International Journal of Interactive Mobile Technologies (iJIM) – eISSN: 1865-7923 – Vol. 15, No. 18, 2021 Paper—User Behavioral Intention to Use Online Distance Learning (ODL): The Role of Self-Efficacy… User Behavioral Intention to Use Online Distance Learning (ODL): The Role of Self-Efficacy and Domain Knowledge https://doi.org/10.3991/ijim.v15i18.24539 Mohamad Rahimi Mohamad Rosman(), Izzatil Husna Arshad, Mohamad Sayuti Md Saleh, Nurulannisa Abdullah, Faizal Haini Fadzil, Mohd Zafian Mohd Zawawi Universiti Teknologi MARA Kelantan Branch, Kelantan, Malaysia rahimimr@uitm.edu.my Abstract—The rise of novel coronavirus 2019 has shifted the roles of education industry. Face-to-face have become a distant memory; students and educators are now heavily relying on the digital communication. Application such as Google Meet, Webex, Webinar, Stream Yard, Zoom, and many more have become the new norm among educators and students. However, the sudden dependency on the digital technologies raises a question on the user intention to use this new digital technology. Therefore, the objective of this study is to determine the role of self-efficacy and domain knowledge towards user behavioral intention to use online distance learning. An instrument was developed by adopting to previous instruments and was analyze using Statistical Package for Social Science and SmartPLS for inferential analysis. Findings shows that the exogenous variables are capable to explained between 47.8% to 68.1% of the endogenous variables. Keywords—online distance learning, domain knowledge, self-efficacy, behavioral intention 1 Introduction COVID-19 stands for coronavirus disease and is referred to as the new coronavirus or ‘2019-nCoV’ for 2019. On 11 March 2020, the World Health Organization (WHO) classified COVID-19 as a global pandemic [1]. Because of COVID-19, thousands of school closures are being enforced worldwide. In April 2020, UNESCO [2] announced that 1, 576, 021, 818 students in 188 countries were affected at all levels of learning. Among the Southeast Asian countries, Malaysia has reported a high number of COVID-19 positive cases [3]. On March 18, 2020, the Movement Control Order (MCO) was enacted in Malaysia [3]. The Minister of Health issued a set of regulations under the Prevention and Control of Infectious Diseases Act 1988 (the Act) as urgent mea- sures to combat the spread of the Corona Virus. The MCO not only restricts movement, but has enforced the closure, whether public or private, of all non-essential premises, including kindergartens, schools, colleges, and universities. 4 http://www.i-jim.org https://doi.org/10.3991/ijim.v15i18.24539 mailto:rahimimr@uitm.edu.my Paper—User Behavioral Intention to Use Online Distance Learning (ODL): The Role of Self-Efficacy… Stopping all educational activities and sending their students home was the imme- diate response of universities and other higher education institutions. The closing of universities has contributed to the introduction of creative education approaches to ensure continuing education for students [4]. Universities are expected to turn to online forms of teaching and learning activities. With social distancing standards, all face-to- face classes have been suspended [5]. Online learning was widely promoted to replace conventional face-to-face learning [6]. A portfolio of learning strategies and materials that can cater to the various needs of learners across a range of learning environments needs to be created [7]. Several studies have also found challenges posed by online learning to students during MCO [8]. Many university students are not prepared for that mode. They lack knowledge on user behavioural intention to use or engage with Online Distance Learning (ODL). The performance of the online learning depends not only on the acceptance of the use of the technology, but also on the user’s technology readiness and its self-efficacy on the Internet [9]. Therefore, due to the obvious challenges of ODL, this paper seeks to examine the relationship between self-efficacy, domain knowledge, perceived usefulness, perceived ease of use, attitude towards ODL and behavioural intention to use ODL in higher education to provide students, lecturers, and faculty management with a deeper under- standing of future teaching and course development planning. 