Mustafa Degerli - 2010 - What is available about technology acceptance of e-learning software and systems - A review and comprehension paper

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What is available about technology acceptance of e-learning software and systems? A review and comprehension paper
Mustafa DEĞERLİ * md.mustafadegerli@gmail.com
June 11, 2010
* Graduate Student in Department of Information Systems, Informatics Institute, METU
The speedy advances in information and communication technologies (ICT) have led to their amplified exploitation in teaching and learning contexts (Cappel and Hayen, 2004). Additionally, International Data Corporation (IDC) estimates that the value of the e-learning market worth will be between $21 billion and $28 billion by 2008 (Brown, 2006). In this context, Mackay and Stockport (2006) mention that according to IDC, the revenue from synchronous e-learning exceeded $5 billion by 2006. Stemmed from these facts, applying technology by means of e-learning software and systems (e-LSS) to facilitate and support learning is an imperative and interested in application area recently.
Nevertheless, another imperative concern intended for this context is surely the technology acceptance (TA) of these e-LSS by people, especially by students and teachers. Even though there are studies conducted in this subject with respect to various contexts, there is lacking a paper that reviews and summarizes previous studies and by this way provides a comprehensive guide to let people know about the TA of e-LSS. This paper aims to compensate this lack for the interested readers wanting to know about not only the TA concepts, but also about the preceding TA of e-LSS studies.

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In this study, searches were conducted using the online databases ABI/INFORM Complete, Academic Search Complete, Cambridge Journals Online, Computers & Applied Sciences Complete, EBSCOhost Databases, Education Research Complete, Emerald Management Xtra, ERIC, IEL-IEEE/IEE Electronic Library, Library, Information Science & Technology Abstracts with Full Text, and World Higher Education Database; with the keywords „„Technology Acceptance Model”, „„TAM”, „„TAM2”, „„UTAUT,”, „„Universal Theory of Acceptance and Use of Technology”, “TPB”, “Theory of Planned Behavior”, “IDT”, “Innovation Diffusion Theory”, “e- learning”, “adoption”, “acceptance”, “educational software”, “e-teaching”, “online learning”, “online teaching”, and “educational computer systems”.
In this context, apparent non-e-learning and non-technology results were detached first, and after this, the abstracts of all left behind results were read. Thus, full versions of all articles that were possibly relevant were retrieved and read. For each retrieved article, a search of references that might meet inclusion criteria was conducted, and any of these relevant articles retrieved and the same procedure of analyzing was applied to these articles. As a consequence of this process, sixteen studies published in or after 2000 are included in this review.
Before reviewing and summarizing preceding studies and providing a comprehensive guide on the subject of TA of e-LSS, it is compulsory to have a look at concept of TA and underlying principles and models related with TA. As indicated by Dillon and Morris (1996), TA is the user acceptance that is defined as the demonstrable willingness of the users to employ information technology (IT) for the tasks that it is intended to support. They argue that demonstrable willingness of the users to use related

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IT must be reached for TA. Moreover, Dillon and Morris also note that every TA process of IT for intended purposes can be modeled and predicted. In fact, this is a promising statement, as they argue that thanks to TA theory it is possible to model and predict any intended ITs' TA. Additionally, in this context, Davis (1993) suggests that TA is the key factor that determines whether an information system (IS) or IT project is to be successful or not. Surely, IT or IS projects will be useless and meaningless unless they are accepted by the intended users for intended purposes.
There are models and theories trying to explain and shape the TA process and its characteristics. For example, as said by Rogers (1995), innovation diffusion theory (IDT) says that there are five characteristics of a technology that determine an IT‟s or IS‟s TA. These are relative advantage, compatibility, complexity, trialability and observability. According to Rogers, as long as these five concerns are took seriously and managed well, related IT or IS is to be accepted by intended users for intended purposes.
Furthermore, Technology Acceptance Model (TAM) of Davis, et al. (1989), Theory of Planned Behavior (TPB) of Ajzen (1991), Technology Acceptance Model 2 (TAM2) of Venkatesh and Davis (2000), and Universal Theory of Acceptance and Use of Technology (UTAUT) of Venkatesh, et al. (2003) are the models in the literature mostly used to design, implement and test TA of IT or IS.
Of these models, the most usually cited one is the TAM of Davis, et al. Their work not only provides major contribution to TA literature, but this model is used as a reference by other studies. TAM of Davis, et al. predicts that TA of any IT is determined by two factors. These are perceived usefulness (PU) and perceived ease of use (PEOU).

