Investigating users perspectives on e-learning-An integration of TAM and IS success model

Hassanzadeh, Kanaani, and Elahi 2012, in their attempts toassess e-learning systems success in Iranian universities, identified technical system quality, educational system quality, conte

Investigating users’ perspectives on e-learning: An integration of TAM

and IS success model

Department of Public Administration, Allameh Tabataba’i University, Tehran, Iran

a r t i c l e i n f o

Keywords:

E-learning

Quality

Satisfaction

Intention to use

Actual use

a b s t r a c t

The purpose of this paper is to examine an integrated model of TAM and D&M to explore the effects of quality features, perceived ease of use, perceived usefulness on users’ intentions and satisfaction, along-side the mediating effect of usability towards use of e-learning in Iran Based on the e-learning user data collected through a survey, structural equations modeling (SEM) and path analysis were employed to test the research model The results revealed that ‘‘intention’’ and ‘‘user satisfaction’’ both had positive effects

on actual use of e-learning ‘‘System quality’’ and ‘‘information quality’’ were found to be the primary factors driving users’ intentions and satisfaction towards use of e-learning At last, ‘‘perceived usefulness’’ mediated the relationship between ease of use and users’ intentions The sample consisted of e-learning users of four public universities in Iran Past studies have seldom examined an integrated model in the context of e-learning in developing countries Moreover, this paper tries to provide a literature review

of recent published studies in the field of e-learning

Ó 2014 Elsevier Ltd All rights reserved

1 Introduction

To meet educational purposes and students’ demands,

e-learn-ing development emerges to be a catalyst for today educational

institutions (Alsabswy, Cater-Steel, & Soar, 2013; Docimini &

Palumbo, 2013) E-learning can be defined as a dynamic and

imme-diate learning environment through the use of internet to improve

the quality of learning by providing students with access to

resources and services, together with distant exchange and

collaboration (Docimini & Palumbo, 2013; Jeong & Hong, 2013)

E-learning supports learners with some special capabilities such

as interactivity, strong search, immediacy, physical mobility and

situating of educational activities, self-organized and self-directed

learning, corporate training, personalized learning, and effective

technique of delivering lesson and gaining knowledge (Bidin &

Ziden, 2013; Docimini & Palumbo, 2013; Jeong & Hong, 2013;

Martin & Ertzberger, 2013; Viberg & Gronlung, 2013) E-learning

has a positive impact on both teachers and students in that it

pos-itively affects the duration of their attention, learning and training

tenacity, and their attitudes towards collaboration and interaction

(Chen & Tseng, 2012; Ozdamli & Uzunboylu, 2014) Past studies

have indicated that anywhere and anytime learning and access to

information and communication are facilitated through using e-learning (Chen & Tseng, 2012; Ho & Dzeng, 2010; Islam, 2013; Pena-Ayala, Sossa, & Mendez, 2014) Kratochvíl (2013) and

in e-learning are fond of using it towards learning because of flex-ible access in terms of time, space, and pace and online collabora-tive learning However, demand for the development of e-learning

is increasingly growing; still the need for research on potential fac-tors affecting e-learning adoption like quality which is the heart of education and training in all countries (Ehlers & Hilera, 2012), is felt especially in the context of developing countries (Masoumi & Lindstrom, 2012), a fact that warrants investigation into it Past studies have used information technology adoption theories such as Technology Acceptance Model (TAM), Innovation Diffusion Theory (IDT) and the Unified Theory of Acceptance and Use of Technology (UTAUT) and the DeLone & McLean’s model to explore e-learning users’ behavioral patterns Some of these stud-ies have taken the barriers and the drivers of e-learning adoption into consideration (e.g.,Chen & Tseng, 2012; Islam, 2012, 2013, 2014; Sumak, Hericko, & Punik, 2011) In this paper it is attempted

to introduce an integrated model of TAM and DeLone & McLean’s model for predicting individual’s actual use of e-learning system

in Iran AsLi, Duan, Fu, and Alford (2012)note, it is essential to examine the relationship between e-learners’ experiences, percep-tions, and their behavioral intentions to use, because system use is

an important indicator of the system’s success

http://dx.doi.org/10.1016/j.chb.2014.07.044

0747-5632/Ó 2014 Elsevier Ltd All rights reserved.

⇑ Address: Pars Pamchal Alley, block 17, No 2, Naghshe Iran St Ansar Alhossein

St Second Square, Kosar, Qazvin, Iran Tel.: +98 9192864512, +98 9214563704.

E-mail addresses: H.mohammadi901@st.atu.ac.ir , Hossein662@gmail.com

Contents lists available atScienceDirect

Computers in Human Behavior

j o u r n a l h o m e p a g e : w w w e l s e v i e r c o m / l o c a t e / c o m p h u m b e h

Hassanzadeh, Kanaani, and Elahi (2012), in their attempts to

assess e-learning systems success in Iranian universities, identified

technical system quality, educational system quality, content and

information quality, service quality, user satisfaction, and intention

to use, influential towards use of system, system loyalty, and goal

achievement Motaghian, Hassanzadeh, and Karimzadegan

IS-oriented, psychological and behavioral factors on instructors’

