H3a Connectivity ----------- > Hardware 0.549 0.000 Supported

H3b Connectivity ------------ > Software 0.532 0.000 Supported

H3c Connectivity ------------ > Security 0.260 0.000 Supported

H3d Connectivity ----------- > Technical skills and support 0.064 0.226 Not Supported

H4a Security --------------- > Technical skills and support 0.566 0.000 Supported

H5a Data center ----------- > Security 0.336 0.000 Supported

H5b Data center ----------- > Cloud Computing 0.127 0.014 Supported


relationship between software and hardware of e-learning readiness. Therefore, hy-pothesis number 1a is supported.

Hypothesis 1b:

Hypothesis number 1b explored the relationship between hardware and security. Thehypothesized model predicted that there is a relationship between hardware andsecurity of e-learning readiness. The results showed a positive path between the twoconstructs (β = 0.135, p = 0.001). As Table 11 shows, there was a significant relation-ship between hardware and security of e-learning readiness. Therefore, hypothesisnumber 1b is supported.

Hypothesis 1c:

Hypothesis number 1c explored the relationship between hardware and flexibility. Thehypothesized model predicted that there is a relationship between hardware andflexibility of e-learning readiness. The results showed a positive path between thetwo constructs (β = 0.141, p = 0.003). As Table 11 shows, there is a significantrelationship between hardware and flexibility of e-learning readiness. Therefore, hy-pothesis number 1c is supported.

Hypothesis 1d:

Hypothesis number 1d explored the relationship between hardware and techni-cal skills. The hypothesized model predicted that there is a relationship betweenhardware and technical skills and support of e-learning readiness. The resultsshowed a positive path between the two constructs (β = 0.171, p = 0.000). AsTable 11 shows, there was a significant relationship between hardware andtechnical skills and support of e-learning readiness. Therefore, hypothesis num-ber 1d is supported.

Hypothesis 2a:

Hypothesis number 2a explored the relationship between software and security. Thehypothesized model predicted that there is a relationship between software and securityof e-learning readiness. The results showed a positive path between the two constructs(β = 0.243, p = 0.000). As Table 11 shows, there was a significant relationship betweensoftware and security of e-learning readiness. Therefore, hypothesis number 2a issupported.

Hypothesis 2b:

Hypothesis number 2b explored the relationship between software and technicalskills. The hypothesized model predicted that there is a relationship betweensoftware and technical skills and support of e-learning readiness. The resultsshowed a negative path between the two constructs (β = −0.083, p = 0.126). AsTable 11 shows, there was not a significant relationship existing between


software and technical skills of e-learning readiness. Therefore, hypothesisnumber 2b is not supported.

Hypothesis 2c:

Hypothesis number 2c explored the relationship between software and flexibility. Thehypothesized model predicted that there is a relationship between software and flexi-bility of the system of e-learning readiness. The results showed a positive path betweenthe two constructs (β = 0.425, p = 0.000). As Table 11 shows, there was a significantrelationship between software and flexibility of e-learning readiness. Therefore, hy-pothesis number 2c is supported.

Hypothesis 2d:

Hypothesis number 2d explored the relationship between software and cloud comput-ing. The hypothesized model predicted that there is a relationship between software andcloud computing of e-learning readiness. The results showed a positive path betweenthe two constructs (β = 0.216, p = 0.000). As Table 11 shows, there was a significantrelationship between software and cloud computing of e-learning readiness. Therefore,hypothesis number 2d is supported.

Hypothesis 3a:

Hypothesis number 3a explored the relationship between connectivity and hardware.The hypothesized model predicted that there is a relationship between connectivity andhardware of the e-learning readiness. The results showed a positive path between thetwo constructs (β = 0.549, p = 0.000). As Table 11 shows, there was a significantrelationship between connectivity and hardware of e-learning readiness. Therefore,hypothesis number 3a is supported.

Hypothesis 3b:

Hypothesis number 3b explored the relationship between connectivity and software.The hypothesized model predicted that there is a relationship between connectivity andsoftware of e-learning readiness. The results showed a positive path between the twoconstructs (β = 0.532, p = 0.000). As Table 11 shows, there was a significant relation-ship between connectivity and software of e-learning readiness. Therefore, hypothesisnumber 3b is supported.

Hypothesis 3c:

Hypothesis number 3c explored the relationship between connectivity and security. Thehypothesized model predicted that there is relationship between connectivity andsecurity of e-learning readiness. The results showed a positive path between the twoconstructs (β = 0.260, p = 0.000). As Table 11 shows, there was a significant relation-ship between connectivity and security of e-learning readiness. Therefore, hypothesisnumber 3c is supported.


