Factors affecting the e learning outcome

Factors affecting the e-learning outcomes: An integrationof 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

Factors affecting the e-learning outcomes: 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

Article history:

Received 14 December 2014

Received in revised form 28 February 2015

Accepted 4 March 2015

Available online 17 March 2015

Keywords:

E-learning

Quality

Satisfaction

Intention

Actual use

Perceived learning assistance

a b s t r a c t

The purpose of this paper is to examine an integrated model of TAM and IS success model

to explore the effects of quality features, perceived ease of use, perceived usefulness on users’ intentions and satisfaction and their effects on e-learning outcomes such as actual use and perceived learning assistance, alongside 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-learn-ing E-learning outcomes such as actual use and perceived learning assistance were posi-tively predicted by satisfaction and intention At last, ‘‘perceived usefulness’’ mediated the relationship between ease of use and users’ intentions The sample consisted of e-learn-ing users of four public universities in Iran Past studies have seldom examined an inte-grated 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

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1 Introduction

To meet educational purposes and students’ demands, e-learning development emerges to be a catalyst for today educa-tional institutions (Dominici and Palumbo, 2013; Alsabawy et al., 2013) E-learning can be defined as a dynamic and immedi-ate 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 (Jeong and Hong, 2013; Dominici and Palumbo,

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 (Martin and Ertzberger, 2013; Viberg and

impact on both teachers and students in that it positively affects the duration of their attention, learning and training tenac-ity, and their attitudes towards collaboration and interaction (Ozdamli and Uzunboylu, 2014; Chen and Tseng, 2012) Past studies have indicated that anywhere and anytime learning and access to information and communication are facilitated through using e-learning (Pena-Ayala et al., 2014; Islam, 2013; Chen and Tseng, 2012; Ho and Dzeng, 2010) Kratochvíl

http://dx.doi.org/10.1016/j.tele.2015.03.002

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Contents lists available atScienceDirect

Telematics and Informatics

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 / t e l e

(2013)andAbachi and Muhammad (2013)note that all individuals involved in e-learning are fond of using it towards learn-ing because of flexible access in terms of time, space, and pace and online collaborative learnlearn-ing However, demand for the development of e-learning is increasingly growing; still the need for research on potential factors affecting e-learning adoption like quality which is the heart of education and training in all countries (Ehlers and Hilera, 2012) and its outcomes,

is felt especially in the context of developing countries (Masoumi and Lindstrom, 2012), a fact that warrants investigation into it This is followed by the fact that Iranian students’ lack of preference for using e-learning, in spite of all pre-mentioned advantages, has created a gap which is seen as a major obstacle in its mass usage and warrants investigation of its reasons This is in spite of the fact that, asHassanzadeh et al (2012)quoted, many Iranian applicants do not have any access to higher education in face-to-face classes and E-learning systems can emerge as an alternative; what’s more, satisfy and compensate the weakness of traditional learning methods So, if we influentially make the best use of learning opportunities provided by computer-mediated and internet-enabled platforms such as e-learning systems, a remarkable result will expect youth and knowledge seekers

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 studies have taken the barriers and the drivers of e-learning adoption into consideration (e.g., Islam, 2014, 2013, 2012; Sumak et al., 2011; Chen and Tseng,

2012) 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 et al (2012)note, it is essential to examine the relationship between e-learners’ experiences, perceptions, and their behavioral intentions to use, because system use is an important indicator of the system’s success

tech-nical 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 et al (2013), in their attempts to assess the influence of 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 and outcomes in the context of developing countries This research, compared to

associa-tion of e-learning usage determinants and learning outcome based on an integrated model of TAM and IS success model and provides a literature review of recent outstanding related studies 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 toInternetworldstats.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 learning outcomes such as actual use and perceived learning assistance, 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 presentation of the research hypotheses, discussion of findings, conclusions, 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 informa-tion system success To address this problem, a success model was presented byDeLone and McLean (1992)which was later modified to compensate for changing in IS over time IS success model (DeLone and 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 sequentially leads to a higher intention to use (Petter et al., 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 Bagozzi (Bagozzi et al., 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 and Davis, 2000) Davis (1989) suggests that the sequential relationship of belief-attitude-intention-behavior in TAM, enables us to predict the use of new technologies 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

Table 1

Outstanding related studies in the area of e-learning usage.