adoption of web-based learning systems in Iran, identified that

perceived usefulness, perceived ease of use, and system quality

improve instructors’ intentions to use web-based learning systems

However, only a limited number of published works have

applied an integrated model of IS success model and TAM to

explore e-learning usage drivers in the context of developing

coun-tries This research, compared toHassanzadeh et al (2012), tries to

step forward to investigate the students’ perceptions of e-learning

services based on an integrated model of TAM and IS success model

and provides a literature review of recent published works in the

context of e-learning which appear to be the main contributions

of the paper This paper is focused on Iran as a developing

country in the Middle East, which possesses a large population of

over 75 million individuals, 37 million of which according to

Internetworldstats.com (2012) are internet users, ranking Iran

first in the Middle East and fourth in Asia This study attempts to

fill a research gap by addressing the effects of quality features of

e-learning systems including educational quality, service quality,

technical system quality, and content and information quality,

accompanied with perceived ease of use and perceived usefulness

on students’ satisfactions and intentions towards use of e-learning,

besides investigating mediating effect of perceived ease of use on

intention through perceived usefulness

The remainder of the paper is structured as follows: we address

literature review in the next section This is followed by the

pre-sentation of the research hypotheses, discussion of findings,

con-clusions, and finally recommendations for future studies

2 Literature review

Owing to complicated, interrelated, and multi-faceted nature of

IS success, early attempts fell short in defining information system

success To address this problem, a success model was presented

com-pensate for changing in IS over time IS success model (DeLone &

McLean, 2003) identified six components of IS success as follows:

system quality, information quality, and service quality, intention

to use/use, user satisfaction, and net benefits In IS success model,

system use precedes user satisfaction and positive experience with

use contributes to the enhancement of satisfaction which

sequen-tially leads to a higher intention to use (Petter, DeLone, & McLean,

2008) The revised IS success model, as one of the most widely used

model for IS success, has so far been frequently adopted to examine

e-learning system success

The Technology Acceptance Model proposed by Davis and

Bago-zzi (Bagozzi, Davis, & Warshaw, 1992) appears to be the most

widely used innovation adoption model This model has been used

in a variety of studies to explore the factors affecting individual’s

use of new technology (Venkatesh & Davis, 2000).Davis (1989)

suggests that the sequential relationship of

belief–attitude–inten-tion–behavior in TAM, enables us to predict the use of new

tech-nologies by users In fact, TAM is an adaptation of TRA in regard

to IS which notes that perceived usefulness and perceived ease of

use determine an individual’s attitudes towards their intention to

use an innovation with the intention serving as a mediator to the

actual use of the system Perceived usefulness is also considered

to be affected directly by perceived ease of use

Cheng (2012)in his study to examine whether quality factors can affect learners’ intention to use e-learning system, incorpo-rated instructor quality to other components of IS success model and concluded that information, service, system, and instructor quality play the antecedent role and come to be as the key drivers

of employees’ perceptions with regard to e-learning acceptance Saba (2013), who carried out a study on implications of e-learning systems and self-efficacy on students’ outcomes, concluded that system quality, information quality, and computer self-efficacy all affected system use, user satisfaction, and self-managed learn-ing behaviors of student.Kim, Trimi, Park, and Rhee (2012)on their study on investigating the impact of quality on the outcomes of e-learning based on IS success model, found that system quality, information quality, and instructional quality positively influence user satisfaction.Li et al (2012)identified that e-learning service quality, course quality, perceived usefulness, perceived ease of use, and self-efficacy directly affect, system functionality and sys-tem response indirectly affect, while syssys-tem interactivity insignif-icantly affects on users’ intentions towards use Chang (2013) showed that web quality significantly and positively influences user value and user satisfaction; furthermore, he concluded that perceived value and satisfaction play the antecedent role in user’s intention towards use of e-learning.Wang and Chiu (2011)who incorporated communication quality, information quality, and ser-vice quality in his model showed that all had significant positive effects on user satisfaction and loyalty intention to use the e-learn-ing system for interacte-learn-ing experience, collaborate-learn-ing with others, and getting feedback Owing to the rarity of research in examining the students’ learning satisfaction with system quality of a system, Tajuddin, Baharudin, and Hoon (2013)carried out a study to exam-ine the relationship between learning satisfaction and system quality which revealed a positive relationship According to Tseng, Lin, and Chen (2011), the most significant determinants of e-learning effectiveness were the quality of the e-learning system and learner attractiveness In his study, increased usage of multi-media features was figured out to attract learner’s attention and eventually improve his attractiveness and reduction in the response time and waiting time for materials to load was found

to improve the quality of the system; accompanied with the responsiveness of instructors to learners’ questions which need

to be maintained and improved.Islam (2012)who included per-ceived system quality in the his expectation–confirmation based

IS model revealed that perceived usefulness, confirmation of initial expectation, and system quality significantly influenced students’ satisfaction, sequentially satisfaction in addition to perceived usefulness significantly determined continuance intention towards e-learning usage.Udo, Bagchi, and Kirs (2011)indicated an instru-ment for assessing e-learning quality comprises five components including assurance, empathy, responsiveness, reliability, and website content that four of which (except reliability) are valid and reliable constructs to measure e-learning quality and influence learners’ satisfactions and intentions to attend in online courses

2.1 Other related theories and studies

On the other hand, there are other related theories that deserve

to be mentioned These are theories such as Theory of Planned Behavior (TPB) which discusses that adoption behavior is preceded

by behavioral intention which in itself is a function of the individ-ual’s attitude, their beliefs about the extent to which they can con-trol a particular behavior and other external factors; Social Cognitive Theory (SCT) is a framework for understanding, predict-ing, and changing behavior which introduces human behavior as a result of the interaction between personal factors, behavior, and the environment; Diffusion of Innovation Theory (IDT) which con-siders adoption of IS as a social construct that gradually develops

through the population over time; the Decomposed Theory of

Planned Behavior (DTPB), an extended version of TAM, which

mod-els perceived ease of use and perceived usefulness as mediators of

behavioral intention in which compatibility serves as an

anteced-ent for both of them, and the Unified Theory of User Acceptance

of Technology (UTAUT) which notes that four key constructs

(per-formance expectancy, effort expectancy, social influence, and

facil-itating conditions) are the main determinants of consumers’ usage

intention and behavior (Hanafizadeh, Byron, & Khedmatgozar,

2014)