Hypothesis 3d:

Hypothesis number 3d explored the relationship between connectivity and technicalskills and support. The hypothesized model predicted that there is a relationshipbetween connectivity and technical skills and support of e-learning readiness. Theresults showed a positive path between the two constructs (β = 0.064, p = 0.226). AsTable 11 shows, there was not a significant relationship existing between connectivityand technical skills and support of e-learning readiness. Therefore, hypothesis number3d is not supported.

Hypothesis 4a:

Hypothesis number 4a explored the relationship between security and technical skillsand support. The hypothesized model predicted that there is a relationship betweensecurity and technical skills and support of e-learning readiness. The results showed apositive path between the two constructs (β = 0.556, p = 0.000). As Table 11 shows,there was a significant relationship between security and technical skills and support ofe-learning readiness. Therefore, hypothesis number 4a is supported.

Hypothesis 5a:

Hypothesis number 5a explored the relationship between data centers and security. Thehypothesized model predicted that there is a relationship between data centers andsecurity of e-learning readiness. The results showed a positive path between the twoconstructs (β = 0.336, p = 0.000). As Table 11 shows, there was a significant relation-ship between data centers and security of e-learning readiness. Therefore, hypothesisnumber 5a is supported.

Hypothesis 5b:

Hypothesis number 5b explored the relationship between data centers and cloudcomputing. The hypothesized model predicted that there is a relationship between datacenters and Cloud Computing of e-learning readiness. The results showed a positivepath between the two constructs (β = 0.127, p = 0.014). As Table 11 shows, there was asignificant relationship between data centers and cloud computing of e-learning read-iness. Therefore, hypothesis number 5b is supported.

5 Discussion

This section discusses the results obtained in Section 4 (the empirical study). Thediscussion of the relationships among the constructs of the proposed model is based onthe hypotheses formulated in this study. The findings of the study provide support forthe proposed model which was conducted with the academic staff sample, regarding theimpacts of technological aspect of e-learning readiness. In relation to the measurementmodel, the results confirmed that the model and its seven constructs are sufficientlyreliable and valid to identify the e-learning readiness of the technological factors, where


six constructs have significant impact on the e-learning readiness in higher educationinstitutions. These are: Hardware; Software; Connectivity; Security; Flexibility of thesystem; Technical Skills and Support. These findings are consistent with the results ofstudies conducted about e-learning readiness, for instance Darab and Montazer (2011);Laohajaratsang (2009); Mercado (2008); Aydin and Tasci (2005); Saleh et al. (2003).Also, the results confirmed that Data center have significant impact on the e-learningreadiness in higher education institutions. In relation to the cloud computing factor, theresult showed that this construct has no significant impact on the e-learning readiness inhigher education institutions as shown in Table 10 (β 0.034, P value 0.455).Accordingly, this factor will be excluded from the list of technological factors whichshould be taken into account when assessing e-learning readiness.

In regard to the relationship between technological factors, the results in Table 11showed that thirteen of the hypotheses were supported, while two hypotheses were not.First, there is a relationship between hardware and software of e-learning readiness(β = 0.507, p = 0.000). The relationship between hardware and software begins with thehardware in that the hardware cannot function on its own without software. Hardwareand software work together in harmony to allow the user to operate his or her electronicdevice. The successful adoption of e-learning requires at least the minimum hardwarerequirements and the software required to use that hardware (Aydin and Tasci 2005;Borotis and Poulymenakou 2004). Second, there is a relationship between hardwareand security (β = 0.135, p = 0.001). The hardware requires security to protect com-puters from harm, and to ensure the continuous operation of the computers that provideeducational information. This area concerns the security of both data storage serversand client computers in the campus computer network. In the case of the networkhardware security, the causes of malfunction are both hardware failures resulting fromnormal use and accidents provoked by the intention to disrupt the normal systemoperation. Third, there is a relationship between hardware and flexibility (β = 0.141,p = 0.003). The systems of hardware in the campus should have the ability to adapt topossible or future changes according to its requirements. Changes occur in the marketplace, with new technologies being offered to enhance computer capabilities. This willpush the decision-maker to decide at some point later in time that he would like newtechnologies to be added to the computer systems in the campus (such as adding DVD-burners or TV-tuners to client computers in the campus). The computer systems shouldbe able to ensure that that capability can be added, so the computer is modifiable inthose new capabilities (Saleh et al. 2003).