Researcher Independent variable Dependent variable Key findings

Cheng

(2012)

Information, service, system, and instructor

quality

Intention to use Information, service, system, and instructor

quality come to be as the key drivers of employees’ perceptions with regard to e-learning acceptance

Saba

(2013)

System quality, information quality, and

computer self-efficacy

System use, user satisfaction, and self-managed learning behaviors

System quality, information quality, and computer self-efficacy all affected system use, user satisfaction, and self-managed learning behaviors of student

Kim et al.

(2012)

System quality, information quality, and

instructional quality

User satisfaction System quality, information quality, and

instructional quality positively influence user satisfaction

Li et al.

(2012)

Service quality, course quality, perceived

usefulness, perceived ease of use, and

self-efficacy, system functionality and system

response, system interactivity

Intention, e-learning usage E-learning service quality, course quality,

perceived usefulness, perceived ease of use, and self-efficacy directly affect, system functionality and system response indirectly affect, while system interactivity insignificantly affects on users’ intentions towards use

Chang

(2013)

Web quality, user value and user satisfaction Intention, e-learning usage 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)

Communication quality, information quality,

and service quality

User satisfaction, loyalty intention, and e-learning usage

Communication quality, information quality, and service quality in his model showed that all had significant positive effects on user satisfaction and loyalty intention to use the e-learning system for interacting experience, collaborating with others, and getting feedback Tajuddin

et al.

(2013)

System quality Learning satisfaction Revealed a positive relationship between

learning satisfaction and system quality Tseng

et al.

(2011)

Quality of the e-learning system and learner

attractiveness

E-learning effectiveness The most significant determinants of e-learning

effectiveness were the quality of the e-learning system and learner attractiveness; reduction in the response time and waiting time for materials to load was found to improve the quality of the system; responsiveness of instructors to learners’ questions, increased usage of multimedia features was figured out to attract learner’s attention and eventually improve his attractiveness

Islam

(2012)

Perceived usefulness, confirmation of initial

expectation, and system quality

Satisfaction, continuance intention, e-learning usage

Perceived usefulness, confirmation of initial expectation, and system quality significantly influenced students’ satisfaction, sequentially satisfaction in addition to the fact that perceived usefulness significantly determined continuance intention towards e-learning usage Udo et al.

(2011)

Quality components such as assurance,

empathy, responsiveness, reliability, and

website content

Satisfactions, intention 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 Alsabawy

et al.

(2013)

Perceived usefulness, IT infrastructure services User satisfaction, customer value,

and organizational value

IT infrastructure services is vital in e-learning service success through positively influencing perceived usefulness, user satisfaction, customer value, and organizational value Chang

(2013)

Perceived usefulness, perceived value,

perceived support,

Intention Perceived value determines users’ intentions

towards use of system He also added that perceived support had a significant effect on perceived usefulness of the e-learning system Islam

(2013)

Perceived usefulness and perceived ease of use Perceived learning assistant,

perceived community building assistant, and perceived academic performance

Three main constructs significantly affect students’ perceptions including perceived learning assistant, perceived community building assistant, and perceived academic performance which are influenced by perceived usefulness and perceived ease of use and how

an e-learning system is used

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 func-tion of the individual’s attitude, their beliefs about the extent to which they can control a particular behavior and other external factors; Social Cognitive Theory (SCT) is a framework for understanding, predicting, 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 considers adoption of IS as a social construct that gradually develops through the pop-ulation over time; the Decomposed Theory of Planned Behavior (DTPB), an extended version of TAM, which models per-ceived ease of use and perper-ceived usefulness as mediators of behavioral intention in which compatibility serves as an antecedent for both of them, and the Unified Theory of User Acceptance of Technology (UTAUT) which notes that four key constructs (performance expectancy, effort expectancy, social influence, and facilitating conditions) are the main deter-minants of consumers’ usage intention and behavior (Hanafizadeh et al., 2014b)

2.2 E-learning

Networked devices are growingly used for educational purposes and have applied a radical change in the scope of educa-tion (Hsu et al., 2013; Ehlers and Hilera, 2012) E-learning can be defined as making use of technology as a mediating tool for

Table 1 (continued)

Researcher Independent variable Dependent variable Key findings

Cheon

et al.