The empirical study conducted byAlsabswy et al (2013)

con-firmed that the role of IT infrastructure services is vital in

e-learn-ing service success through positively influence-learn-ing perceived

usefulness, user satisfaction, customer value, and organizational

value According to Ossiannilsson (2012), technology and digital

study accelerates the change of academic learning, but more

emphasis should be put on cultural and structural changes without

which technology fall short in changing it; in fact, technology

should play the supporting role in this regard

Sloan, Porter, Robins, and McCourt (2014)in his study on in

e-learning challenges on how to support international postgraduate

students identified that providing student with support and

feed-back in understanding the content is of immense importance

Sawang, Newton, and Jamieson (2013)figured out that high levels

of support can compensate for low technological efficacy in

e-learning adoption and that e-e-learning characteristics mediate the

relationship between learner characteristics and intention towards

its further adoption.Lambropoulos, Faulkner, and Culwin (2012)

found that e-teacher play a critical role to facilitate and support

active participation and engagement towards collaborative

learn-ing In accordance withLara, Lizcano, Martinez, Pazos, and Riera

(2014)study, temporal and spatial gap between the teacher and

student appears to be an impediment to student follow-up by

tea-cher, and student supervision is essential for the distinction of

stu-dent behaviors that bring about course dropout.Steet and Goh

(2012)who conducted a study on exploring the acceptance of an

e-reader device as a collaborative learning system, identified that

provision of five major determinants including mobility, support,

connectivity, immediacy, collaborative support significantly affect

users’ acceptance of proposed system; while sustainability

affor-dance, was found to have limited influence on the acceptance of

proposed system.Jordan (2013)identified that students’

effective-ness mostly relies on the teachers’ way of designing and orienting

the online learning experience In other words, the succession of an

online learning environment emanated from a strong pedagogical

method that put emphasis on a constructivist approach in practice,

without which the technology tool fall short to guarantee the

suc-cession online learning by its own.Leeds (2014)in their study on

how technology changes temporal culture in e-learning, found that

starting to study online for the first time, e-learners may

experi-ence temporal culture shock which needs to be addressed in

e-learning program Therefore, their time preferences need to be

included in designing an e-learning environment to make certain

that it’s equipped with enough temporal flexibility, and it should

be explicit so that learner expectation can be managed Chang

(2013)figured out that perceived value determines users’

inten-tions towards use of system He also added that perceived support

had a significant effect on perceived usefulness of the e-learning

system.Islam (2013)identified that there exist to be three main

constructs significantly affect students’ perceptions including

per-ceived learning assistant, perper-ceived community building assistant,

and perceived academic performance which are influenced by

per-ceived usefulness and perper-ceived ease of use and how an e-learning

system is used.Xu, Huang, Wang, and Heales (2014), on the other

hand, concluded that personalized e-learning facilities improve

online learning effectiveness in terms of examination, satisfaction, and self-efficacy criteria

Learning in future will be reoriented along concepts of collabo-ration and networking (Ossiannilsson & Landgren, 2012).Alverez,

e-learning provides the possibility of offering hybrid courses which

is a blend of face-to face classroom instruction with web-based learning.Barker et al (2013)concluded that e-learning, although welcomed by students, needs to be supplementary to face-to-face learning In his study, four main themes were identified, moving with the times, global networking, inequity as a barrier, and transfer of internet learning into practice Corti-Novo,

e-learning and face-to-face learning clearly improves the participa-tion of students, increase their motivaparticipa-tion, competencies and so, their performance in terms of qualifications The study carried out byRolstadas (2013)suggested that training based on a combi-nation of on-campus and web-based learning is an effective approach and this approach is more beneficial than those tradi-tional with only plenary session or virtual content Hajili, Bugshan, Lin, and Featherman (2013)in their study on the impact

of Web 2.0 emergence in learning context and its benefits and val-ues in education notes that the future of e-learning is social learn-ing, in which learning online is facilitated due to the prevalence of social media In fact, social relationships and interactions of indi-viduals in the internet and online communities clearly improve their learning competencies and qualities Diamond and Irwin (2013) identified that e-learning facilities were mostly adopted

to provide flexible access to information, followed by support for communication and collaboration, and were scarcely used for the development of specific skills, personal identity and confidence Gupna, Chauhan, and Dutta (2013)indicated that e-learning sys-tem radically changes the concept of education whether it is full time, part time, or a distant education program In their study, classroom teaching based on e-learning was well welcomed and paid attention by student as a precious experience and student were figured out to be very comfortable with the courses presented through e-learning and this virtual environment believed to strengthen the face to face classroom They further declared that quality of e-learning system influence the quality of teaching in educational sector Manca and Pozzi (2014) introduced a three dimensional model for evaluation of e-learning system based on which students, teachers, and e-learning managers should be involved in the evaluation He declared that platform, resources and approach are the e-learning system components to be assessed, and design, running and evaluation are the phases of the course lifecycle to be analyzed Troussas, Virou, and Alepis (2013)who put collaboration among learners into consideration

in a computer assisted learning environment, found that effective collaboration learning results when students appropriately per-ceive the significance of working actively with others in order to learn and act in ways which improve the educational procedure and emphasize the value of cooperation Zhang, Fang, Wei, and Wang (2012)in their study on investigating the intention of stu-dents to continue their participation in e-learning system, found that psychological safety communication environment evokes the intention to continue participation in e-learning.Chen and Tseng (2012)found that motivation to use and internet self-efficacy both had significant positive effects while computer anxiety had a sig-nificant negative effect on intention towards web-based e-learn-ing Perceived usefulness and motivation to use were ultimately found key reasons for the acceptance of e-learning system in their study

