Factors affecting the e learning outcome

Telematics and Informatics 32 (2015) 701–719 Contents lists available at ScienceDirect Telematics and Informatics journal homepage: www.elsevier.com/locate/tele Factors affecting the e-learning outcomes: An integration of TAM and IS success model Hossein Mohammadi ⇑ 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 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-learning E-learning outcomes such as actual use and perceived learning assistance were positively predicted by satisfaction and intention 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 2015 Elsevier Ltd All rights reserved Introduction To meet educational purposes and students’ demands, e-learning development emerges to be a catalyst for today educational institutions (Dominici and Palumbo, 2013; Alsabawy et al., 2013) E-learning can be defined as a dynamic and immediate 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 Gronlung, 2013; Dominici and Palumbo, 2013; Jeong and Hong, 2013; Bidin and Ziden, 2013) E-learning has a positive impact on both teachers and students in that it positively affects the duration of their attention, learning and training tenacity, 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 ⇑ Address: Pars Pamchal Alley, Block 17, No 2, Naghshe Iran St Ansar Alhossein St Second Square, Kosar, Qazvin, Iran Tel.: +98 9192864512, +98 9370845268 E-mail address: Hossein662@gmail.com http://dx.doi.org/10.1016/j.tele.2015.03.002 0736-5853/Ó 2015 Elsevier Ltd All rights reserved 702 H Mohammadi / Telematics and Informatics 32 (2015) 701–719 (2013) and Abachi and Muhammad (2013) note that all individuals involved in e-learning are fond of using it towards learning because of flexible access in terms of time, space, and pace and online collaborative learning 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, as Hassanzadeh et al (2012) quoted, many Iranian applicants 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 As Li 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 Hassanzadeh et al (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 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 Hassanzadeh et al (2012), tries to step forward to investigate the students’ perceptions of e-learning services on the association 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 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 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 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 by DeLone 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 beliefattitude-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 (Mohammadi, 2015) Perceived usefulness is also considered to be affected directly by perceived ease of use (Mohammadi, 2014) Table presents the most related and outstanding studies in the area of e-learning usage H Mohammadi / Telematics and Informatics 32 (2015) 701–719 703 Table 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 Saba (2013) System quality, information quality, and computer self-efficacy System use, user satisfaction, and self-managed learning behaviors Kim et al (2012) System quality, information quality, and instructional quality User satisfaction Li et al (2012) Service quality, course quality, perceived usefulness, perceived ease of use, and selfefficacy, system functionality and system response, system interactivity Intention, e-learning usage Chang (2013) Web quality, user value and user satisfaction Intention, e-learning usage Wang and Chiu (2011) Communication quality, information quality, and service quality User satisfaction, loyalty intention, and e-learning usage Tajuddin et al (2013) Tseng et al (2011) System quality Learning satisfaction Information, service, system, and instructor quality come to be as the key drivers of employees’ perceptions with regard to elearning acceptance System quality, information quality, and computer self-efficacy all affected system use, user satisfaction, and self-managed learning behaviors of student System quality, information quality, and instructional quality positively influence user satisfaction 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 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 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 elearning system for interacting experience, collaborating with others, and getting feedback Revealed a positive relationship between learning satisfaction and system quality Quality of the e-learning system and learner attractiveness E-learning effectiveness Islam (2012) Perceived usefulness, confirmation of initial expectation, and system quality Satisfaction, continuance intention, e-learning usage Udo et al (2011) Quality components such as assurance, empathy, responsiveness, reliability, and website content Satisfactions, intention Alsabawy et al (2013) Perceived usefulness, IT infrastructure services User satisfaction, customer value, and organizational value Chang (2013) Perceived usefulness, perceived value, perceived support, Intention Islam (2013) Perceived usefulness and perceived ease of use Perceived learning assistant, perceived community building assistant, and perceived academic performance 