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PU is defined as the extent to which users believe that using the system will enhance his or her performance regarding the intended purpose. Moreover, PEOU is defined as the extent to which the users believe that using the system will be free from effort. In accordance with TAM, both PU and PEOU have major impact on a users‟ attitude toward using the IT and determining its TA.
The illustrations of the models related with TA, TAM of Davis, et al. (1989), TPB of Ajzen (1991), TAM2 of Venkatesh and Davis (2000), and UTAUT of Venkatesh, et al. (2003), are provided below in Figures 1-4. In addition, definitions of the variables used in these figures are provided in Table 1 below.
As these TA models are crucial to understand the TA studies for TA of e-LSS, it is a good idea to examine the below figures and the table.
Figure 1: Illustration of TAM
Attitude
Behavioral Intention to Use (Acceptance)
Actual Use
Perceived Usefulness
Perceived Ease of Use

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Figure 2: Illustration of TPB
Figure 3: Illustration of TAM2
Attitude
Behavioral Intention
Behaviour
Subjective Norm
Perceived Behavioral Control
Behavioral Beliefs
Normative Beliefs
Control Beliefs
Subjective Norm
Actual Use
Perceived Usefulness
Perceived Ease of Use
Image
Job Relevance
Output Quality
Results Demonstrability

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Figure 4: Illustration of UTAUT
Variable Definition Behavior Use (BU)
The action, specific or general, whose prediction is of interest Behavioral Intention (BI)
One specific behavior of interest performed by individuals with regard to some IT system Attitude (ATT)
An individual‟s evaluative judgment of the target behavior on some dimension (e.g., good/bad, harmful/beneficial, pleasant/unpleasant) Perceived Ease of Use (PEOU)
An individual‟s perception that using an IT system will be free of effort
Performance Expectancy
Actual Use
Effort Expectancy
Social Influence
Facilitating Conditions

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Perceived Usefulness (PU)
An individual‟s perception that using an IT system will enhance job performance Subjective Norm (SN)
An individual‟s perception of the degree to which important other people approve or disapprove of the target Perceived Behavioral Control (PBC)
An individual‟s perception of how easy or difficult it will be to perform the target behavior (self-efficacy), of factors that impede or facilitate the behavior (facilitating conditions), or of the amount of control that one has over performing the behavior (controllability) Effort Expectancy
An individual‟s perception that using an IT system will be free of effort Performance Expectancy
An individual‟s perception that using an IT system will enhance job performance Social Influence
An individual‟s perception of the degree to which important other people approve or disapprove of the target Facilitating Conditions
An individual‟s perception of how easy or difficult it will be to perform the target behavior (self-efficacy), of factors that impede or facilitate the behavior (facilitating conditions), or of the amount of control that one has over performing the behavior (controllability)

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Image
The degree to which one perceives the use of the technology as a means of enhancing one's status within a social group Job Relevance
An individual's perception of the degree to which the technology is applicable to his or her job Output Quality
An individual's perception of how well a system performs tasks necessary to his or her job. Results Demonstrability
The tangibility of the results of using the technology Behavioral Beliefs
An individual‟s belief about consequences of particular behavior Normative Beliefs
An individual‟s perception about the particular behavior, which is influenced by the judgment of significant others Control Beliefs
An individual's beliefs about the presence of factors that may facilitate or impede performance of the behavior
Table 1: Definitions of the Variables Used in TA Models
The first study examined in the context of reviewing papers in TA of e-LSS is Martinez-Torres, et al.‟s “A technological acceptance of e-learning tools used in practical and laboratory teaching, according to the European higher education area” titled study. The objective of their study is to examine the effectiveness of TAM of web-