Kim, Lee, and Ryu (2013)showed that taking learners’ personal-ity characteristics and its effects on learning preferences into

consideration empowers us to improve both the primary learning

experience and the information derived.Fryer, Bovee, and Nakao

participating and involving in e-learning studies incorporating

poor ability belief and low task value.Chien’s (2012)study

con-cluded that learner’s computer self-efficacy is a primary reason

affecting e-learning self-effectiveness Chien (2012) further

dis-cussed that both system factors involving functionality and

instructor factors involving self-efficacy contributes to greater

e-learning effectiveness, alongside the fact that learner’s computer

self-efficacy moderates the relationship between system

function-ality and training effectiveness Poulova and Simonova (2014)

found that learners experienced higher inner satisfaction mostly

with an instruction method which reflects their preferences

Gonzalez-Gonzalez, Gallardo-Gallardo, and Jimenez-Zarco (2014)

introduced critical thinking as one of the students’ prerequisite

competencies to improve the efficiency and efficacy of their

activ-ities.Wang (2014)in his study provided evidences indicating that

personalized dynamic assessment automatically developed by

sys-tem for each learner strengthened students learning effectiveness

and facilitated their learning achievements and disappeared

mis-conceptions Le (2012)concluded that e-portfolio results in the

improvement of educational qualities since teaching and learning

focus is transferred from supervisor-centered to student-centered

learning and research, as well as from technological control to

technological empowerment According to his study, e-portfolio

enables students to completely overcome to their own learning

and research practices

Bhuasiri, Xaymoungkhoun, Zo, Rho, and Ciganek (2012)

informed that technology awareness, motivation, and changing

learners’ behavior are three major requirements for successful

implementation of e-learning Al-Samarraie, Teo, and Abbas

enhancement of e-learning motivation, attention, and interactivity

which results in students’ better thinking skills, and influences

their meta-cognitive activities and facilitates understanding.Yoo,

Han, and Huang (2012)declared that intrinsic motivators including

effort expectancy, attitudes, and anxiety more rigorously orient

intention towards use of e-learning than did the extrinsic

motiva-tors including performance expectancy, social influence, and

facil-itating conditions Castillo-Merino and Serradell-Lopez (2014)

informed that motivation is the most important variable

influenc-ing performance of online students which is positively influenced

by students’ perception of their own efficiency He further declared

that their perception about their ability to use digital technologies

leads to better achievements

Islam (2014)who adopted his theoretical assumptions from

Oli-ver’s expectation–confirmation theory, Herzberg’s two-factor

the-ory and Kano’s satisfaction model concluded that individuals’

satisfactions towards e-learning were mostly resulted from both

environmental and job-specific factors while their dissatisfactions

were mostly resulted from only environmental factors Moreno,

Moreno, and Molina (2013)who studied how satisfaction is

gener-ated towards e-learning system, identified that disconfirmation in

the case of measuring expectation before using the service, and

expectation in the case of measuring expectation after using the

service, occurs as the most important in the model He further

added that perceived usefulness and effort expectancy positively

affect satisfaction

Fiorella and Mayer (2014)indicated that those who actually

taught the material by explaining the material to others,

outper-formed those who did not teach; though, this effect was

stron-gest for those who were prepared to teach In his attempt,

preparing to teach resulted in short-term learning gains, whereas

the act of teaching coupled with preparing to teach was

impor-tant for long-term learning Hopp (2013) identified that

stan-dardized blended-learning system of mobile and e-learning can

be a powerful tool to increase e-learning benefits to the maximum

In the context of m-learning as a form of e-learning also some investigations are carried out as follows: Viberg and Gronlung

were most positive with personalization, followed by collabora-tion and authenticity They concluded that Hofsted’s factors failed

to explain the differences in students’ attitudes; gender in their study was identified as influential Mahat, Ayub, and Wong (2012) discovered the significant effects of three main factors including personal innovativeness, readiness to use, and self effi-cacy on students’ intentions towards m-learning adoption Cheon, Lee, Crooks, and Song (2012)informed that TPB appropri-ately explained college students’ adoption of m-learning, and atti-tude, subjective norm, and behavioral control positively affected their intention to adopt mobile learning Liu, Li, and Carlsson (2010) indicated that perceived near-term/long-term usefulness and personal innovativeness significantly affect intention towards m-learning adoption, while perceived long-term usefulness had a significant influence on near-term usefulness Hwang and Tsai

learning from 2001 to 2010, found that the number of articles had significantly increased during the past 10 years and research-ers had focused on the related fields in recent years Wu et al

of mobile learning studies from 2003 to 2010, identified that most studies in the context of mobile learning had investigated effec-tiveness, followed by mobile learning system design They further figured out that surveys and experimental approaches were the most preferred research methods, research outcomes were signif-icantly positive in mobile learning studies, mobile phones and PDAs were the most frequently used devices for mobile learning, and mobile learning was mostly adopted by higher education institutions