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 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 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 IT infrastructure services is vital in e-learning service success through positively influencing perceived usefulness, user satisfaction, customer value, and organizational value 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 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 (continued on next page) 704 H Mohammadi / Telematics and Informatics 32 (2015) 701–719 Table (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 Wang (2014) Personalized dynamic assessment developed by system Learning effectiveness Lee et al (2012) E-portfolio Educational qualities Islam (2014) Environmental, job-specific factors, environmental factors Satisfaction, dissatisfaction Chen and Tseng (2012) Perceived usefulness, motivation to use, selfefficacy, computer anxiety Intention Moreno et al (2013) Perceived usefulness and effort expectancy, disconfirmation Satisfaction Xu et al (2014) Personalized e-learning Online learning effectiveness such as examination, satisfaction, and self-efficacy Gupna et al (2013) Quality of e-learning system Quality of teaching Both system factors involving functionality and instructor factors involving self-efficacy contributes to greater e-learning effectiveness, alongside the fact that learner’s computer selfefficacy moderates the relationship between system functionality and training effectiveness Personalized dynamic assessment automatically developed by system for each learner strengthened students learning effectiveness and facilitated their learning achievements and disappeared misconceptions E-portfolio results in the improvement of educational qualities since teaching and learning focus is transferred from supervisorcentered to student-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 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 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 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 Personalized e-learning facilities and improves online learning effectiveness in terms of examination, satisfaction, and self-efficacy criteria E-learning system radically changes the concept of education whether it is full time, part time, or a distant education program, quality of elearning system influence the quality of teaching in educational sector 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 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 population over time; the Decomposed Theory of Planned Behavior (DTPB), an extended version of TAM, which models perceived ease of use and perceived 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 determinants 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 education (Hsu et al., 2013; Ehlers and Hilera, 2012) E-learning can be defined as making use of technology as a mediating tool for H Mohammadi / Telematics and Informatics 32 (2015) 701–719 705 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 comprises the computer-aided learning, e-learning, remote learning, and on-line learning (Ho and Dzeng, 2010) The former three ones are the learning ways conducted through electronic 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 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) As Islam (2013) further added that, based on what Johnson et al (2008) suggest, an online learning system can be useful 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 appropriate 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 by Hassanzadeh 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) As Hassanzadeh 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 satisfaction Educational 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 training (Petter and McLean, 2009) and helpdesk The inclusion of this success dimension is not undoubted, since it normally seen 706 H Mohammadi / Telematics and Informatics 32 (2015) 701–719 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 significant positive effect on satisfaction in e-learning context (Xu et al., 2014; Poulova and Simonova, 2014; Tajuddin et al., 2013; Wang and Chiu, 2011; Roca et al., 2006), and on intention to use e-learning system in some studies (Hassanzadeh et al., 2012; Cheng, 2012; Li et al., 2012; Wang and Chiu, 2011; Ramayah et al., 2010) In this study, service quality is assumed to have a 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 by DeLone 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 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; Motaghian et al., 2013; Saba, 2013; Tajuddin et al., 2013; Hassanzadeh et al., 2012; Kim et al., 2012; Islam, 2012; Wang and Chiu, 2011; Rai et al., 2009; Wu et al., 2008), and on intention to use e-learning system (Islam, 2012; Cheng, 2012; Li et al., 2012; Wang and Chiu, 2011; Ramayah et al., 2010) Technical system quality, thus, is assumed to have a positive effect 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 and 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 (Saba, 2013; Kim et al., 2012; Hassanzadeh et al., 2012; Wang and Chiu, 2011, Roca et al., 2006), and for intention to use e-learning system (Cheng, 2012; Wang and Chiu, 2011; Ramayah et