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based e-LSS used in practical and laboratory teaching. In the study, Martinez-Torres, et al. tried to empirically validate the research hypotheses derived from TAM, whose illustration is provided in Figure 1 above, using the responses to a survey on e-LSS usage among 220 users. The obtained results of their study strongly support the extended TAM in predicting users‟ intention to use e-LSS and define a set of external variables with a major influence in the original TAM variables. However, they found out that PEOU did not create a significant impact on users‟ attitude or intention towards e-LSS usage. Martinez-Torres, et al. integrated new factors related to human and social change processes to the initial TAM to adapt it for the study of e-LSS. These factors refer to providing students with a new channel to learn, such as providing interactivity and control, feedback, communicativeness); others refer to factors that can influence users‟ motivations to use the tool, such as enjoyment, user tools, diffusion, methodology, user adaptation. To sum up, Martinez-Torres, et al.‟s study concluded that TAM is there to use to provide TA of e-LSS with some additional extensions.
The second study examined in the context of reviewing papers in TA of e-LSS is Park‟s “An Analysis of the Technology Acceptance Model in Understanding University Students’ Behavioral Intention to Use e-Learning” titled study. A sample of 628 university students took part in the related research. In Park‟s study, the general structural model including e-learning self-efficacy, subjective norm, system accessibility, perceived usefulness, perceived ease of use, attitude, and behavioral intention to use e-LSS, is developed based on the TAM. The results of the study are proved TAM to be a good theoretical tool to understand users‟ acceptance of e-LSS.

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Additionally, Park noted that e-learning self-efficacy was the most important construct, followed by subjective norm in explicating the causal process in the model.
The third study examined in the context of reviewing papers in TA of e-LSS is Hsia and Tseng‟s “An enhanced technology acceptance model for e-learning systems in high-tech companies in Taiwan: analyzed by structural equation modeling” titled study. In their study, Hsia and Tseng‟s efforts aimed to integrate two constructs, perceived flexibility and computer self-efficacy, to examine the applicability of TAM in explaining employees‟ decisions to accept e-LSS. Their study is based on a sample of 233 employees from 16 high-tech companies at Hsinchu Science Park in Taiwan. The result of their study significantly supports the extended TAM in predicting employees‟ behavioral intention to use e-LSS. Additionally, results of this study showed that ee-LSS must be flexible in any time and place. That is perceived flexibility has the most significant direct and total effect on behavioral intention to use e-LSS. Moreover, Hsia and Tseng‟s study also showed that computer self-efficacy had a positive effect on perceived ease of use, perceived usefulness, and perceived flexibility in the context of TA of e-LSS.
The fourth study examined in the context of reviewing papers in TA of e-LSS is Liu, et al.‟s “Applying the technology acceptance model and flow theory to online e- learning users’ acceptance behavior” titled study. In their study, Liu, et al. tested constructs from IS, TAM, and Human Behavior and Psychology (Flow Theory) in an integrated theoretical framework of online e-learning users‟ acceptance behavior. Their study concludes that the most media-rich presentation interface (text-audio-video based presentations) generated higher levels of PU and concentration than text-audio and

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audio-video based presentations. Additionally, they note that PU and concentration influence user intentions. Consequently, the study concludes that the TA rate of text- audio-video based presentations is high thanks to not only its PU but also owing to that it generates the highest user concentration.
The fifth study examined in the context of reviewing papers in TA of e-LSS is Khan and Iyer‟s “ELAM: A Model for Acceptance and Use of E-learning by Teachers and Students” titled study. In their study, Khan and Iyer propose a conceptual framework for understanding TA of e-LSS. Their model, namely e-learning acceptance model (ELAM), is based on the UTAUT of Venkatesh, et al. (2003). ELAM identifies the key factors in TA of e-LSS as measured by behavioral intention to use the technology and actual usage. The four determinants of TA of e-LSS are performance expectancy, effort expectancy, social influence, and facilitating conditions. Specifically, the following factors are included in facilitating conditions variable in ELAM: reliable infrastructure, institutional policies, training and support. Additionally, Khan and Iyer note that since e-learning is associated with individualization of the teaching and learning process, the learning style of the student and teaching style of the teacher is an essential factor affecting the TA process for e-LSS.
The sixth study examined in the context of reviewing papers in TA of e-LSS is Maldonado, at al.‟s “E-learning motivation, Students’ Acceptance/Use of Educational Portal in Developing Countries” titled study. In their study, Maldonado, at al. tried to adopt and modify UTAUT model of Venkatesh, et al. by adding a new construct of e- learning motivation and they applied it to Peruvian context for prediction of the role of e-learning motivation in TA and use. Furthermore, they found that e-learning motivation