2.2 E-learning

Networked devices are growingly used for educational pur-poses and have applied a radical change in the scope of education (Ehlers & Hilera, 2012; Hsu, Hwang, & Chang, 2013) E-learning can be defined as making use of technology as a mediating tool for learning through electronic devices which enable users to readily access information and interact with others online (Wu

et al., 2012) The learning styles falls into four categories com-prises the computer-aided learning, e-learning, remote learning, and on-line learning (Ho & Dzeng, 2010) The former three ones are the learning ways conducted through electronic media, such

as CD, auxiliary software, and interactive TV The online learning

is conducted through internet or intranet to generate the interac-tion among learners, course, and teacher E-learning indeed is a form of online learning; therefore, online learning is called e-learning at present (Ho & Dzeng, 2010) E-learning seeks to improve the culture of equal participation among students and teachers for them to share their efforts to gain greater success (Shipee & Keengwee, 2014) and better achieve the key educational objective which is the enhancement of learning effectiveness and efficiency Thus, the students’ perceptions of e-learning technolo-gies are of great importance and precede the successful integration

of these technologies in education (Ozdamli & Uzunboylu, 2014) Therefore, exploring the learners’ perceptions concerning e-learn-ing are of immense importance to researchers, because it helps educational institutions such as schools, colleges and universities, and even organizations to get a real advantage by enabling enhanced understanding of key factors that affect intention to use e-learning

3 Research model and Hypotheses

In this section, the research variables and hypotheses are

presented

3.1 Educational quality

Educational quality, as a new component to IS success model

incorporated byHassanzadeh et al (2012), is seen as system

qual-ity in terms of characteristics and features it can render to facilitate

users learning and training (Hassanzadeh et al., 2012) Educational

quality can be defined as the extent to which an IS system

man-aged to provide a conductive learning environment for learners

in terms of collaborative learning (Hassanzadeh et al., 2012; Kim

et al., 2012) AsHassanzadeh et al (2012)concluded in their study,

educational quality positively affects individuals’ satisfactions

which is also confirmed by Kim et al (2012) who found that

instructional quality have a significant positive effect on user

satis-faction Educational quality, therefore, is assumed to have a

posi-tive effect on individuals’ satisfaction; however, it is assumed to

have a positive effect on intention to use as well

H1 Educational quality positively affects user satisfaction

H2 Educational quality positively affects intention to use

3.2 Service quality

Service quality constitutes the quality of the support that users

receive from the IS (Wang & Wang, 2009) such as training (Petter &

McLean, 2009) and helpdesk The inclusion of this success

dimen-sion is not undoubted, since it normally seen as subordinate to

sys-tem quality in the model, but some researchers claim that it could

stand as an independent variable owing to the great change in IS

role in recent years (Wang & Liao, 2008) Service quality has been

found to have a significant positive effect on satisfaction in

e-learn-ing context (Poulova and Simonova, 2014; Roca, Chiu, & Martinez,

2006; Tajuddin et al., 2013; Wang & Chiu, 2011; Xu et al., 2014),

and on intention to use e-learning system in some studies

(Cheng, 2012; Hassanzadeh et al., 2012; Li et al., 2012; Ramayah,

Ahmad, & Lo, 2010; Wang & Chiu, 2011) In this study, service

quality is assumed to have a positive impact on both individuals’

satisfaction and their intentions to use

H3 Service quality positively affects user satisfaction

H4 Service quality positively affects intention to use

3.3 Technical system quality

In IS success model proposed by DeLone and McLean (2003),

technical system quality refers to technical success and the

accu-racy and efficiency of the communication system that produces

information; in fact, it constitutes the desirable characteristics

and measures of an IS and relates to the presence and absence of

a bug in system (Rabaa’i, 2009) Technical system quality has been

found to have a significant positive effect on satisfaction in

e-learning context (Alsabawy et al., 2013; Hassanzadeh et al.,

2012; Islam, 2012; Kim et al., 2012; Motaghian et al., 2013; Rai,

Acton, Golden, & Conboy, 2009; Saba, 2013; Tajuddin et al.,

2013; Wang & Chiu, 2011; Wu, Hsia, Liao, & Tennyson, 2008),

and on intention to use e-learning system (Cheng, 2012; Islam,

2012; Li et al., 2012; Ramayah et al., 2010; Wang & Chiu, 2011)

Technical system quality, thus, is assumed to have a positive effect

on both individuals’ satisfaction and their intentions towards use

of system

H5 Technical system quality positively affects user satisfaction

H6 Technical system quality positively affects intention to use

3.4 Content and information quality

The success dimension content and information quality repre-sents the desirable characteristics of an IS’s output (Petter & McLean, 2009) An example would be the information the system and student can generate using the e-learning system Thus, it includes measures focusing on the quality of the information that the system generates and its usefulness for the user Information quality is often seen as a key antecedent for user satisfaction (Hassanzadeh et al., 2012; Kim et al., 2012; Roca et al., 2006; Saba, 2013; Wang & Chiu, 2011), and for intention to use e-learning system (Cheng, 2012; Ramayah et al., 2010; Wang & Chiu, 2011) In this study, therefore, content and information quality is assumed

to have a positive impact on both individuals’ satisfaction and their intentions to use

H7 Content and information quality positively affects user satisfaction

H8 Content and information quality positively affects intention to use

3.5 Perceived ease of use

Perceived ease of use is defined as the degree to which a person believes that using a particular system would be free of effort (Davis, 1989), which is an imminent acceptance driver of new technology-based applications (Venkatesh, 2000) The effect of perceived ease of use on intention towards use of e-learning is revealed in some past studies (e.g.,Chen and Tseng, 2012; Chow, Herold, Choo, & Chan, 2012; Islam, 2013; Li et al., 2012; Liu

et al., 2010; Sumak et al., 2011) As a result, the greater the per-ceived ease of use of e-learning system, the more positive is the intention towards its usage; thus greater the likelihood that it will

be used Moreover, perceived ease of use is assumed to have an indirect effect on intention to use through perceived usefulness

in e-learning context as well (Chen and Tseng, 2012) Therefore, perceived ease of use is further expected to have an indirect effect

on users’ intentions via perceived usefulness

H9 Perceived ease of use positively affects intention to use

H10 Perceived ease of use positively affects perceived usefulness

3.6 Perceived usefulness

Perceived usefulness is a key determinant of intention, which encourages 21st century IS users to adopt more innovative and user-friendly technologies that give them greater freedom (Pikkarainen, Pikkarainen, and Karjaluoto, 2004) In fact, an individual’s willingness to use a specific IS for their activities depends on their perception of its use (Hanafizadeh, Behboudi, Khoshksaray, & Shirkhani Tabar, 2014) Perceived usefulness has been found to have a significant positive effect on usage intention

towards use of e-learning services (Chen and Tseng, 2012; Cheng,

Wang, Moormann, Olaniran, & Cheng, 2012; Chow et al., 2012;