al., 2010) In this study, therefore, content and information quality is assumed 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 et al., 2012; Chen and Tseng, 2012; Li et al., 2012; Sumak et al., 2011; Liu et al., 2010a) As a result, the greater the perceived 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 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 and Tseng, 2012; Chow et al., 2012; Li et al., 2012; Cheng et al., 2012; Islam, 2012; Sumak et al., 2011; Liu et al., 2010a) As a 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 satisfaction 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 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 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; Islam, 2012; Hassanzadeh et al., 2012; Udo et al., 2011; Petter et al., 2008; Roca et al., 2006) Satisfaction has been found to H Mohammadi / Telematics and Informatics 32 (2015) 701–719 707 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 positive impact on both intention to use and actual use On the other hand, considering that academic outcomes refer to the mastering and perceived mastering 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 outcomes (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 researchers have studied the relationship between intention and actual use in e-learning context (e.g., Chow et al., 2012; Hassanzadeh et al., 2012; Alkhalaf 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 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 opportunities 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 learning in which they create and receive knowledge through discussions and interactive sharing, offering resolution and novel insight (Hung and Cheng, 2013), and this facilitates problem solving and critical thinking skills through enhanced engagement (Liaw et al., 2007) Therefore, individuals are supposed to learn better when they explore things by themselves (Hung and Cheng, 2013), and this implies that the use of e-learning systems may contribute to the improvement of learning effectiveness among individuals by providing them self-directed learning opportunities (Fang and Chiu, 2010; Lu and Yang, 2011) As Xu 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 As Xu et al (2014) note, e-learning systems provide learners with self-evaluation which allows them to assess their learning performance and distinct their learning weaknesses; therefore, learners using online learning platforms typically show higher perceived learning performance than those who 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, As Islam (2013) noted, some studies concluded that individuals interact more effectively when a social structure enables them to access a large number of contacts and interactions 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-learning systems, which assists individuals in learning more influentially through enhanced engagement, positively influences their perceived learning assistance Table lists the dimensions’ and definitions; Fig shows the conceptual model H16 The use of e-learning system positively affects individuals’ perceived learning assistance 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 from Kim and Mirusmonov (2010), intention to use from Lin (2011), system, service, and information quality, and satisfaction from DeLone and McLean (2003), and educational quality along with actual use from Hassanzadeh 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 in Table 3, in the FCFA of sample group (20% of total), 708 H Mohammadi / Telematics and Informatics 32 (2015) 701–719 Table Definitions of dimensions Construct Definition Source Educational quality A conductive learning environment in terms of collaborative learning offered by e-learning system The quality of the support that users receive from e-learning system The desirable characteristics and features of e-learning system and components Kim et al (2012) and Hassanzadeh et al (2012) Petter et al (2008) Petter et al (2008) The desirable characteristics and features of the output and the quality of learning content of the e-learning system The degree to which a person believes that using the e-learning system would enhance his or her job performance The degree to which a person believes that using the e-learning system would be free of effort The extent to which users believe that their needs, goals, and desires have been fully met through using the e-learning system Key likelihood that an individual will use the e-learning system User perception of the extent to which he/she believes that e-learning system will actually assists their learning and ability to learn Petter et al (2008) Service quality Technical system quality Information quality Perceived ease usefulness Perceived ease of use Satisfaction Intention to use Perceived learning assistance Davis (1989) Davis (1989) Sanchez-Franco (2009) Schierz et al (2010) Islam (2012) Educational Quality Service quality Technical system quality Information quality Perceived ease of use H2 H1 H4 H3 Satisfaction H14 H5 H6 H12 H13 H16 H7 H8 Learning Assistance Actual use Intention H15 H9 H10 Perceived usefulness H11 Fig Research model 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 