(2012)

System factors involving functionality and

instructor factors involving self-efficacy

E-learning effectiveness 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 functionality and training effectiveness Wang

(2014)

Personalized dynamic assessment developed by

system

Learning effectiveness Personalized dynamic assessment

automatically developed by system for each learner strengthened students learning effectiveness and facilitated their learning achievements and disappeared misconceptions Lee et al.

(2012)

E-portfolio Educational qualities E-portfolio results in the improvement of

educational qualities since teaching and learning focus is transferred from supervisor-centered to student-supervisor-centered learning and research, as well as from technological control

to technological empowerment, e-portfolio enables students to completely overcome to their own learning and research practices Islam

(2014)

Environmental, job-specific factors,

environmental factors

Satisfaction, dissatisfaction 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

Chen and

Tseng

(2012)

Perceived usefulness, motivation to use,

self-efficacy, computer anxiety

Intention Motivation to use and internet self-efficacy both

had significant positive effects while computer anxiety had a significant negative effect on intention towards web-based e-learning Perceived usefulness and motivation to use were ultimately found key reasons for the acceptance of e-learning system in their study Moreno

et al.

(2013)

Perceived usefulness and effort expectancy,

disconfirmation

Satisfaction 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, perceived usefulness and effort expectancy positively affect satisfaction

Xu et al.

(2014)

Personalized e-learning Online learning effectiveness

such as examination, satisfaction, and self-efficacy

Personalized e-learning facilities and improves online learning effectiveness in terms of examination, satisfaction, and self-efficacy criteria

Gupna

et al.

(2013)

Quality of e-learning system Quality of teaching E-learning system radically changes the concept

of education whether it is full time, part time, or

a distant education program, quality of e-learning system influence the quality of teaching in educational sector

learning through electronic devices which enable users to readily access information and interact with others online

learning, and on-line learning (Ho and Dzeng, 2010) The former three ones are the learning ways conducted through elec-tronic media, such as CD, auxiliary software, interactive TV etc The online learning is conducted through internet or intranet

to generate the interaction among learners, course, and teacher E-learning indeed is a form of online learning; therefore, online learning is called e-learning at present (Ho and 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 (Shippee and Keengwe,

2014) and better achieve the key educational objective which is the enhancement of learning effectiveness and efficiency Thus, the students’ perceptions of e-learning technologies are of great importance and precede the successful integration

of these technologies in education (Ozdamli and Uzunboylu, 2014) Therefore, exploring the learners’ perceptions concerning e-learning 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 towards use of e-learning

3 Research model and hypotheses

In this section, the research variables and hypotheses are presented

The IS success model theoretically supports the associations between determinants-satisfaction-behavior-outcomes of a system usage (Islam, 2013) Thus, based on the theoretical support from IS success model and TAM researches, it seems logical to base a framework by incorporating e-learning antecedents including quality aspects, perceived ease of use and usefulness, and learning outcomes including satisfaction, actual use, perceived learning assistance into a model It deserves mentioning that this framework could be justified by IS success and TAM models (Islam, 2013) Based on the technology adoption researches’ findings, we consider quality aspects, perceived ease of use and usefulness as e-learning usage determinants and suggest that these constructs impact e-learning outcomes On the other hand, according to IS success model, an individual’s use of IS systems is primarily determined by their satisfaction with use of that service Therefore,

in this study, individuals’ use of e-learning system was preceded by their satisfaction Satisfaction is a significant measure

of IS success and often regarded as the easiest and most useful way to evaluate an IS On the other hand their learning is believed to be improved when they are satisfied with the system and it’s a measure for learning effectiveness and develops learning performance (Xu et al., 2014)

through helping the participants manage and control their learning process This offers an influential and significant constructs regarding individuals’ learning processes, known as perceived learning assistance In other words, the use of e-learning system provides participants with such assistance Hence, we consider learning assistance as another e-learning usage outcome The reason behind these relationships is that online learning systems with high educational, information, service, and system quality are expected to offer an opportunity to learn more effectively and assist learners in process of learning indirectly through satisfaction and intention to use The indirect influences are theoretically supported by the IS success model (Islam, 2013)

