Vol. 2, No. 2 | July – December 2018 Online Support Services in e-Learning: A Technology Acceptance Model Hina Saeed∗ Moiz uddin Ahmed∗ Shahid Hussain† Shahid Farid‡ Abstract The development in the Information and Communication Technology in the contemporary digital age is rapidly changing the dynamics of the communication industry. The integration of technology in education is especially open and distance learning sector has given rise to e-leaning, which is the technology driven mode of education. Due to this emerging nature of the phenomenon, the students’ ability to accept and respond to online support services is important for the success of e-learning system. This paper investigates the attitude of the students towards online support services at the Allama Iqbal Open University (AIOU), Pakistan, using Technology Acceptance Model. The AIOU is the second largest distance learning institute of the world. A questionnaire was adopted and cus- tomized from the previous studies to collect the feedback from the students. The feedback from 220 students was collected using the said questionnaire. The statistical techniques using the descriptive statistics and linear regression were applied to analyze the data. The results show a positive attitude of students towards the online support services. The regression analysis elaborates that there is a sig- nificant influence of the “perceived usefulness” of online support services on “behavioral intention”. Furthermore, the regression analysis shows a significant influence of the “ease of use” on “behavioral intention”. Keywords: Technology Acceptance Model (TAM), E-learning, “Ease of use”, “Perceived Usefulness” 1. Introduction E-learning is the process of teaching via computers, the Internet and media technologies. It includes com- puter software based training, World Wide Web-based learning and broadcast media based learning [1]. The electronic mode uses Information and Communication Technology (ICT) to deliver the instructions [2]. These instructions are digitized using audio, video and mul- timedia technologies. The e-learning has become the need of learners of the present age. However, new is- sues are arising because technology is changing rapidly, affecting every field of life [3]. New models of e-learning are needed that can engage learners by catering their needs and styles of e-learning [4] through effective on- line support services. The online support services deliver the course instruc- tions by using web portals and Learning Management Systems (LMS). LMS is a specially designed applica- tion which provides a platform for executing online support services to distant learners [5]. The course instructions can be uploaded by the instructors that can be downloaded by the students, anywhere and anytime. The communication can also be established by using synchronous and asynchronous mode of in- structions [5]. The synchronous mode is the real-time interaction in which students, as well as teachers, are present. Audio/Video conferencing and chat discus- sions are the synchronous forms of communication. Asynchronous is offline communication between teach- ers and students in the form of email, and forums, etc. [6]. Both synchronous and asynchronous interactions are the basic building block of communication in an e-learning system; however; a successful online system requires acceptance of emerging tools and technologies by the relevant users. The success of any technology- based service is dependent on its acceptance which can be measured by employing the technology acceptance models and framework that analyze users’ intention of using new systems and applications. The important models of technology acceptance as reported in the lit- erature review are Unified Theory of Acceptance, and Use of Technology (UTAUT), Diffusion of Innovation (DOI), and Technology Acceptance Model (TAM) [vii]. The TAM has been widely used to find user acceptance, ∗Department of Computer Science, Allama Iqbal Open University, Islamabad †Department of Mass Communication, Allama Iqbal Open University, Islamabad ‡Department of Computer Science, BZU, Multan Corresponding Email: hinas1817@gmail.com SJCMS | P-ISSN: 2520-0755 | E-ISSN: 2522-3003 c© 2018 Sukkur IBA University - All Rights Reserved 22 Hina Saeed (et al.), Online Support Services in e-Learning: A Technology Acceptance Model (22-29) willingness, attitude, behavioral intention about the new technology and online support services which are affected indirectly by the perception of usefulness and ease. Keeping importance of technology transformation in view and implementation of e-learning this paper an- alyzes personal beliefs of “Perceived Usefulness” (PU), “Perceived Ease of Use” (EOU) and “Behavioral Inten- tions” (BI) about online support services in e-learning initiated at AIOU, Pakistan. 