universities of Tehran including Elm-o-Sanat, Amir Kabir, Shahid Beheshti, and Tehran universities All these universities 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 Z a2 value of about 1.96, e value less than 0.1 of about 0.099 and q value 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 709 H Mohammadi / Telematics and Informatics 32 (2015) 701–719 Table The research instrument Construct Question Source Factor loading Educational quality E-learning E-learning E-learning E-learning E-learning E-learning assures the presents of students provides collaborative learning provides required facilities such as chat and forum provides possibility of communicating with other students provides possibility of learning evaluation is appropriate with my learning style Chang and Chen (2009) Hassanzadeh et al (2012) Lee (2010) Lee (2010) Hassanzadeh et al (2012) Vernadakis et al (2011) 0.77 0.71 0.69 0.66 0.32 0.63 Service quality E-learning E-learning E-learning E-learning provides a proper online assistance and explanation department staff responds in a cooperative manner provides me with the opportunity of reflecting views provides me with courses management Wang and Wang (2009) Au et al (2008) Andrade and Bunker (2009) Au et al (2008) 0.78 0.81 0.73 0.66 Technical system quality E-learning E-learning E-learning E-learning E-learning E-learning E-learning E-learning E-learning is aesthetically satisfying optimizes response time is user friendly provides interactive features between users and system possesses structured design has flexible features has attractive features is reliable is secure Ho and Dzeng (2010) DeLone and McLean, 2003 Ozkan and Koseler (2009) Ozkan and Koseler (2009) Ho and Dzeng (2010) Au et al (2008) Wang et al (2007) Ozkan and Koseler (2009) Ozkan and Koseler (2009) 0.54 0.76 0.63 0.62 0.74 0.66 0.83 0.71 0.67 Information quality E-learning E-learning E-learning E-learning E-learning E-learning provides provides provides provides provides provides Au et al (2008) Ho and Dzeng (2010) Wang and Wang (2009) Ozkan and Koseler (2009) Wang and Liao (2008) Wang et al (2007) 0.82 0.64 0.72 0.60 0.65 0.57 Perceived ease of use E-learning E-learning E-learning E-learning E-learning is is is is is Wang and Liao (2008) DeLone and McLean, 2003 DeLone and McLean, 2003 DeLone and McLean, 2003 DeLone and McLean, 2003 0.64 0.69 0.34 0.49 0.74 Perceived usefulness E-learning E-learning E-learning E-learning E-learning E-learning E-learning helps to save time helps to save cost helps me to be self-reliable helps to improve my knowledge helps to improve my performance is effective is efficient DeLone and McLean, 2003 Ho and Dzeng (2010) Chiu and Wang (2008) Hassanzadeh et al (2012) Hassanzadeh et al (2012) DeLone and McLean, 2003 DeLone and McLean, 2003 0.64 0.48 0.64 0.69 0.73 0.76 0.64 Satisfaction E-learning is enjoyable I am pleased enough with e-learning system E-learning satisfies my educational needs I am satisfied with performance of system E-learning is pleasant to me E-learning give me self-confidence DeLone and McLean, 2003 Lee (2010) Lee et al (2009) Wu et al (2010) Lee (2010) DeLone and McLean, 2003 0.75 0.64 0.63 0.37 0.57 0.59 Intention to use I tend to use e-learning system I believe that use of e-learning is available I am likely to use e-learning system in the near future Lin (2007) Lin (2007) Lin (2011) 0.54 0.31 0.66 Actual use I use e-learning on daily basis I use e-learning frequently I visit e-learning often DeLone and McLean, 2003 DeLone and McLean, 2003 DeLone and McLean, 2003 0.57 0.56 0.49 Learning assistance E-learning E-learning E-learning E-learning Islam Islam Islam Islam 0.82 0.86 0.87 0.81 information that is relevant to my needs comprehensive information information that is exactly what I want me with organized content and information up to date content and information required content and information easy to use easy to learn easy to access easy to understand convenient provides flexibility of learning with regard to time and place assists learning performance assists learning efficiency assists learning motivation (2012) (2012) (2012) (2012) 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 N¼ N Á Z a2 Á p Á q e2 N 1ị ỵ Z a2 p q Formula Cochran formula for finite population ð1Þ 710 H Mohammadi / Telematics and Informatics 32 (2015) 701–719 Data analyses 5.1 Response rate and representatives Table summarizes the response rate Three hundred and 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 representative of the Iranian e-learning population In addition, MA students (71.8%) dominated other groups Table presents 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 and Minor, 2005) Convergent validity measures whether items can 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 lists Average Variance Extracted (AVE), Composite Reliability (CR), R square (R2), Communality, and Cronbach alpha values, and standardized factor loadings As seen in Table 6, almost all factor loadings are larger than 0.4, while t-values (shown in Fig 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 R2 of 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 R2 of about 0.22 is partially forecasted by its predictor, and