2 Research model Figure 1 shows the research model of the study, adopted from Technology Accep- tance Model [10]. There are a total of 6 variables altogether. A total of 7 hypothesis were formulated and subsequently explain below. Fig. 1. Model for paper TAM which proposed by Davis [10] highlighted that external factors may affect purpose and actual use through mediated effects on perceived usefulness and perceived ease of use. One of the factors emphasized by this study is domain knowledge. Das [11] pointed out that the way domain knowledge is structured in its computer incorporation aids knowledge collection. Domain knowledge (an element of user personal infrastructure) also revealed as one of the key factors affecting information retrieval (IR) system use [12]. Further- more, Ahmed Younis Alsabawy, Cater-Steel and Soar [13] stated that to ensure high quality of service delivery, two factors should be considered, consisting of system iJIM ‒ Vol. 15, No. 18, 2021 5 Paper—User Behavioral Intention to Use Online Distance Learning (ODL): The Role of Self-Efficacy… and information quality. Information quality which related to domain knowledge is a critical element in measuring e-learning system success. F. Mohammadi, A. Abrizah, M. Nazari [14] studied teachers’ perceptions of information quality in Farsi Web-based Learning Resources and highlighted 14 indicators of characteristic which indicated that information quality could reflect in measuring the success of information systems. Meanwhile Roca, Chiu, and Martínez [15] identified that that information quality had significant effects on user satisfaction, which directly affected the user’s intention to use e-learning systems. Arbaugh [16] stated that perceived usefulness of the e-learning would improve the attitude of students toward learning experience and students’ consideration to enrol into online courses in future. E-learning users believe to accept useful educational services with high quality, whereby a secure environment of e-learning systems can contribute to support perceived usefulness. This includes security in information exchange between users, educational materials usage, and provision of respond to students’ enquiries [13]. Thus, the ability of e-learning to support safe and secure environment may affect users’ attitude towards ODL initiative. Likewise, Cerezo, et al. [17] highlighted that perceived usefulness may increase motivation for the learning task. Volery and Lord [18] examined critical success factors in online education and found out that quality of a system was a key factor in measuring online education according two indicators: ease of access in navigation and interface. In addition, Tella [19] used seven indicators to evaluate e-learning system quality which comprise of availability, easy to use, user-friendly, interaction, accessibility, attractive features, and presentation. Furthermore, Eom and Stapleton [20] emphasized system quality as a system which possesses distinguishing characteristics which evaluated response time, systems accessibility, system reliability, systems flexibility, systems usefulness, ease of use, ease of learning, etc. Chiu and Wang [21] stated that intrinsic values are significant predictors of students’ intentions to continuously participate in e-learning. According to Muhammad Faizal Samat, Nur Amalina Awang, Siti Nor Adawiah Hussin, and Farahiyah Akmal Mat Nawi [22], selection of ODL platforms and tools should consider on healthy emotional state to ensure educators and learners could have intention to explore the technologies pro- vided. Similarly, Khechine, Raymond and Augier [23] pointed out that intrinsic value is the significant contributor in the estimating behavioural intention and use behaviour in the context of ODL adoption. Therefore, based on the above arguments, the following are the hypothesis of the study: H1: Self-efficacy has a positive and significant relationship with perceived usefulness. H2: Self-efficacy has a positive and significant relationship with perceived ease of use. H3: Domain knowledge has a positive and significant relationship with perceived usefulness. H4: Domain knowledge has a positive and significant relationship with perceived ease of use. H5: Perceived usefulness has a positive and significant relationship with attitude towards ODL. H6: Perceived ease of use has a positive and significant relationship with attitude towards ODL. 6 http://www.i-jim.org Paper—User Behavioral Intention to Use Online Distance Learning (ODL): The Role of