As mentioned, many studies from different viewpoints have been conducted to find a proper model Hence, it is difficult

to accurately determine which approach is more important than another One of the methods through which a more appro-priate answer can be found is investigating the commonalities of the approaches; i.e variables emphasized by all scholars In this respect, as noted in the study, the critical factors and indicators of each factor are carefully extracted from previous literature and have been considered in order to provide the decision makers and researchers with a comprehensive package

of factors affecting e-learning usage Generally, by reviewing related literature, factors affecting the process of e-learning usage were identified and hypothesized

3.1 E-learning determinants

Educational quality, as a new component to IS success model incorporated byHassanzadeh et al (2012), is seen as system quality 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 managed to provide a conductive learning environment for learners in terms of collaborative learning (Kim et al., 2012; Hassanzadeh et al., 2012) AsHassanzadeh

quality, therefore, is assumed to have a positive effect on individuals’ satisfaction; however, it is assumed to have a positive effect on intention as well

H1 Educational quality positively affects user satisfaction

H2 Educational quality positively affects intention

Service quality constitutes the quality of the support that users receive from the IS (Wang and Wang, 2009) such as train-ing (Petter and McLean, 2009) and helpdesk The inclusion of this success dimension is not undoubted, since it normally seen

as subordinate to system 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 and Liao, 2008) Service quality has been found to have a signifi-cant positive effect on satisfaction in e-learning context (Xu et al., 2014; Poulova and Simonova, 2014; Tajuddin et al., 2013;

positive impact on both individuals’ satisfaction and their intentions

H3 Service quality positively affects user satisfaction

H4 Service quality positively affects intention

In IS Success Model proposed byDeLone and McLean (2003), technical system quality refers to technical success and the accuracy and efficiency of the communication system that produces information; in fact, it constitutes the desirable char-acteristics 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; Motaghian et al., 2013; Saba, 2013; Tajuddin et al., 2013; Hassanzadeh et al., 2012; Kim et al., 2012; Islam, 2012; Wang

on both individuals’ satisfaction and their intentions

H5 Technical system quality positively affects user satisfaction

H6 Technical system quality positively affects intention

The success dimension content and information quality represents the desirable characteristics of an IS’s output (Petter

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 (Saba, 2013; Kim et al., 2012;

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

H7 Content and information quality positively affects user satisfaction

H8 Content and information quality positively affects intention

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.,Islam, 2013; Chow

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 useful-ness 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

H10 Perceived ease of use positively affects 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 et al., 2004) In fact, an individual’s willingness

to use a specific IS for their activities depends on their perception of its use (Hanafizadeh et al., 2014a) Perceived usefulness has been found to have a significant positive effect on usage intention towards use of e-learning services (Islam, 2013; Chen

consequence, the greater the perceived usefulness of e-learning system, the more positive is the intention; thus greater the likelihood that it will be used

H11 Perceived usefulness positively affects intention

3.2 E-learning outcomes

Rather than to sell, to supply, or to serve, the main objective of every business is to satisfy the needs and meet the satisfac-tion of its users (Dominici 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 and

sup-port services (Petter et al., 2008) The updated IS success model assumes that system use precedes user satisfaction which leads to an increased satisfaction which sequentially results in a higher intention to use (Petter et al., 2008) Satisfaction has been found to have a significant positive effect on intention towards use of e-learning services in some studies (Chang, 2013;

have a significant positive effect on actual use as well.Hassanzadeh et al (2012)in their study uncovered the positive effect

of satisfaction on actual use of e-learning system Therefore, in the context of this study, satisfaction assumed to have a posi-tive impact on both intention to use and actual use On the other hand, considering that academic outcomes refer to the mas-tering and perceived masmas-tering of the materials,Hsieh and Cho (2011)figured out, there exists to be a strong relationship between satisfaction and e-learning outcomes.Johnson et al (2008)asserted that the e-learning system provides learning assistance to the students in their courses, and thus students remain satisfied and achieve better course performance Satisfaction can be considered as a significant measure of learning effectiveness and learners’ self-efficacy Individuals who are satisfied with the system are more likely to make an effort to be effective, thus assists achieving better learning out-comes (Xu et al., 2014) Therefore, we assume that satisfaction positively associated with learners’ perceived learning assistance