the Table Sample selection University Students (N) in each university n for each university Frequency of sample/population Percentage (%) Elm-o-Sanat Shahid Beheshti Amir Kabir Tehran Total 500 550 580 500 2130 81 81 81 81 324 97/105 98/105 97/105 98/105 390/420 24.6 25.6 24.6 25.1 100 Table The demographic characteristics of the sample Frequency Percentage Gender Female Male Total 192 198 390 49.2 51.8 100.0 Age 2020–30 Total 48 342 390 12.3 87.7 100.0 Education BA MA Total 110 280 390 28.2 71.8 100.0 711 H Mohammadi / Telematics and Informatics 32 (2015) 701–719 Table Main statistics Factor Item Standardized loading AVE CR R square Communality Alpha Actual use Act1 Act2 Act3 Eas1 Eas2 Eas3 Eas4 Lea1 Lea2 Lea3 Lea4 Edu1 Edu2 Edu3 Edu4 Edu5 Inf1 Inf2 Inf3 Inf4 Inf5 Inf6 Int1 Int2 Sat1 Sat2 Sat3 Sat4 Sat5 Ser1 Ser2 Ser3 Ser4 Sys1 Sys2 Sys3 Sys4 Sys5 Sys6 Sys7 Sys8 Sys9 Per1 Per2 Per3 Per4 Per5 Per6 Per7 0.72 0.72 0.67 0.86 0.82 0.88 0.86 0.82 0.86 0.89 0.81 0.79 0.78 0.71 0.74 0.73 0.83 0.72 0.81 0.76 0.80 0.86 0.78 0.78 0.86 0.82 0.84 0.76 0.79 0.87 0.86 0.82 0.87 0.80 0.81 0.82 0.73 0.85 0.74 0.80 0.84 0.77 0.74 0.85 0.82 0.89 0.83 0.89 0.88 0.8354 0.8775 0.7373 0.8354 0.8655 0.7813 0.8674 0.7813 0.8551 0.7240 0.9120 0.7240 0.8919 0.7623 0.8691 0.7623 0.8605 0.7669 0.8750 0.7669 0.8692 0.8605 0.8799 0.6312 0.8605 0.8589 0.7578 0.8779 0.2228 0.7578 0.8586 0.7705 0.8641 0.7705 0.8501 0.7735 0.8842 0.7735 0.8819 0.7616 0.8739 0.7616 0.8679 Ease of use Learning assistance Educational quality Information quality Intention to use Satisfaction Service quality System quality Perceived usefulness 0.3477 0.0230 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 R2 of about 0.73 is proven to be well predicted by its predictors which are users’ intentions and satisfaction Furthermore, perceived learning assistance – with an R2 of about 0.34 is proven to be influentially predicted by its predictors which are actual use and satisfaction Moreover, the indices used for ‘‘satisfaction’’, ‘‘intention’’, ‘‘actual use’’, ‘‘perceived learning assistance’’ 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 in Table 7, 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 estimate the structural model 5.3 Path coefficient As listed in Table 8, among the factors influencing satisfaction, information quality (c = 0.29, p < 0.01) and technical system quality (c = 0.29, p < 0.01) showed the greatest effects, educational quality (c = 0.26, p < 0.01) and service quality 712 H Mohammadi / Telematics and Informatics 32 (2015) 701–719 Educational quality 26 ** 05 Service quality *** 29 ** 23 *** Technical system quality 21 ** Satisfaction 52 *** 34 *** 18 *** 29 ** Information quality 14 ** Perceived ease of use Intention 83 *** Learning assistance 43 *** Actual use 06 16 ** 52 *** Perceived usefulness Chi-Squere=2540, df=890, P-value=0.00000, RMSEA=0.051 Fig Standard coefficients and significance values Table The square root of AVE (italic at diagonal) and correlation coefficients ACT EAS ASS EDU INF INT SAT SER SYS USE ACT EAS ASS EDU INF INT SAT SER SYS USE 0.9140 0.2809 0.8456 À0.0875 À0.5938 0.8859 0.4216 À0.5845 À0.4418 0.1027 0.8839 0.2131 À0.6039 À0.2927 0.1829 À0.0253 À0.1296 0.0965 À0.1518 0.8508 À0.0765 À0.4136 0.6759 0.5215 À0.2458 À0.5492 0.2344 0.8730 0.2311 0.0575 0.1569 0.1054 À0.3416 0.1302 0.8757 À0.5221 À0.2664 0.6000 0.4084 À0.1302 0.9276 0.2237 À0.5277 À0.6015 À0.1291 0.8705 À0.2208 À0.0090 0.7979 0.8777 0.5352 À0.1328 0.8794 0.1283 0.8726 Table Path coefficients and significances Question Path H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 H12 H13 H14 H15 H16 Educational Educational Service Service System System Information Information Ease of use Ease of use Usefulness Satisfaction Satisfaction Satisfaction Intention Actual use Significance codes: ⁄⁄⁄ 0.001, ⁄⁄ Satisfaction Intention Satisfaction Intention Satisfaction Intention Satisfaction Intention Intention Usefulness Intention Intention Actual use Learn assistance Actual use Learn assistance Path coefficient Supported or not ⁄⁄ Yes No Yes Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes 0.26 0.05 ⁄⁄ 0.21 ⁄⁄⁄ 0.2 ⁄⁄ 0.29 ⁄⁄⁄ 0.23 ⁄⁄ 0.29 ⁄⁄ 0.14 0.06 ⁄⁄ 0.16 ⁄⁄⁄ 0.52 ⁄⁄⁄ 0.52 ⁄⁄⁄ 0.18 ⁄⁄⁄ 0.34 ⁄⁄⁄ 0.83 ⁄⁄⁄ 0.43 0.01, ⁄0.05 (c = 0.21, p < 0.01) had significant paths as well Among the factors influencing intention, technical system quality (c = 0.23, p < 0.001), and service quality (c = 0.2, p < 0.001), information quality (c = 0.14, p < 0.01) had respectively significant positive paths However, educational quality (c = 0.05) showed no significant effect in this regard Perceived usefulness (b = 0.52, p < 0.001) had significant positive path towards intention, while perceived ease of use (c = 0.06) showed no significant effect on the intention Furthermore, perceived ease of use (c = 0.16, p < 0.01) had a significant effect on perceived usefulness Satisfaction (b = 0.52, p < 0.001) also appeared to have a significant positive path towards intention It (b = 0.34, p < 0.001) 713 H Mohammadi / Telematics and Informatics 32 (2015) 701–719 Table The values of fit indices Fit indices x2 =df (GFI) (IFI) (CFI) (NNFI) (NFI) (RMSEA) Actual Recommended 2.85 0.80 0.92 >0.90 0.92 >0.90 0.90 >0.90 0.92 >0.90 0.05