Self-Efficacy… H7: Attitude towards ODL has a positive and significant relationship with behavioural intention to use ODL. 3 Methodology The conduct of this study is quantitative using questionnaire. An instrument was developed based on previous research and validated based on pre-test among experts. The selection of experts based on the following criteria: (1) academic qualification, (2) academic experience, (3) expertise within the field of Information System (IS). The expert review process took 2 weeks before the instrument was sent back to the researchers. The instrument was modified based on the recommendation and sugges- tion from the experts. Next, face validity was conducted. A total of 10 respondents were chosen from the Faculty of Information Management, Universiti Teknologi MARA Cawangan Kelantan. These respondents were excluded from the total sampling. Pilot test of instrument was carried out to determine the reliability of the instrument. A total of 60 respondents involves in the pilot test. The following Table 1 shows the results of the reliability analysis. The result of the Cronbach’s alpha shows a value ranging from 0.770 to 0.963, indicating a sufficient result to confirm the reliability of the instrument as suggested by Nunnally [24]. Table 1. Reliability analysis of pilot study assessment Construct Items Cronbach Alpha SEF 5 0.770 DOK 4 0.873 PEU 4 0.910 EOU 3 0.900 ATT 3 0.924 BIU 3 0.963 The respondent of the study was selected based on convenience sampling. The respondents were chosen based on the following criteria: (1) undergraduate students enrolled with Universiti Teknologi MARA Cawangan Kelantan, (2) Students status currently active, and (3) enrolled for at least one subject that utilize ODL for the current semester. An invitation email was sent to undergraduate students that meet the criteria; a total of 524 valid responses were received. Data were coded, perform data cleaning, before analysis using SPSS and SmartPLS. The subsequent section describes the find- ings of the study. 4 Results and findings The following Table 2 shows the demographic profile of the respondents. A total of 524 respondents have responded the survey. The gender profile showed 77.3% (n=405) of respondents are female, while 22.7% (n=119) are male. The age of respondent is mostly between 20 – 30 years (n=354 or 67.6%), below 20 (n=164 or 31.3%), 31–50 (n=5 or 1.0%) and above 50 (n=1 or 0.2%). iJIM ‒ Vol. 15, No. 18, 2021 7 Paper—User Behavioral Intention to Use Online Distance Learning (ODL): The Role of Self-Efficacy… From the locality perspective, most respondents are from Kelantan (n=240 or 45.8%), Terengganu (n=65 or 12.4%), Pahang (n=53 or 10.1%), Selangor (n=52, 9.9%), Kedah (n=29 or 5.5%), Perak (n=25 or 4.8%), Johor (n=19, 3.6%), Kuala Lumpur (n=13 or 2.5%), Pulau Pinang (n=11 or 2.1%), Negeri Sembilan (n=8 or 1.5%), Melaka (n=5 or 1.0%), Perlis (n=3 or 0.6%), and Sabah (n=1 or 0.2%). From the context of faculty, a total of 47.5% (n=249) respondents are from Faculty of Information Management, follow with Faculty of Business and Management 26.1% (n=137), Faculty of Administrative Science and Policy Studies 11.3% (n=59), Faculty of Accountancy 5.7% (n=30), Faculty of Art and Design 5.0% (n=26), and Faculty of Computer and Mathematical Science 4.4% (n=23). All the respondent divided into 67.2% (n=352) diploma level, 31.7% (n=166) of degree, and others 1.1% (n=6). Table 2. Demographic profile Item Frequency % Gender Male 119 22.7 Female 405 77.3 Age Below 20 164 31.3 20–30 354 67.6 31–50 5 1.0 > 50 1 0.2 State Kelantan 240 45.8 Terengganu 65 12.4 Pahang 53 10.1 Kedah 29 5.5 Perlis 3 0.6 Pulau Pinang 11 2.1 Kuala Lumpur 13 2.5 Selangor 52 9.9 Negeri Sembilan 8 1.5 Melaka 5 1.0 Johor 19 3.6 Sabah 1 0.2 Perak 25 4.8 Faculty Faculty of Information Management 249 47.5 Faculty of Administrative Science and Policy Studies 59 11.3 Faculty of Business and Management 137 26.1 Faculty of Accountancy 30 5.7 Faculty of Computer and Mathematical Sciences 23 4.4 Faculty of Art and Design 26 5.0 Level of study Diploma 352 67.2 Degree 166 31.7 Others 6 1.1 8 http://www.i-jim.org Paper—User Behavioral Intention to Use Online Distance Learning (ODL): The Role of Self-Efficacy… Table 3 shows the result of the measurement model analysis. A total of 2 runs was conducted. During the initial run, all indicators meet the expected value as suggested by Hair, Sarstedt, Hopkins, and G. Kuppelwieser [25], except for SEF (factor load- ing 0.328). SEF5 was removed from the initial model (4.5%). A second run analysis shows that all factor loadings, average variance extract (AVE), Cronbach’s alpha and composite reliability (CR) meet the expected threshold [25]. The factor loading for SEF between 0.628 to 0.846 (AVE 0.623, CR 0.867), DOK between 0.861 to 0.896 (AVE 0.731, CR 0.916), PEU between 0.846 to 0.917 (AVE 0.789, CR 0.937), EOU between 0.892 to 0.926 (AVE 0.834, CR 0.938), ATT between 0.914 to 0.941 (AVE 0.869, CR 0.952), and BIU between 0.959 to 0.970 (AVE 0.931, CR 0.976). Therefore, it is assumed that convergence validity has been ascertained. Table 3. Final assessment of convergence validity Construct Indicators Factor Loading Average Variance Extract (AVE) Cronbach’s Alpha Composite Reliability Self-Efficacy (SEF) SEF1 0.846 0.623 0.795 0.867 SEF2 0.824 SEF3 0.839 SEF4 0.628 Domain Knowledge (DOK) DOK1 0.861 0.731 0.877 0.916 DOK2 0.884 DOK3 0.775 DOK4 0.896 Perceived Usefulness (PEU) PEU1 0.846 0.789 0.910 0.937 PEU2 0.902 PEU3 0.887 PEU4 0.917 Perceived Ease of Use (EOU) EOU1 0.892 0.834 0.900 0.938 EOU2 0.926 EOU3 0.921 Attitude Towards ODL (ATT) ATT1 0.941 0.869 0.924 0.952 ATT2 0.914 ATT3 0.940 Behavioural Intention Towards ODL (BIU) BIU1 0.966 0.931 0.963 0.976 BIU2 0.959 BIU3 0.970 iJIM ‒ Vol. 15, No. 18, 2021 9 Paper—User Behavioral Intention to Use Online Distance Learning (ODL): The Role of Self-Efficacy… Next step is to access the model for discriminant validity. A Fornell-Larcker Criterion was conducted. The following Table 4 shows the result of the Fornell-Larcker Criterion assessment. Based on the result, the square root of the AVE is bigger compared to its previous value, therefore indicating that discriminant validity has been achieved. Table 4. Result of Fornell-Larcker criterion ATT BIU DOK EOU PEU SEF ATT 0.932 BIU 0.781 0.965 DOK 0.612 0.474 0.855 EOU 0.818 0.678 0.677 0.913 PEU 0.737 0.626 0.640 0.827 0.888 SEF 0.610 0.502 0.736 0.670 0.648 0.790 The following Table 5 shows the result of a structural model analysis. The result show that all hypotheses were accepted. Self-efficacy has a significant and positive relation- ship with perceived usefulness (H1: Supported, t=6.947, p=0.000) and perceived ease of use (H2: Supported, t=7.619, p=0.000). Domain knowledge also has a positive and significant relationship with perceived usefulness (H3: Supported, t=5.855, p=0.000) and perceived ease of use (H4: Supported, t=7.009, p=0.000). On the other hand, per- ceived usefulness has a significant and positive relationship with attitude towards ODL (H5: Supported, t=3.429, p=0.001). Likewise, perceived ease of use also has a posi- tive and significant relationship with attitude towards ODL (H6: Supported, t=12.715, p=0.000). Lastly, attitude towards ODL also has a significant and positive relationship with behavioural intention to use ODL (H7: Supported, t=42.395, p=0.000). The fol- lowing Figure 2 shows the final structural model of the study. Table 5. Relationship between variables (direct effect) Relationship Coefficient Std. Dev. t-Value p-Values Decision H1 SEF  PEU 0.387 0.056 6.947** 0.000** Supported H2 SEF  EOU 0.377 0.049 7.619** 0.000** Supported H3 DOK  PEU 0.355 0.061 5.855** 0.000** Supported H4 DOK  EOU 0.401 0.057 7.009** 0.000** Supported H5 PEU  ATT 0.192 0.056 3.429** 0.001** Supported H6 EOU  ATT 0.660 0.052 12.715** 0.000** Supported H7 ATT  BIU 0.781 0.018 42.395** 0.000** Supported Notes: *t > 1.645, p-value > 0.05, ** t > 2.58, p-value < 0.01. 10 http://www.i-jim.org Paper—User Behavioral Intention to Use Online Distance Learning (ODL): The Role of Self-Efficacy… Fig. 2. Final structural model of the study The next Table 6 shows the R2 result of study. Wherry [26] suggested that research should use the adjusted R2 instead of the regular R2 because there were some issues with regular R2. Regular R2 value increased when additional predictor constructs were included in the model. From the result shown on the Table 6, the exogenous variables of the study were capable to explain the 68.1% (moderate), 61% (moderate), 52.3% (moderate), and 47.8% (small) from the overall variance of perceived use- fulness, perceived ease of use, attitude towards ODL and behavioural intention to use ODL [27]. Table 6. Result of coefficient of determination score Construct R Square R Square Adjusted Decision ATT 0.681 0.679 Moderate BIU 0.610 0.609 Moderate EOU 