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plays a decisive role in the adoption and use of e-LSS and they demonstrated that e- learning motivation is different from conventional learning motivation by means of adding technology characteristics (like effort expectancy) to traditional motivational construct. What is more, Maldonado, at al. examined the cyclic effect of the technology use on e-learning motivation, and they found that e-educational portal use simulates students‟ e-learning motivation. They also confirmed the importance of influence of teachers, parent and other peers in TA of e-LSS in schools in Peru context and they used region and gender as moderating variables in their study.
The seventh study examined in the context of reviewing papers in TA of e-LSS is Yuen and Ma‟s “Exploring teacher acceptance of e-learning technology” titled study. In their study, Yuen and Ma attempted to explore a model to understand teachers‟ TA of e-LSS. In the related study, a self-reported questionnaire was used to examine teacher acceptance and attitude towards e-LSS. Data were collected from 152 in-service teachers who were studying in a part-time teacher education program in Hong Kong. Additionally, TAM was used as the core framework in favor of analysis while additional constructs were added in order to find a better model to understand teacher acceptance of e-learning technology. A composite model including five constructs, specifically, intention to use, perceived usefulness, perceived ease of use, subjective norm and computer self-efficacy, were formed and tested in the study. It was found that subjective norm and computer self-efficacy serve as the two significant perception commentators of the fundamental constructs in TAM. However, contrary to previous literature, PEOU became the sole determinant to the prediction of intention to use, while perceived usefulness was non-significant to the prediction of intention to use. In my opinion, the

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reason for this is that the target is not students but teachers for the study. This seems to indicate that the perceived ease of use amongst teachers is extremely important. As well, this is just because teachers are different from students with some major respects.
The eighth study examined in the context of reviewing papers in TA of e-LSS is Moghadam and Bairamzadeh‟s “Extending the Technology Acceptance Model for E- learning: A Case Study of Iran” titled study. In their study, Moghadam and Bairamzadeh attempted to extend the TAM to include subjective norm, personal innovativeness in domain of information technology and self-efficacy to evaluate TA of e-LSS. Responses from 155 university students were collected to evaluate the proposed structural model. The results indicated that personal innovativeness in domain of IT has a direct effect on self-efficacy. Both personal innovativeness in domain of IT and self- efficacy have unswerving effect on perceived ease of use. Perceived usefulness has a direct effect on intention of students‟ to accept an e-LSS. Additionally, the study suggested that e-LSS should include functions that add to efficiency and effectiveness of teaching and learning, and also to promote the belief of being easy to use. Furthermore, in their study, Moghadam and Bairamzadeh illustrated the role of personality traits in TA of e-LSS.
The ninth study examined in the context of reviewing papers in TA of e-LSS is Liu, at al.‟s “Impact of media richness and flow on e-learning technology acceptance” titled study. In their study, Liu, at al. tried to propose an integrated theoretical framework for the user‟s acceptance behaviour of web-based streaming media for e- LSS. In their related study, they tested concepts from TAM and human behaviour and psychology (flow theory) with reference to the TA of e-LSS. In addition to the TAM,

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flow theory was used to study the influence of user concentration on task activity. The related study concluded that the most media-rich presentation interface (text–audio– video presentation) always generates higher levels of PU and concentration than text– audio-based or audio–video-based presentations. This study further confirms that course materials that use rich media can promote higher user acceptance through stimulating a higher PU and concentration.
The tenth study examined in the context of reviewing papers in TA of e-LSS is Zayim‟s “Instructional technology adoption of medical school faculty in teaching and learning: faculty characteristics and differentiating factors in adopter categories” titled study. In her study, Zayim used a mix-method research design, a quantitative methodology (survey) in conjunction with qualitative methodology (in-depth interviews) for the purpose of gathering data about characteristics and adoption patterns of medical school faculty from 155 teaching personnel. The findings provided an evidence for similarities between adoption patterns of medical school faculty and other higher education faculty; relatively new tools associated with instruction were not adopted by majority of the faculty. In this study, additionally it is noted that some differences were found between early adopters and mainstream faculty in terms of individual characteristics, adoption patterns, perceived barriers and incentives to adoption and preferred methods of learning about technology and support.
The eleventh study examined in the context of reviewing papers in TA of e-LSS is Işık‟s “Perceptions of students and teachers about the use of e - learning / sharing portal in educational activities” titled study. In his study, Işık conducted a questionnaire with 200 students of 6th and 7th grade students. In the study, he investigated the