Islam, 2012, 2013; Li et al., 2012; Liu et al., 2010; Sumak et al.,

2011) As a consequence, the greater the perceived usefulness of

e-learning system, the more positive is the intention towards its

usage; thus greater the likelihood that it will be used

H11 Perceived usefulness positively affects intention to use

3.7 Satisfaction

Rather than to sell, to supply, or to serve, the main objective of

every business is to satisfy the needs and meet the satisfaction of

its users (Docimini and Palumbo, 2013) Satisfaction is defined as

the individuals’ perceptions of the extent to which their needs,

goals, and desires have been fully met (Sanchez-Franco, 2009)

and refers to their overall view of IS (Wang & Wang, 2009) It

sounds better to note that user satisfaction refers to the extent to

which users are pleased with IS and support services (Petter

et al., 2008) The updated IS success model assumes that system

use precedes user satisfaction which leads to an increased

satisfac-tion which sequentially results in a higher intensatisfac-tion to use (Petter

et al., 2008) Satisfaction has been found to have a significant

posi-tive effect on intention towards use of e-learning services in some

studies (Chang, 2013; Hassanzadeh et al., 2012; Islam, 2012; Petter

et al., 2008; Roca et al., 2006; Udo et al., 2011) Satisfaction has

been found to have a significant positive effect on actual use as

well.Hassanzadeh et al (2012)in their study uncovered the

posi-tive effect of satisfaction on actual use of e-learning system

There-fore, in the context of this study, satisfaction assumed to have a

positive impact on both intention to use and actual use

H12 Satisfaction positively affects intention to use

H13 Satisfaction positively affects actual use

3.8 Intention to use

Intention, which is the main dependent variable identified in the studies conducted based on the TAM, is defined as the likeli-hood that an individual will use an IS Intention plays a critical role

in the actual use of a new technology (Davis, 1989) Intention to use can also be considered as an attitude (DeLone & McLean,

2003) In the acceptance domain, some researchers have studied the relationship between intention and actual use in e-learning context (e g.,Alkhalaf, Drew, AlGhamdi, & Alfarraj, 2012; Chow

et al., 2012; Hassanzadeh et al., 2012).Petter et al (2008)note that

to refrain more complexity, IS success model did not distinct between intention to use and system use in their updated model, but intention to use is generally an individual level construct Venkatesh, Morris, Davis, and Davis (2003)confirms the positive relationship between intention to use and actual use Thus, in the context of this study, intention to use assumed to have a positive impact on actual use.Table 1lists the dimensions’ and definitions; Fig 1shows the conceptual model

H14 Intention to use positively affects actual use

4 Instrument development

The final structured instrument was used to collect data using a seven-point Likert scale: perceived usefulness and perceived ease

of use were adopted fromKim and Mirusmonov (2010), intention

to use fromLin (2011), system, service, and information quality, and satisfaction fromDeLone and McLean (2003), and educational quality along with actual use fromHassanzadeh et al (2012)

To ensure the validity of the instrument, the first Confirmatory Factor Analysis (FCFA) was taken Studying the interior structure of

a collection of indices and validity measures, this approach sought

to evaluate factor loadings and relationships between a collection

of indices and their corresponding factors As seen inTable 2, in the FCFA of sample group (20% of total), except for four indices, almost all indices received the standardized factor loadings larger than the recommended value (0.4); thus, having excluded the inva-lid indices, the model was tested with other selected indices so that the instrument to be valid

4.1 Data collection

The research aimed to understand the e-learning satisfaction and intention towards actual use of e-learning in Tehran early

2014 This period was marked by recent developments in Iran

Table 1

Definitions of dimensions.

Educational quality A conductive learning environment in terms of collaborative learning Kim et al (2012), Hassanzadeh et al.

(2012) Service quality The quality of the support that users receive from IS system Petter et al (2008)

Technical system

quality

The desirable characteristics and features of IS system Petter et al (2008) Information quality The desirable characteristics and features of the output Petter et al (2008)

Perceived ease

usefulness

The degree to which a person believes that using a particular system would enhance his or her job performance

Davis (1989) Perceived ease of use The degree to which a person believes that using a particular system would be free of effort Davis (1989)

Satisfaction The extent to which user believe that their needs, goals, and desires have been fully met Sanchez-Franco (2009)

Intention to use Key likelihood that an individual will use a technology Schierz, Schilke, and Wirtz (2010)

H2

H12

H1 H3 H4

H5

H6

H7

H8

H9

H11

H13

H14

H10

Educational

Quality

Service

quality

Technical

system quality

Content and

information quality

Intention to use

Satisfaction

Actual use

Perceived

usefulness

Perceived ease

of use

Fig 1 Research model.