H12 Satisfaction positively affects intention

H13 Satisfaction positively affects actual use

H14 Satisfaction positively affects perceived learning assistance

Intention, which is the main dependent variable identified in the studies conducted based on the TAM, is defined as the likelihood 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 and McLean, 2003) In the acceptance domain, some research-ers have studied the relationship between intention and actual use in e-learning context (e.g., Chow et al., 2012;

did not distinct between intention to use and system use in their updated model, but intention to use is generally an individ-ual level construct.Venkatesh et al (2003)confirms the positive relationship between intention to use and actual use Thus,

in the context of this study, intention assumed to have a positive impact on actual use

H15 Intention positively affects actual use

Past studies indicated that use of e-learning systems may help individuals in their learning activities from various aspects: faster interaction with those of shared interests (Zheng et al., 2013; Liu et al., 2010b), individualized (based on individual learning styles, approaches, abilities), self-regulated (learners determine their own learning content, possibility

of follow ones’ own plans, quick access to overall goal of learning, fulfilling learning in either formal or informal settings through collaboration (Huang et al., 2012), self-organized, voluntary, less formal, and open participatory learning opportuni-ties it offers (Fang and Chiu, 2010; Lu and Yang, 2011), and faster information sharing and exchange even with no previous social ties (Kim et al., 2011) Some studied have also uncovered that e-learning systems enable individuals with such a learn-ing in which they create and receive knowledge through discussions and interactive sharlearn-ing, offerlearn-ing resolution and novel insight (Hung and Cheng, 2013), and this facilitates problem solving and critical thinking skills through enhanced engage-ment (Liaw et al., 2007) Therefore, individuals are supposed to learn better when they explore things by themselves

effectiveness among individuals by providing them self-directed learning opportunities (Fang and Chiu, 2010; Lu and Yang,

2011) AsXu et al (2014)indicated, the learning outcomes should be measured and assessed through learning performance, and learners’ performance and achievements can be measured by their effectiveness AsXu et al (2014)note, e-learning sys-tems provide learners with self-evaluation which allows them to assess their learning performance and distinct their learn-ing weaknesses; therefore, learners uslearn-ing online learnlearn-ing platforms typically show higher perceived learnlearn-ing performance than those who do not Troussas et al (2013)who put collaboration among learners into consideration in a computer assisted learning environment, found that effective collaborative learning results when students appropriately perceive 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 Furthermore, AsIslam (2013)noted, some studies concluded that individuals inter-act more effectively when a social structure enables them to access a large number of continter-acts and interinter-actions and makes the information sharing faster (Cho et al., 2007; Ortiz et al., 2004) As he asserted, in his study on investigating e-learning system usage, use of e-learning positively affects perceived learning assistance It, therefore, is assumed that use of e-learn-ing systems, which assists individuals in learne-learn-ing more influentially through enhanced engagement, positively influences their perceived learning assistance.Table 2lists the dimensions’ and definitions;Fig 1shows the conceptual model H16 The use of e-learning system positively affects individuals’ perceived learning assistance

4 Instrument development

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

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 3, 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 invalid 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 learning outcomes such as actual use

of e-learning and perceived learning assistance in Tehran early 2014 This period was marked by recent developments in Iran 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 learners’ perspectives about the effects of e-learning usage determinants and its role on e-learning outcomes The sample is taken from the students of four public uni-versities of Tehran including Elm-o-Sanat, Amir Kabir, Shahid Beheshti, and Tehran uniuni-versities All these uniuni-versities make use of e-learning systems and software, known as ETS standing for ‘‘Electronic Training System’’, which are mostly the same

in features and components rendered to learners and instructors Universities’ e-learning systems were checked before being selected for the sample research of this study based on which four pre-mentioned universities were finally singled out The final questionnaire was arrived at after examining theoretical literature and studies undertaken by previous researchers based on which indices were selected (Table 3)

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 and

qvalue 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 questionnaire was released to them A total of 420 students were selected Next, participants were

Table 2

Definitions of dimensions.

Educational quality A conductive learning environment in terms of collaborative learning offered by e-learning

system

Kim et al (2012) and Hassanzadeh et al (2012) Service quality The quality of the support that users receive from e-learning system Petter et al (2008)

Technical system

quality

The desirable characteristics and features of e-learning system and components Petter et al (2008) Information quality The desirable characteristics and features of the output and the quality of learning content

of the e-learning system

Petter et al (2008) Perceived ease

usefulness

The degree to which a person believes that using the e-learning system would enhance his

or her job performance

Davis (1989) Perceived ease of

use

The degree to which a person believes that using the e-learning system would be free of effort.