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perceptions in terms of three aspects: effects of the use of this technology on their perceived motivation, the perceived usefulness and the perceived ease of use of this technology. The findings of the study indicated that the students and the teachers perceived that e-learning / sharing portal technology is a useful and they easy to use technology for targeted people. In the study, it was found out that the students and the teachers are satisfied with advantages of the use of this new technology in their learning environment. In the same way, the teachers and the students stated that using the system effected students‟ perceived motivation towards the educational activities in a positive way.
The twelfth study examined in the context of reviewing papers in TA of e-LSS is Özdemir‟s “The effect of educational ideologies on technology acceptance” titled study. In his study, Özdemir tried to investigate the effect of both students‟ and academics‟ educational ideologies on TA, and to find out whether there are differences in the PEOU of technology, PU of technology, attitudes toward technology, and the frequency of use of technology in education in terms of their educational ideologies. In the study, a survey design was used. The questionnaire used in the study was developed by making use of the related literature, and it was administered to 58 academic personnel and 320 students. The results of the study demonstrated that academics‟ educational ideologies affect their acceptance of technology; specifically they affect the perceived usefulness of educational technology. Furthermore, there is an effect of students‟ educational ideologies on the frequency of their use of educational technologies. Educational ideology is a factor affecting academics‟ perceptions of the usefulness of technology, and it is a factor affecting the students‟ the frequency of use of educational technology.

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The thirteenth study examined in the context of reviewing papers in TA of e-LSS is Tseng and Hsia‟s “The impact of internal locus of control on perceived usefulness and perceived ease of use in e-learning: an extension of the technology acceptance model” titled study. In their study, Tseng and Hsia are aimed to broaden the TAM to include variables related to human factor. Therefore, their mainly effort was to integrate internal locus of control (ILOC) and computer self-efficacy (CSE), to examine the applicability of the TAM in explaining employees‟ decisions about TA of e-LSS. Based on a sample of 204 employees taken from 12 high-tech companies in Taiwan, the results strongly supported the extended TAM in predicting employees‟ behavioral intention to use e- learning. It is seen that PU has the most significant direct effect on behavioral intention to use e-LSS. TAM has been extended in an e-learning context. Specifically, CSE had a positive effect on PEOU and behavioral intention to use.
The fourteenth study examined in the context of reviewing papers in TA of e- LSS is Henderson and Steward‟s “The Influence of Computer and Internet Access on E- learning Technology Acceptance” titled study. In their study, Henderson and Steward tried to investigate whether computer and Internet access influence TA of e-LSS. The related instrument was administered to 583 business students at two universities in the Southeast. Regression analysis revealed that computer and Internet access affected the degree to which students expect Blackboard and the Internet to be easy to use. Computer and Internet access also affected their attitude towards these technologies. Additional findings revealed that socioeconomic status and race influenced computer ownership, convincingly.

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The fifteenth study examined in the context of reviewing papers in TA of e-LSS is Roca, at al.‟s “Understanding e-learning continuance intention: An extension of the Technology Acceptance Model” titled study. In their study, Roca, at al. proposed model in which the perceived performance component is decomposed into perceived quality and perceived usability. A sample of 172 respondents took part in this study. The results suggest that users‟ continuation intention is determined by satisfaction, which in turn is jointly determined by PU, information quality, confirmation, service quality, system quality, PEOU and cognitive absorption. More importantly, this study found that the influence of perceived quality, which is information quality, service quality and system quality, on confirmation and satisfaction was strong. The empirical results of the related study showed that information quality had a strong influence on confirmation, and the effect of information quality on satisfaction was stronger than service quality and system quality on satisfaction.
The sixteenth study examined in the context of reviewing papers in TA of e-LSS is Saadé, at al.‟s “Viability of the Technology Acceptance Model in Multimedia Learning Environments: a Comparative Study” titled study. In their study, Saadé, at al. conducted a comparative study consisting of 362 students. The related study‟s results suggest that TAM is a solid theoretical model where its validity can extend to the multimedia and e- learning context. The study provides a more intensive view of the multimedia learning system (MMLS) users and is an important step towards a better understanding of the user behavior on the system and a multimedia acceptance model. The results showed that PU has a significant impact on student attitude towards using MMLS. Attitude is confirmed to play an essential role of affecting behavioral intention to use MMLS. The