which push researchers and educators to take a pedagogical view

towards developing educational applications to promote teaching

and learning; hence, this study offers an appropriate window for

studying variations in educator’s intention The sample is taken

from the students of four public universities of Tehran including

Elm-o-Sanat, Amir Kabir, Shahid Beheshti, and Tehran universities

The final questionnaire was arrived at after examining theoretical

literature and studies undertaken by previous researchers based

on which indices were selected (Table 2)

The research used stratified sampling – since it was concerned

with different attributes of research population The research

model uses a cross-sectional survey In fact, the research model

is investigated based on views expressed by the respondents at one point of time This approach, as one of the common approaches, was taken due to theoretical and survey limitations

In the Cochran formula for finite population, with Za

2value of about 1.96, e value less than 0.1 of about 0.099 andqvalue of about 0.5, each university was calculated at a minimum of 81 Students had

to confirm they are users of e-learning system before the question-naire was released to them A total of 420 students were selected Next, participants were intercepted in randomly chosen faculties where questionnaires were physically administered to them There were a total of 105 questionnaires for each university in three main faculties, out of which 390 were gathered This research is practical

Table 2

The research instrument.

loading Educational quality E-learning assures the presents of students Chang and Chen (2009) 0.77

E-learning provides collaborative learning Hassanzadeh et al (2012) 0.71 E-learning provides required facilities such as chat and forum Lee (2010) 0.69 E-learning provides possibility of communicating with other

students

E-learning provides possibility of learning evaluation Hassanzadeh et al (2012) 0.32 E-learning is appropriate with my learning style Vernadakis, Antoniou, Giannousi, Zetou, and

Kioumourtzoglou (2011)

0.63 Service quality E-learning provides a proper online assistance and

explanation

E-learning department staff responds in a cooperative manner Au, Ngai, and Cheng (2008) 0.81 E-learning provides me with the opportunity of reflecting

views

E-learning provides me with courses management Au et al (2008) 0.66 Technical system

quality

E-learning provides interactive features between users and system

E-learning has attractive features Wang, Wang, and Shee (2007) 0.83

Information quality E-learning provides information that is relevant to my needs Au et al (2008) 0.82

E-learning provides comprehensive information Ho and Dzeng (2010) 0.64 E-learning provides information that is exactly what I want Wang and Wang (2009) 0.72 E-learning provides me with organized content and

information

E-learning provides up to date content and information Wang and Liao (2008) 0.65 E-learning provides required content and information Wang et al (2007) 0.57

Perceived usefulness E-learning helps to save time DeLone and McLean (2003) 0.64

E-learning helps to improve my knowledge Hassanzadeh et al (2012) 0.69 E-learning helps to improve my performance Hassanzadeh et al (2012) 0.73

E-learning satisfies my educational needs Lee, Yoon, and Lee (2009) 0.63

I am likely to use e-learning system in the near future Lin (2011) 0.66

in nature and the goal was to conduct it from an extensive

perspec-tive; it was thus, exploratory and descriptive in approach Alpha

Cronbach for the questionnaire emerged to be 0.839

n ¼ N  z

a 2

2 p  q

e2 ðN  1Þ þ za22 p  q

Formula 1 Cochran formula for finite population

5 Data analyses

5.1 Response rate and representatives

ninety out of four hundred twenty questionnaires were collected

with valid data The discard rate was low

The total population of Iranian students by sex and age group

was obtained from Iran Center of Census and Statistics This was

compared to the gender and age distribution of the sample in order

to test its’ representativeness In terms of gender, the distribution

of the sample was 51.8% male and 49.2% female According to

the Technology and Science Minister’s latest report, by end of

2013, the male to female population of student ratio in Iran was

47% and 53%; thus the sample appeared to be representative in

terms of gender distribution Having analyzed the demographic

characteristics of e-learning students, it was concluded that most

of them (87.7%) were in the age group of 20–30 years followed

by those in the age group of 20-years (12.3%) The population of

Iranian e-learning students shared a similar age distribution of

78% and 22% respectively This indicates that the sample is

repre-sentative of the Iranian e-learning population In addition, MA

stu-dents (71.8%) dominated other groups Table 4 presents the

demographic characteristics of the sample

5.2 Exploratory and confirmatory analysis

To perform an exploratory analysis, convergent and

discrimi-nant validities and scale reliability are considered (Fraering and

effectively reflect their corresponding factors, while discriminant validity measures whether two factors are statistically different from each other (Anderson and Gerbing, 1988) Following the two-step approach proposed by Anderson and Gerbing (1988),

we first examined the measurement model to test its reliability and validity Then we examined the structural model to test the model fitness and the relationships between variables

Table 5lists Average Variance Extracted (AVE), Composite Reli-ability (CR), R square (R2), Communality, and Cronbach alpha val-ues, and standardized factor loadings As seen inTable 5, almost all factor loadings are larger than 0.4, while t-values (shown in Fig 3) indicate that all of them are significant at 0.05 All AVEs exceed 0.5, all CRs (the degree to which items are free from ran-dom error and therefore render consistent results) exceed 0.7, and all communalities exceed 0.7 showing minimally accepted construct reliability (Gefen, Straub, & Boudreau, 2000) Thus, the scale has a good convergent validity In addition, all alpha values are larger than 0.7, showing good reliability (Nunnally, 1978)

On the other hand, intention – with an R2of about 0.63 is pro-ven to be well predicted by its predictors and the remaining 0.36 is the prediction error Besides, satisfaction, with an R2of about 0.22

is partially forecasted by its predictor, and the remainder 0.77 is the prediction error Therefore, users’ intention is proved to be a strong predictor of their actual use of e-learning At last, actual use – with an R2of about 0.73 is proven to be well predicted by its predictors which are users’ intentions and satisfaction More-over, the indices used for ‘‘satisfaction’’, ‘‘intention’’, and ‘‘actual use’’ gained larger factor loadings than the recommended values which underlines their careful selection