2. Related Work TAM has come out from the Theory of Reasoned Ac- tion (TRA) about human behavior [7]. It deals with the level of acceptance of Information Systems by analyzing the behavior and intentions of concerned users [8]. The Davis defines EOU as a degree of belief according to which a user will not be scared of any physical exertion while using a new technology system. The PU is con- ceptualized as a degree of belief according to which a user will be able to enhance his/her performance while using a new system [8]. Both the parameters are the basic building block of TAM theory and can predict the attitude of individuals towards using a new system. The attitude affects the “Behavioral intention” (BI) towards using a new system or application [9]. In the case of e-learning, the likelihood of using the support services depends upon the attitude of students towards computer-based systems [10]. It implies that if the online system is user-friendly and easy to op- erate, it may result in increasing the interaction level [11]. There is a number of external variables that can be used with TAM to investigate the learner’s inten- tion towards e-learning system [12]. The important variables include personal profiles of learners, organiza- tional parameters, characteristics of e-learning systems and access to ICT devices. These variables influence PU though EOU depending upon the degree of beliefs that impress the learner’s decision towards online sup- port services provided. The user interface of LMS and web portals can play an important role to strengthen the Human-Computer Interaction (HCI). The study of [13] highlighted that organizational poli- cies, training of using online systems and interface are important in the adoption of LMS in the higher ed- ucation institutes. The study investigated the effect of social networking, performance & effort expectancy and infrastructure towards the acceptance of LMS in the higher institutes in Kenya. The research described in [14] has analyzed the “behavioral intention” of stu- dents towards using e-learning applications. The study revealed that self-efficacy is the most important motiva- tional factor towards e-learning “behavioral intention”. The study [15] reviewed the acceptance of e-learning in developing countries. The study showed that social factors and motivation have a strong impact on inten- tion towards the usage of e-learning systems. The research [16] evaluated the parameters that in- fluence the acceptance and usage of e-learning in edu- cational institutes of New Zealand. The results high- lighted that personal profiles and organizational pa- rameters are both important towards the adoption of learning online. The research [17] evaluated students’ attitude in relation to TAM while using e-learning. The study found that attitude has a substantial role in e-learning acceptance among students enrolled in University in Malaysia. The regional characteristics in terms of localized parameters are also found as an im- portant factor in its acceptance. The acceptance level will be increased if the learning system will be devel- oped after considering its user’s local learning needs and requirement. It is concluded that the rapid developments and ex- pansion in modern technology has posed many chal- lenges regarding its acceptance among potential users [18]. Different theories and models have come up to analyze user behaviors. TAM is more important as it has widely been used to analyze the student’s feedback in many technologies enable learning paradigm. The previous studies have shown strong empirical results to prove the validity of TAM. Social media is also one of the areas which have been explored to study the learning impact on student’s behavior using TAM [19]. The systematic literature view has also concluded to use the TAM in new learning dimensions [20]. 