Fourth, there is a relationship between hardware in addition to technical skills andsupport (β = 0.171, p = 0.000). The hardware system requires technical skills andsupport to be provided for maintenance of equipment such as computers, it alsosupported by (Omoda-Onyait and Lubega 2011; Darab and Montazer 2011). It alsorequires the equipment to be upgraded with a newer or better version, in order to bringthe system up to date or to improve its characteristics. Fifth, there is a relationshipbetween software and security (β = 0.243, p = 0.000). The software requires security toensure the continuous operation of the computer operating systems that provideeducational information. This area concerns the operating systems security of allcomputers connected to the educational platform. The malfunction of operating sys-tems can be attributed to both incorrect installations and configurations, as well asmalicious software (viruses, worms, etc.) that have been installed without the user’s


knowledge. Within this area of security, the elimination of system software installed onpurpose to gain unauthorized access to information should also be considered. Sixth,there is a relationship between software and flexibility (β = 0.425, p = 0.000). Thesoftware system should have the ability to adapt to possible or future changes accordingto its requirements. Institutions of higher education need to modify themselves tomaintain competitive advantages. In order to adapt to such changes, the software usedby the institutions often needs to change urgently; however, software without flexibilityis often difficult to change. Hence, it is important to measure and estimate softwareflexibility (Saleh et al. 2003). Seventh, there is a relationship between software andcloud computing (β = 0.216, p = 0.000). Some higher education institutions which donot have the resources and infrastructure needed to run top e-learning solutions areusing cloud computing to access a cloud software as service (Corrado & Moulaison2011). The reason for its popularity is that it can support the delivery of complexapplications over the internet, and provide reliable storage. A browser, such as InternetExplorer, is used to access software provided over the Internet. Essentially, the end userpurchases the right to access a software package and does not need to be concernedabout purchasing the underlying infrastructure to run that software.

Eighth, there is a relationship between connectivity and hardware (β = 0.549, p =0.000). The connectivity requires hardware to connect with the Internet. The minimumhardware requirements are: desktop or laptop computers; hard drive; CD-ROM drive;headphones /microphone and camera; color video card; as well as modem (InternetConnection). Ninth, there is a relationship between connectivity and software (β =0.532, p = 0.000). The connectivity requires software to connect with the Internet. Theminimum software requirements are: Windows 7 or later/ Mac OS 10.7 or later; AdobeAcrobat Reader - v. 10.1.4 or higher/Adobe Acrobat Reader (most recent); Flash Player;Browser (Chrome or Firefox...etc.). Tenth, there is a relationship between connectivityand security (β = 0.260, p = 0.000). The connectivity requires security to ensure thecontinuous operation of the hardware responsible for the data flow in a computernetwork. Higher education institutions require secure network connectivity to theirbusiness data and applications. The need to connect and collaborate with students,academic staff and employees anytime and anywhere has expanded network connectiv-ity requirements beyond traditional wired Local Area Networks (LANs) to include dial-up remote access, virtual private networks (VPNs), and wireless networks. To enablegreater access to the network, institutions must address issues around security such assecure access to corporate information assets from within institutions, or externally fromthe Internet. Also, there should be a high level of security for protection of connectivitysystem (hardware, software) from data loss or corruption to hardware failure, humanerror, hacking or malware (Darab and Montazer 2011; Aydin and Tasci 2005).

There is a relationship between security as well as technical skills and support (β =0.556, p = 0.000). The security system requires technical skills and support to manageall tasks that influence the security system related to hardware/software/ connectivity, inaddition to regular updating of the security system. There is also a relationship betweendata center and security (β = 0.336, p = 0.000). Data center requires security to protectthem from damage. Data center is the functioning heart of each e-learning system.Thus, higher education institutions should be protecting their data center from directaccess, hacking or malware. Also, the institution should have a planning program thatcan protect the data in the event of an emergency or disaster that could potentially


destroy it (Cisco,1012). Further, there is a relationship between data center and cloudcomputing (β = 0.127, p = 0.014). Some higher education institutions which do nothave the resources and infrastructure needed to run top e-learning solutions are usingcloud computing to access an entire data-center as a service to enable data storage.Cloud computing offers an effective solution against the problem of reliance on a singleserver by providing multiple entry points application programming interfaces (API) asa service platform that enables data storage and retrieval from multiple points (Corrado& Moulaison 2011). Therefore, a failure at one point could be overcome by redirectingthe request to a different point.

The results are not supported by the relationship between software and technicalskills and support (β = −0.083, p = 0.126). Based on the respondents’ answers, softwaredoes not require technical skills and support. Similarly, the results are not supported bythe relationship between connectivity and technical skills and support (β = 0.064, p =0.226). Based on the respondents’ answers, connectivity does not require technicalskills and support.