To examine the discriminant validity, the squared roots of the AVEs are compared with the factor correlation coefficients As listed inTable 6, for each factor, the square root of AVE is larger than its correlation coefficient with other factors, showing good discriminant validity (Gefen et al., 2000) In the second step, we employed structural equations modeling by LISREL 8.80 to esti-mate the structural model

5.3 Path coefficient

As listed inTable 7, among the factors influencing satisfaction, information quality (c= 0.29, p < 0.01) and technical system qual-ity (c= 0.29, p < 0.01) showed the greatest effects, educational quality (c= 0.27, p < 0.01) and service quality (c= 0.24, p < 0.01) had significant paths as well Among the factors influencing inten-tion to use, technical system quality (c= 0.23, p < 0.01), and service quality (c= 0.17, p < 0.01), information quality (c= 0.13, p < 0.01) had respectively significant positive paths However, educational quality (c= 0.03) showed no significant effect in this regard Per-ceived usefulness (b = 0.52, p < 0.001) had significant positive path towards intention, while perceived ease of use (c= 0.07) showed

no significant effect on the intention to use Furthermore, per-ceived ease of use (c= 0.16, p < 0.001) had a significant effect on perceived usefulness Satisfaction (b = 0.52, p < 0.001) also appeared to have a significant positive path towards intention to use Finally, satisfaction (b = 0.18, p < 0.001) and intention to use

Table 3

Sample selection.

University Students (N) in each university n for each university Frequency of sample/population Percentage (%)

Table 4

The demographic characteristics of the sample.

Gender

Age

Education

(b = 0.85, p < 0.001) both positively affected actual use of

e-learning Therefore, all paths exceptH2andH9are supported

Path coefficients and their significances are listed inFigs 2 and 3

5.4 Measurement of the model fitness

To ensure that the measurement model possesses a sufficiently

good model fit, the overall model fit is assessed in terms of seven

common measures: Normedv2- the ratio ofv2to the degree of

freedom, Goodness of Fit Index (GFI), Comparative Fit Index (CFI), Normed Fit Index (NFI), Non-Normed Fit Index (NNFI), Incremental Fit Index (IFI), and Root Mean Square Error of Approximation (RMSEA) A model fit is usually considered strong when Normed

v2is smaller than 3, GFI is larger than 0.8, CFI, NFI, NNFI, and IFI are larger than 0.9, and RMSEA is around 0.06 Table 8lists the recommended and actual values of fit indices The actual values

of all fit indices were better than the recommended values, showing a superior fit

Table 5

Main statistics.

Table 6

The square root of AVE (italic at diagonal) and correlation coefficients.

5.5 Path analysis

As seen inFig 4, the path to ease of use: usefulness–intention–

actual use was tested via path analysis, in which the path was

proved to be significant atq= 0.000 On the other hand, the path

ease of use–intention did not prove to be significant atq= 0.000

which underlies the insignificant path coefficient derived from

structural model The ratio of path loading to standard error

indi-cates that path loadings are greater than twice their standard

errors showing convergent reliability; their variances also

substan-tiate the decision to use The results of path analysis involving

regression coefficients and their significances are listed inTable 9

If the indirect path gains a greater effect than that of direct one, then indirect path would prove to be mediator – that is a direction which leads us to actual use faster As showed inTable 10, to exam-ine the mediating effect of perceived usefulness in the relationship between ease of use and intention, both the direct and indirect effects of perceived ease of use were tested Given the insignifi-cance of the path ease of use-intention (0.07), perceived ease of use can only affect intention through usefulness (0.083); therefore, the mediating role of usefulness (H10) is proven to be significant at the 0.05 significance level

6 Discussion

In view of the fact that user satisfaction and intention to use both affect users’ actual use positively, it can be concluded that educational quality, service quality, technical system quality, and information quality – those with significant effects – positively affects users’ actual use – all indirectly and through satisfaction and intention In fact, the e-learning system posses users’ relative confidence about educational quality, service quality, technical system quality, and information quality; among which technical system quality appears to have a greater positive effect than oth-ers This confirms whatHassanzadeh et al (2012)concluded in their study in which technical system quality was found to be the strongest factor affecting users’ satisfaction of e-learning sys-tem in Iran.Alsabawy et al (2013), Motaghian et al (2013), Saba (2013), Tajuddin et al (2013), Kim et al (2012), and Islam (2012), in their studies into e-learning systems, found that system quality positively affects user satisfaction as well, which corre-sponds with the studies undertaken byWang and Chiu (2011), Rai et al (2009), and Wu et al (2008) in e-learning context Islam (2012), Cheng (2012), Li et al (2012), Ramayah et al

Table 7

Path coefficients and significances.

Question Path Path coefficient Supported or not

H1 Educational ? satisfaction 0.27 ** Yes

H2 Educational ? intention 0.03 No

H3 Service ? satisfaction 0.24 ** Yes

H4 Service ? intention 0.17 *** Yes

H5 System ? satisfaction 0.29 ** Yes

H6 System ? intention 0.23 *** Yes

H7 Information ? satisfaction 0.29 ** Yes

H8 Information ? intention 0.13 ** Yes

H9 Ease of use ? intention 0.07 No

H10 Ease of use ? usefulness 0.16 * Yes

H11 Usefulness ? intention 0.1 *** Yes

H12 Satisfaction ? intention 0.52 *** Yes

H13 Satisfaction ? actual use 0.52 *** Yes

H14 Intention ? actual use 0.85 *** Yes

* Significance codes: 0.05.

** Significance codes: 0.01.

*** Significance codes: 0.001.