3. Proposed Model The three online support services are selected to find their “perceived usefulness” and “ease of use” for en- couraging students’ “behavioral intention”. A. Quality of Digital Contents Digital Contents are an important part of an e- learning system. The digital contents comprise course tutorials, assignment, activities, questions, FAQS and announcements etc. Due to the geo- graphical distance between students and teachers the quality of digital contents is very important. These contents must match with the course objec- tives and the learning outcomes. The content may effectively contribute to self-paced learning of the students if it conforms to the quality standards. B. Uploading and Downloading of Contents There are heterogeneous Internet connections avail- able to students. These connections are dependent upon various parameters like efficiency, reliability, user-friendly interface, and error handling. Any complexity in the process may confuse the students [21]. A student requires seamless and error-free ser- vices for downloading the digital contents. There- fore, the “behavioral intention” is related to “ease of use” and “usefulness” of uploading/downloading. C. Asynchronous/synchronous interaction The synchronous and asynchronous interaction pro- Sukkur IBA Journal of Computing and Mathematical Sciences - SJCMS | Volume 2 No. 2 July – December 2018 c© Sukkur IBA University 23 Hina Saeed (et al.), Online Support Services in e-Learning: A Technology Acceptance Model (22-29) vides a communication mechanism between teach- ers and students and services departments like ad- mission, examination, etc. The communication should be reliable and fast for the prompt reply to the distant students. If synchronous and asyn- chronous interaction meets the students’ expecta- tions, it may result in increasing the degree of be- lief of “ease of use” and “perceived usefulness” and motivation. 4. Proposed Hypothesis The high degrees of PU and EOU can result in more confidence of students towards online support services while participating in e-learning activities [22]. This study, therefore, considered the important parameters reported in the literature review for the formulation of the research hypothesis. The statement form of the hypotheses are given below and the symbolic form is shown in figure 1. [H1] Uploading and downloading of digital contents will have a significant influence on “perceived useful- ness” of online support services. [H2] Uploading and downloading of digital contents will have a significant influence on “ease of use” of online support services. [H3] The quality of digital contents will have a significant influence on “perceived usefulness” of online support services. [H4] The quality of digital contents will have a signifi- cant effect on “ease of use” of online support services. [H5] The asynchronous and synchronous interaction will have a positive influence on “perceived usefulness” of online support services. [H6] The asynchronous and synchronous interaction will have a positive influence on “perceived ease of use” of online support services. [H7] The “perceived ease of use” will have a positive influence on “perceived usefulness” of online support services. [H8] The “perceived usefulness” will have a positive influence on “behavioral intention” of using online sup- port services. [H9] The “perceived ease of use” will have a posi- tive influence on “behavioral intention” of using online support services. 5. Research Methodology A. Sample A survey was conducted from the students of AIOU, Pakistan to evaluate the application of TAM on on- line support services. The AIOU is the second largest open university of the world in terms of number of stu- dents. The university is in the transformation phase of converting distance learning programs into modern e-learning based mode [23]. The survey was distributed to Computer Science stu- dents studying at AIOU. It was comprised of ques- tions about online support services on 5 – Point Likert scale. These questions were developed on the basis of e-learning initiatives taken at AIOU [23] and previous research of analyzing information systems using TAM [24 - 27]. The final questionnaire was comprised of 23 items to measure the six constructs Digital Content Quality (DCQ), Uploading/Downloading UD, Asynchronous/ Synchronous Interaction (ASI), “Perceived Usefulness” (PU), “Perceived Ease of Use” (EOU), “Behavioral In- tention to use” (BI). The survey questionnaire also com- prised the demographic items that indicated the age, gender, employment and accessibility to computer & Internet. The convenience sampling technique was used to collect feedback from the target population. B. Data Analysis The data collected with the help of questionnaire has been analyzed quantitatively. The demographics results are shown in Table 1. It shows that males are 80% and females 20%. The age of respondents ranged from 15- 20 to 30 +, however majority of the respondents be- longs to 26 – 30 age group. The majority (59.1%) are engaged in jobs. PCs with Internet connections are also available to a large majority of respondents. Table 1: Demographics Variable Frequency Percentage Gender Male 176 80% Female 