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findings validate the TAM as basis for this new model and support the value of attitude toward MMLS in student acceptance.
Above, all sixteen reviewed studies‟ details provided and explained. Nonetheless, in below Table 2, studies reviewed and their details are provided, and purposely the extension variables of these studies on TAM are listed correspondingly. For a comparative and contrastive, and a general view the below table shall be referred. # Title of Study Sample Size Referenced TA Model Added / Extension Variables
1
A technological acceptance of e-learning tools used in practical and laboratory teaching, according to the European higher education area
220
TAM New channel to learn, such as providing interactivity and control, feedback, communicativeness); factors that can influence users‟ motivations to use the tool, such as enjoyment, user tools, diffusion, methodology, user adaptation.
2
An Analysis of the Technology Acceptance Model in Understanding University Students‟
628
TAM E-learning self-efficacy, subjective norm, system accessibility, perceived usefulness, perceived ease of

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Behavioral Intention to Use e-Learning use, attitude, and behavioral intention
3
An enhanced technology acceptance model for e- learning systems in high- tech companies in Taiwan: analyzed by structural equation modeling
233
TAM Perceived flexibility and computer self-efficacy
4
Applying the technology acceptance model and flow theory to online e-learning users‟ acceptance behavior
102
TAM The most media-rich presentation interface, perceived usefulness, and concentration
5
ELAM: A Model for Acceptance and Use of E- learning by Teachers and Students
NA
UTAUT Performance expectancy, effort expectancy, social influence, and facilitating conditions. Specifically, the following factors are included in facilitating conditions variable: reliable infrastructure, institutional policies, training and support.

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6
E-learning motivation, Students‟ Acceptance/Use of Educational Portal in Developing Countries
150
UTAUT E-learning motivation, influence of teachers, parent and other peers, region and gender
7
Exploring teacher acceptance of e-learning technology
152
TAM Intention to use, perceived usefulness, perceived ease of use, subjective norm and computer self-efficacy
8
Extending the Technology Acceptance Model for E- learning: A Case Study of Iran
155
TAM subjective norm, personal innovativeness in domain of information technology and self-efficacy
9
Impact of media richness and flow on e-learning technology acceptance
NA
TAM The most media-rich presentation interface (text– audio–video presentation), user concentration, perceives usefulness
10
Instructional technology adoption of medical school faculty in teaching and learning: faculty
155
NA Individual characteristics, adoption patterns, perceived barriers and incentives to adoption and preferred

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characteristics and differentiating factors in adopter categories methods of learning about technology and support.
11
Perceptions of students and teachers about the use of e - learning / sharing portal in educational activities
200
NA Perceived motivation, the perceived usefulness and the perceived ease of use
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The effect of educational ideologies on technology acceptance
378
IDT Educational ideologies
13
The impact of internal locus of control on perceived usefulness and perceived ease of use in e- learning: an extension of the technology acceptance model
204
TAM Integrate internal locus of control (ILOC) and computer self-efficacy (CSE)
14
The Influence of Computer and Internet Access on E- learning Technology Acceptance
583
TAM Computer and Internet access

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15
Understanding e-learning continuance intention: An extension of the Technology Acceptance Model
172
TAM Perceived usefulness, information quality, confirmation, service quality, system quality, perceived ease of use and cognitive absorption
16
Viability of the Technology Acceptance Model in Multimedia Learning Environments: a Comparative Study
362
TAM Attitude
Table 2: Studies Reviewed and Their Details
As sixteen studies reviewed above showed, it is seen that most of the extension studies referred the TAM to provide a model in order to understand, implement and test the TA of e-LSS. Moreover, it is seen that the TAM is a venerated theory of TA and it has a use that has been widely researched in IT practices, and it is an important theoretical tool for e-LSS research and studies.
Nevertheless, all these studies tried to extend the TAM or any other fundamental TA models from diverse perspectives. This is just because of the fact that it is necessary to take into consideration the intended people and intended purpose. As long as intended people and intended purpose are recognized wholly, by using the fundamental models

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and principles in relation with TA explained above, it is possible to model and generate any sort of TA process for e-LSS.
Teachers, students, academicians, designers, purchasers, and all others involved with e-LSS projects are consistently advised to take into account the fundamental TA models and TA of e-LSS studies to give support to the design or purchasing process, training and informational sessions, implementation, and other activities in these contexts. Surely, to the degree that the factors predicting TA for e-LSS are controllable, they can be salient levers meant for acceptance and use.
However, there is also a need to continue exploring new theoretically motivated variables and relationships that can be added to fundamental TA models, or extended ones. Moreover, it is necessary for researchers to conduct studies for the purpose of identifying prominent beliefs that actors in e-LSS have on the subject of using e-LSS.
In a word, this paper is written for the interested readers wanting to know about not only the TA concepts, but also about the preceding TA of e-LSS studies.