6 Study contributions and limitations

The first contribution of this study was in developing and testing a model to identify thetechnological aspect factors of e-learning readiness in higher education institutions. It isanticipated that the identified technological factors will help management of highereducation institutions to identify and understand the technological aspect factors thatmust be considered when assessing their readiness to adopt e-learning. In addition, theidentified technological factors can be used by designers and developers as a guidelinefor identifying the necessary technological requirements for e-learning implementation.The second contribution is identified as the relationship between technological factors,when there are no studies that have considered the relationship between technologicalfactors. The relationship between technological factors explains how technologicalfactors (Software, hardware, Connectivity, Security, Flexibility of the system,Technical Skills and Support, Data center) work in an interconnected way towards e-learning implementation.

This study was limited to the topic of e-learning in higher education institutions in asingle country (Malaysia). This limitation can be an obstacle to generalizing thefindings of this study to other countries adopting e-learning systems due to differencesin the respective environment of countries. Also, this study was limited to e-learning inthe higher education sector and did not include e-learning in industrial organizations.Thus, the findings cannot be generalized to other organizations because there aredifferences between the environment of universities and industrial organizations.

7 Conclusion and recommendations

This study has presented the results of a survey conducted in six Malaysian highereducation institutions that offer e-learning programs. The data was collected from 374academic staff members. The results of examining the model showed that seventechnological factors (Software, hardware, Connectivity, Security, Flexibility of the


system, Technical Skills and Support, Data center) have a significant impact on e-learning readiness, while one technological factor (cloud computing) has not had asignificant impact on e-learning. This is because most higher education institutionshave their own software applications and data center and they prefer not to use cloudcomputing. In addition, all the relationships between the technological factors weresupported by the results; however, two relationships were not supported.

Based on these findings, a set of recommendations can be offered to educationalinstitutions to enhance the performance of e-learning systems. First, more attentionshould be paid to the technological factors due to the critical role of these factors in e-learning readiness. Higher education institutions should measure their technologicalaspect readiness to identify any gaps, and then redesign their strategies in order to adoptthe e-learning system.

In future work, the survey’s respondents included only academic staff in Malaysia’shigher education. Future research should also include staff of technology informationdepartments in higher education institutes, e-learning designers and developers, toobtain their knowledge about technological aspects of e-learning readiness. This isimportant for determining holistic perspectives from specialists within the e-learningfield. In addition, this study developed an instrument for validating the research model,the instrument can be applied in practical environments for assessing the technologicalaspects of e-learning readiness. The assessment of technological aspects will provideguidance for stakeholders in developing policies and plans, and in identifying weakpoints which will need to be improved on by taking relevant actions. This will helpavoid potential risks when implementing e-learning stages (Alshaher 2013).

Acknowledgements The author would like to thank her family members for their support in the completionof this paper. I would also like to thank the three experts who have been chosen to review the draft of surveyfor their participation in this study, and their contribution to the results. In addition, I thank all the academicstaff for their participation in the survey. I also would like to express my gratitude to the reviewers for theirhelpful comments which helped to improve the quality of this paper.

Appendix

Table 12 Questionnaire items used to measure the constructs of the model

No Statements

Hardware: This factor refers to physical equipment such as computers, servers and communication networksthat must be available in order to apply e-learning

HA1 The institution is willing to provide students and faculty access to appropriate hardware in order toundertake e-learning (e.g., Computers; laptops; Printer; Printer/Scanner; Microphone/Speakers/-Headset)

HA2 Students and faculty in the institution have access to a computer with the necessary software installed

HA3 The institution’s systems are sufficiently flexible to incorporate electronic links to external parties

HA4 The institution has a high quality of hardware equipment with which to apply e-learning (e.g.,Computer; laptops; Printer; Printer / Scanner; Microphone/Speakers/Headset)


Table 12 (continued)

No Statements

Software: This factor refers to the programs and other operating information that enables computer systems towork. It includes libraries and related non-executable data, such as online documentation or digital methods

SO1 The institution provides the necessary software needed for e-learning implementation

SO2 The institution has an online platform used for course delivery (e.g., learning management system,social media and peer-to-peer platforms, online meeting/conferencing)

SO3 The online platform used for course delivery has the necessary system capacity to support thelearning activities of the course

SO4 The online platform provides appropriate tools for communication and collaboration

SO5 Software applications can be easily transported and used across multiple platforms

Connectivity: This factor refers to the ability to link to and communicate with other computer systems,electronic devices, software or the Internet

CO1 The institution provides a wide range of connectivity services by which to apply e-learning (e.g.,network services, broadband services, Intranet capabilities)

CO2 The institution has sufficiently stable Internet connection to apply e-learning

CO3 Connection speeds are sufficient for communication and accessing all course materials

CO4 The institution has extensive bandwidth capability

CO5 The institution has a high degree of systems inter-connectivity

Security: This factor refers to the protection of the computer system from data corruption, destruction,interception, loss or unauthorized access

SE1 The institution is provided with a virus protection to apply e-learning (e.g., Norton Antivirus,McAffee, AVG, etc.)