44 20% Age Group 15-20 57 25.9% 21-25 26 11.8% 25-30 73 33.2% 30+ 64 29.1% In service Employed 130 59.1% Non-employed 90 40.9% Personal Computer Yes 220 100% No 0 Nil Internet Facility Yes 211 95.9% No 9 4.1% C. Descriptive Statistics The Descriptive statistics of six variables can be seen in Table 2. The mean value is high i.e. closer to 4 which indicate the influence of variables on acceptance of on- line support services. The SD values are approximately Sukkur IBA Journal of Computing and Mathematical Sciences - SJCMS | Volume 2 No. 2 July – December 2018 c© Sukkur IBA University 24 Hina Saeed (et al.), Online Support Services in e-Learning: A Technology Acceptance Model (22-29) equal to 1 which indicates small deviations from the mean value. Table 2: Descriptive Statistics Variables Mean Std. Deviation EOU1 4.04 0.840 EOU2 3.90 1.066 EOU3 3.87 1.061 EOU4 3.88 1.070 PU1 3.93 1.074 PU2 3.85 1.056 PU3 3.88 1.172 PU4 3.93 1.042 BI1 3.88 1.185 BI2 3.74 1.148 BI3 3.77 1.054 UD2 3.84 1.121 UD3 3.80 1.081 UD4 3.75 1.087 UD5 3.80 1.061 DC1 3.93 0.953 DC2 3.93 1.002 DC3 3.80 1.064 DC4 3.94 0.972 ASI4 3.76 1.118 ASI5 4.06 0.894 ASI6 3.87 1.052 ASI7 3.92 1.022 D. Reliability Measures The internal reliability and construct validity of the questionnaire were evaluated to determine the stability and suitability of the questions. There are 220 entries of data and for this range of data the factor loading val- ues should be 0.5 least and the Cronbach’s alpha range should be between 0.6-0.7, 0.8 is considered as strong re- liability between variables while below 0.6 is as weaken. The below table shows that total 23 variables are being used for factor analysis, having 0.825 Cronbach’s alpha value for the reliability of these variables that is much efficient to prove that. Table 3: Cronbach Alpha Cronbach’s Alpha Cronbach’s Al- pha Based on Standardized Item No. of Items 0.825 0.824 23 E. Appropriateness of Data (adequacy) The appropriateness and sphericity of data were calcu- lated through KMO and Bartlett’s test. Kaiser-Meyer Olkin test value was 0.790 (close to 0.8), and therefore, considered as good [28]. The significance value was less than 0.05 that showed the data appropriateness for fur- ther factor analysis. Table 4: KAISER-MEYER-OLKIN (KMO) Mea- sure KMO Measure of Sampling Adequacy 0.790 Bartlett’s Test of Sphericity Approx. Chi-Square 1.367E Df 253 Sig. 0.000 Figure 1: Proposed Model with Hypothesis relation Sukkur IBA Journal of Computing and Mathematical Sciences - SJCMS | Volume 2 No. 2 July – December 2018 c© Sukkur IBA University 25 Hina Saeed (et al.), Online Support Services in e-Learning: A Technology Acceptance Model (22-29) F. Factor Loading The factor loading matrix is shown below in Table 5 which indicates the relationship of the questions with factors. Higher the loading across the item, stronger is its relationship with the factor [29]. The values are between 0.5 and 0.7 which indicate a strong relation- ship among the items. The Exploratory Factor Analy- sis (EFA) was calculated by computing the eigenvalues. The factors with eigenvalues greater than 1 are consid- ered significant and others are discarded on the basis of Kaiser latent root criterion [29]. Six factors with eigen- values greater than 1 were extracted which resulted in 57% of the variance. Table 5: Factor Loading 1 2 3 4 5 EOU1 0.800 EOU2 0.824 EOU3 0.758 EOU4 0.582 PU1 0.497 PU2 0.530 PU3 0.723 PU4 0.771 BI1 BI2 BI3 UD1 0.646 UD2 0.740 UD3 0.749 UD4 0.686 DC1 0.566 DC2 0.699 DC3 0.656 DC4 0.715 ASI1 0.706 ASI2 0.722 ASI3 0.646 ASI4 0.562 %Variance explained 12.806 11.001 8.531 8.474 8.413 Cumulative percent- age 12.806 23.807 32.337 40.811 49.224 6. Analysis of Proposed Hypothesis To test the first hypothesis (H1), the regression analysis was carried out as shown in Table 6. Table 6 shows that uploading and downloading of contents is positively re- lated to “perceived usefulness”. It reveals that strong relationship between online support services and “per- ceived usefulness”. The p-value shows that H1 is sup- ported and accepted. Table 7 shows that uploading and downloading of con- tents is positively related to “ease of use”. It reveals that strong relationship exists between online support services and “ease of use”. Table 8 shows the regression analysis for H3. The re- sult shows that digital content quality has a significant influence on “perceived usefulness”. Table 9 shows the regression analysis for H4. The result shows that digital content quality (DC) has a significant influence on “ease of use”. Table 10 shows the regression analysis for H5. The result shows that asynchronous and synchronous