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References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179 – 211.
Brown, A. (2006). Learning from a distance. Journal of Property Management, 71(4), 42 – 45.
Cappel, J. J., & Hayen, R. L. (2004). Evaluating e-learning: A case study. Journal of Computer Information Systems, 44(4), 49 – 57.
Davis, F. D. (1993). User acceptance of information technology: Systems characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38, 3, 475 – 487.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982 – 1003.
Dillon, A., & Morris M. (1996). User acceptance of information technology: theories and models. In: M. Williams (ed.), Annual Review of Information Science and Technology, Vol. 31, (Medford, NJ: Information Today).
Henderson, R. G., & Stewart, D. L. (2007). The Influence of Computer and Internet Access on E-learning Technology Acceptance. Business Education Digest 2007 Issue XVI, 3 – 16.

25
Hsia, J. W., & Tseng, A. H. (2008). An Enhanced Technology Acceptance Model for E- Learning Systems in High-Tech Companies in Taiwan: Analyzed by Structural Equation Modeling. 2008 International Conference on Cyberworlds, 39 – 44.
Işık, A. (2009). Perceptions of students and teachers about the use of e - learning / sharing portal in educational activities. METU, 2009.
Khan, F. U., & Iyer, S. (2009). ELAM: A Model for Acceptance and Use of E-learning by Teachers and Students. International Conference on e-learning (ICEL), Toronto, Canada, July 2009.
Liu, S. H., Liao, H. L., & Peng, C. J. (2005). Applying the technology acceptance model and flow theory to online e-learning users’ acceptance behavior. Issues in Information Systems, 4(2), 175 – 181.
Liu, S., Liao, H., & Pratt, J. A. (2009). Impact of media richness and flow on E-learning technology acceptance. Computers & Education, 52(3), 599 – 607.
Mackay, S., & Stockport, G. J. (2006). Blended learning, classroom and e-learning. The Business Review, 5(1), 82 – 88.
Maldonado, U. P. T., Khan, G. F., Moon, J., & Rho, J. J. (2009). E-learning motivation, Students' Acceptance/Use of Educational Portal in Developing Countries: A Case Study of Peru. 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology, 2009, 1431 – 1441.

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Martínez-Torres, M. R., Toral Marín, S.L., García, F. Barrero, Vázquez, S. Gallardo, Oliva, M. Arias, & Torres, T. (2008). A technological acceptance of e-learning tools used in practical and laboratory teaching, according to the European higher education area. Behaviour & Information Technology, 27: 6, 495 – 505.
Moghadam, A. H., & Bairamzadeh, S. (2009). Extending the Technology Acceptance Model for E-learning: A Case Study of Iran. 2009 Sixth International Conference on Information Technology: New Generations, 2009, 1659 – 1660.
Özdemir, D. (2004). The effect of educational ideologies on technology acceptance. METU, 2004.
Park, S. Y. (2009). An Analysis of the Technology Acceptance Model in Understanding University Students' Behavioral Intention to Use e-Learning. Educational Technology & Society, 12 (3), 150 – 162.
Roca, J. C., & Chiu, C. M. (2006). Understanding e-learning continuance intention: An extension of the technology acceptance model. Human-Computer Studies. v64 i6. 683 – 696.
Rogers, E. M. (1995). Diffusion of Innovations (4th ed.). New York: Free Press.
Saadé, G. R., Nebebe, F., & Tan W. (2007). Viability of the Technology Acceptance Model in Multimedia Learning Environments: A Comparative Study. Interdisciplinary Journal of Knowledge and Learning Objects, Vol. 3, 175 – 184.

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Tseng, A. H., & Hsia, J. W. (2008). The Impact of Internal Locus of Control on Perceived Usefulness and Perceived Ease of Use in E-Learning: An Extension of the Technology Acceptance Model. 2008 International Conference on Cyberworlds, 2008 – 815 – 819.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, (46:2), 186 – 204.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, (27:3), 425 – 478.
Yuen, H. K., & Ma, W. K. (2008), Exploring teacher acceptance of e-learning technology. Asia-Pacific Journal of Teacher Education, 36(3), 229 – 243.
Zayim, N. (2004). Instructional technology Adoption of Medical School Faculty in Teaching and Learning: Faculty Characteristics and Differentiating Factors in Adopter Categories. METU, 2004.

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