Davis (1989) Satisfaction The extent to which users believe that their needs, goals, and desires have been fully met

through using the e-learning system

Sanchez-Franco (2009) Intention to use Key likelihood that an individual will use the e-learning system Schierz et al (2010)

Perceived learning

assistance

User perception of the extent to which he/she believes that e-learning system will actually assists their learning and ability to learn

Islam (2012)

H2

H12

H1

H3 H4 H5 H6 H7

H8

H9

H11

H13

H15

H10

H16 H14

Educational Quality

Service quality

Technical system quality Information quality

Intention

Satisfaction

Actual use

Perceived usefulness

Perceived ease

of use

Learning Assistance

Fig 1 Research model.

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 in nature and the goal was to conduct it from an extensive perspective; it was thus, exploratory and descriptive in approach Alpha Cronbach for the questionnaire emerged to be 0.839

a 2

2 p  q

e2 ðN  1Þ þ Za 2

Formula 1 Cochran formula for finite population

Table 3

The research instrument.

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 Lee (2010) 0.66 E-learning provides possibility of learning evaluation Hassanzadeh et al (2012) 0.32 E-learning is appropriate with my learning style Vernadakis et al (2011) 0.63 Service quality E-learning provides a proper online assistance and explanation Wang and Wang (2009) 0.78

E-learning department staff responds in a cooperative manner Au et al (2008) 0.81 E-learning provides me with the opportunity of reflecting views Andrade and Bunker (2009) 0.73 E-learning provides me with courses management Au et al (2008) 0.66 Technical system quality E-learning is aesthetically satisfying Ho and Dzeng (2010) 0.54

E-learning optimizes response time DeLone and McLean, 2003 0.76 E-learning is user friendly Ozkan and Koseler (2009) 0.63 E-learning provides interactive features between users and system Ozkan and Koseler (2009) 0.62 E-learning possesses structured design Ho and Dzeng (2010) 0.74

E-learning has attractive features Wang et al (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 Ozkan and Koseler (2009) 0.60 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 ease of use E-learning is easy to use Wang and Liao (2008) 0.64

E-learning is easy to learn DeLone and McLean, 2003 0.69 E-learning is easy to access DeLone and McLean, 2003 0.34 E-learning is easy to understand DeLone and McLean, 2003 0.49

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

E-learning helps me to be self-reliable Chiu and Wang (2008) 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

I am pleased enough with e-learning system Lee (2010) 0.64 E-learning satisfies my educational needs Lee et al (2009) 0.63

I am satisfied with performance of system Wu et al (2010) 0.37

E-learning give me self-confidence DeLone and McLean, 2003 0.59

I believe that use of e-learning is available Lin (2007) 0.31

I am likely to use e-learning system in the near future Lin (2011) 0.66 Actual use I use e-learning on daily basis DeLone and McLean, 2003 0.57

I use e-learning frequently DeLone and McLean, 2003 0.56

Learning assistance E-learning provides flexibility of learning with regard to time and place Islam (2012) 0.82

E-learning assists learning performance Islam (2012) 0.86

E-learning assists learning motivation Islam (2012) 0.81

5 Data analyses

5.1 Response rate and representatives

col-lected 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 representative of the Iranian e-learning population In addition, MA students (71.8%) dominated other groups.Table 5presents the demographic characteristics of the sample

5.2 Exploratory and confirmatory analysis

To perform an exploratory analysis, convergent and discriminant validities and scale reliability are considered (Fraering

dis-criminant validity measures whether two factors are statistically different from each other (Anderson and Gerbing, 1988) Following the two-step approach proposed byAnderson 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

alpha values, and standardized factor loadings As seen inTable 6, almost all factor loadings are larger than 0.4, while t-val-ues (shown inFig 2) 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 random error and therefore render consistent results) exceed 0.7, and all communalities exceed 0.7 showing minimally accepted construct reliability (Gefen et al., 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 proven 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

Table 4

Sample selection.

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

Table 5

The demographic characteristics of the sample.

Gender

Age

Education