inter- action has a significant influence on “perceived useful- ness”. Table 11 shows the regression analysis for H6. The result shows significant influence of asynchronous and synchronous interaction on “perceived ease of use”. Table 12 shows regression analysis for H7. The result shows that there is significant influence of “perceived ease of use” on “perceived usefulness”. Table 13 shows the regression analysis for H8, which elaborates that there is significant influence of “per- ceived usefulness” of online support services on “be- havioral intention”. Table 14 shows the regression analysis for H9. The re- sult shows a significant influence of “ease of use” on “behavioral intention”. The results show that effects are significant and all the hypotheses have been supported. The previous studies have also shown significant effect of EOU on PU and BI [9, 30]. The students of e-learning have shown posi- tive attitude towards online support services. The “per- ceived usefulness” has a greater significant correlation with usage behavior than “perceived ease of use”. This led to hypothesize that “perceived ease of use” may be a causal predecessor to “perceived usefulness” rather than a direct determinant of technology usage [9]. The positive feelings of the users to “ease of use” are cer- tainly linked with its sustainability [31]. The proposed model with hypothesis results is shown in figure 2. Table 6: Regression Result For H1 H1 B Standard- Error(B) T P R Square UD→PU 0.167 0.064 2.504 <0.013 0.028 Table 7: Regression Result For H2 H2 B Standard- Error(B) T P R Square UD→EOU 0.205 0.067 3.091 <0.002 0.042 Table 8: Regression Result For H3 H3 B Standard- Error(B) T P R Square DC→PU 0.159 0.075 2.375 <0.018 0.025 Table 9: Regression Result For H4 H4 B Standard- Error(B) T P R Square DC→PU 0.160 0.079 2.395 <0.017 0.026 Sukkur IBA Journal of Computing and Mathematical Sciences - SJCMS | Volume 2 No. 2 July – December 2018 c© Sukkur IBA University 26 Hina Saeed (et al.), Online Support Services in e-Learning: A Technology Acceptance Model (22-29) Figure 2: Proposed Model with Hypothesis results Table 10: Regression Result For H5 H5 B Standard- Error(B) T P R Square ASI→PU 0.329 0.075 5.146 <0.000 0.108 Table 11: Regression Result For H6 H6 B Standard- Error(B) T P R Square ASI→EOU 0.160 0.074 2.393 <0.018 0.026 Table 12: Regression Result For H7 H7 B Standard- Error(B) T P R Square EOU→PU 0.537 0.060 9.407 <0.000 0.289 Table 13: Regression Result For H8 H8 B Standard- Error(B) T P R Square PU→BI 0.267 0.069 4.086 <0.000 0.071 Table 14: Regression Result For H9 H9 B Standard- Error(B) T P R Square EOU→BI 0.124 0.075 1.840 <0.067 0.015 7. Conclusion The global development in the technological era is posing many challenges but at the same time it is opening many avenues of advancements. The dis- tance learning is also affected by the technological developments and shifting towards electronic mode. E-learning is based on online support services, which rely on uploading/downloading of digital contents and interaction through synchronous/asynchronous modes [32]. The role of student support services and its acceptance have become more important [33 - 35]. As in other relevant studies, this study revealed that TAM can effectively be used to predict and understand users’ perception on using e-learning support services. Sukkur IBA Journal of Computing and Mathematical Sciences - SJCMS | Volume 2 No. 2 July – December 2018 c© Sukkur IBA University 27 Hina Saeed (et al.), Online Support Services in e-Learning: A Technology Acceptance Model (22-29) The results have shown favorable response from the respondents. The hypotheses on “behavioral in- tention” of using online support service are supported. The results also show that there is a positive readiness for implementation of e-learning systems in Pakistan. The research study shows that high acceptance rate of technology for online teaching and learning can lead to enhance the learning capacity and knowledge level of students. Results also show that uploading and down- loading of contents is positively related to “ease of use”. The digital content quality has a significant influence on “perceived usefulness”. Furthermore, asynchronous and synchronous interaction has a significant influence on “perceived usefulness”. The result also elaborate that there is significant influence of “perceived ease of use” on “perceived usefulness” and a significant influence of “ease of use” on “behavioral intention”. The students of e-learning have shown positive attitude towards online support services. 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