SE2 The institution is provided with an identity recognition mechanism such as login name, password

SE3 A control mechanism for access levels is available in the institution

SE4 In the institution, there are different levels of access to the e-learning system (lecturer, students,guests).

SE5 There is a high level of security for protection of systems (hardware, software) from misdirection orpenetration.

SE6 Backup of the data is constantly being made

SE7 The institution uses an efficient method for protecting information security and privacy (e.g.Biometrics, Encryption, intrusion detection system (IDS), Firewall, Virtual Private Network)

SE8 The institution has a planning program that can protect the data in the event of an emergency ordisaster that could potentially destroy data

SE9 The institution has physical security systems for the data center (e.g. biometrics and videosurveillance systems)

Flexibility of the system: This factor refers to the ability of a system to engage with future changes in itsrequirements such as adaptability, changeability, agility and elasticity

FS1 The system has the ability to adapt to new peripherals

FS2 The system can be adapted using some deviations in the software environment

FS3 The system (hardware, software, connectivity) has the ability to adapt with possible or futurechanges to its requirements

FS4 The systems are sufficiently flexible to incorporate electronic links to external parties

Cloud Computing: This factor refers to the use of services and applications available on demand via theInternet and accessed by Internet protocols and networking standards.

CC1 The institution is taking advantage of the services offered by the cloud computing paradigm toapply e-learning


Table 12 (continued)

No Statements

CC2 The institution offers software applications through the internet as a service (e.g. Google Docs,Salesforce.com CRM, Zoho Office... etc.)

CC3 The institution uses a virtual platform over the Internet to develop and deploy their applications in thecloud scenario with the tools, languages, functions, libraries and services enabled by the serviceprovider

CC4 The institution uses cloud computing to obtain the needed resources like servers, storage, andconnections

Data Centers: This factor refers to a large group of networked computer servers typically used by organizationsfor the remote storage, processing, or distribution of large amounts of data

DC1 The institution has a physical location of data centers

DC2 The institution has an alternative location for the data centers

DC3 The institution has actual equipment for IT operations and storage of the organization’s data; thisincludes servers, storage hardware, cables, and racks

DC4 The institution has Uninterruptible Power Sources (UPS) for data centers (e.g. battery banks,generators, and redundant power sources)

DC5 The institution has environmental control for data centers (e.g. computer room air conditioners(CRAC), heating, ventilation, and air conditioning (HVAC) systems, and exhaust systems)

Technical skills and support: This factor refers to the knowledge, understanding and abilities that are used toaccomplish tasks related to the maintenance and upgrade of the infrastructure of computers, networks, andcommunications. They also provide support to users when they face technical problems

TS1 The institution provides maintenance of equipment such as computers

TS2 The institution periodically updates software with newer versions to meet changing informationrequirements

TS3 The institution periodically upgrades hardware with a newer or better version

TS4 Adequate and timely support is available in the institution to the lecturer and students when technicalissues arise

TS5 The institution has experienced human resources, or a department that organizes training sessionsrelated to online learning, as well as accomplishing tasks related to the use of technology

E-learning Readiness: The mental or physical preparedness of higher education institutions for the e-learningexperience

ER1 I like the idea of using e-learning to design and deliver instruction

ER2 I like to try new technologies in teaching related to e-learning

ER3 I feel confident in my ability to use e-learning in teaching

ER4 I can teach myself most of the things I need to know about using e-learning

ER5 I would feel better about using e-learning if I knew more about it

ER6 I am beginning to understand the process of incorporating e-learning in my courses

ER7 I think about e-learning as a tool to assist me in teaching my courses.

ER8 Greater incentives are needed to encourage faculty members to design an e-learning course


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https://doi.org/10.1007/978-3-540-87783-7_34

https://doi.org/10.1007/978-3-540-87783-7_34

http://searchdatacenter.techtarget.com/definition/data-center

http://searchdatacenter.techtarget.com/definition/data-center

https://www.webopedia.com/TERM/D/data-center.html

https://www.webopedia.com/TERM/D/data-center.html

a model for technological aspect of e-learning readiness ... · successful e-learning system;...

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