0.523 0.521 Moderate PEU 0.478 0.476 Small The next step is to assess the level of effect size (f 2). Cohen [28] recommended that the f 2 values of 0.35, 0.15, and 0.02 were regarded as large, medium, and small effect sizes, respectively [29], [30], [31]. Table 7 shows the effect level of study size. It can be concluded that all constructs have an effect size ranging from 0.036 to 0.1.565. iJIM ‒ Vol. 15, No. 18, 2021 11 Paper—User Behavioral Intention to Use Online Distance Learning (ODL): The Role of Self-Efficacy… Table 7. Assessing the level of effect size (f 2) Relationship f 2 Decision ATT  BIU 1.565 Large PEU  ATT 0.036 Medium EOU  ATT 0.430 Large SEF  PEU 0.132 Small SEF  PEU 0.135 Small DOK  EOU 0.111 Small DOK  EOU 0.155 Medium 5 Conclusion The study has investigated and identified the user behavioural intention to use Online Distance Learning (ODL). The study seeks to examine the relationship between self-efficacy, domain knowledge, perceived usefulness, perceived ease of use, attitude towards ODL and behavioural intention to use ODL in higher education to provide stu- dents, lecturers, and faculty management with a deeper understanding of future teach- ing and course development planning. The result show that all hypotheses were accepted. The investigation led to identi- fying the role of self-efficacy and domain knowledge towards ODL since all students involved in online studies due to the changes of teaching and learning methods during Covid-19 pandemic. The study conducted based on convenience sampling of Faculty of Information Management, Universiti Teknologi MARA Cawangan Kelantan only and it may not be representative of the whole university. The study has contributed to the effectiveness for online teaching and learning method used during COVID-19 pandemic as precaution actions to ensure schools and academic institutions can continue its operation in the education system. The implication of this study will be beneficial for the university to engage programs for successful implemen- tation towards adopting Online Distance Learning (ODL). 6 Acknowledgement This research work is supported by the Research Management Centre (RMC), Uni- versiti Teknologi MARA Kelantan Branch under Special Interest Group (SIG) fund. 7 References [1] Cucinotta, D., & Vanelli, M. (2020). WHO declares COVID-19 a pandemic. Acta Bio-Medica: Atenei Parmensis, 91(1), 157–160. [2] UNESCO (2020). Global Monitoring of School Closures caused by COVID-19. 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International Journal of Online and Biomedical Engineering, 16(1), 63–82. https://doi.org/10.3991/ijoe .v16i01.12263 8 Authors Mohamad Rahimi Mohamad Rosman is a senior lecturer at the Faculty of Information Management, Universiti Teknologi MARA Kelantan Branch. His research interests are information system management, user engagement, digital library, and content management. Izzatil Husna Arshad is a senior lecturer at the Faculty of Information Management, Universiti Teknologi MARA Kelantan Branch. Her research interests are information system management and information management. 14 http://www.i-jim.org https://doi.org/10.4018/978-1-60960-615-2.ch005 https://doi.org/10.1016/j.im.2008.02.003 https://doi.org/10.24191/ajue.v16i3.9787 https://doi.org/10.1111/bjet.12905 https://doi.org/10.1016/j.jfbs.2014.01.002 https://doi.org/10.1016/j.jfbs.2014.01.002 https://doi.org/10.1214/aoms/1177732951 https://doi.org/10.1214/aoms/1177732951 https://doi.org/10.1002/bs.3830330104 https://doi.org/10.3991/ijim.v14i06.13479 https://doi.org/10.3991/ijim.v14i06.13479 https://doi.org/10.3991/ijim.v15i08.20415 https://doi.org/10.3991/ijim.v15i08.20415 https://doi.org/10.3991/ijoe.v16i01.12263 https://doi.org/10.3991/ijoe.v16i01.12263 Paper—User Behavioral Intention to Use Online Distance Learning (ODL): The Role of Self-Efficacy… Mohamad Sayuti Md Saleh is a finance lecturer at Universiti Teknologi MARA, Kelantan branch. He is very committed to deepening the latest knowledge whether in the field of finance, Islamic banking, entrepreneurship, and economics. Nurulannisa Abdullah is a senior lecturer at the Faculty of Information Man- agement, Universiti Teknologi MARA Kelantan Branch. Her research interests are electronic records management, digital repository, and cataloguing education. Faizal Haini Fadzil is a senior lecturer at the Faculty of Information Management, Universiti Teknologi MARA Kelantan Branch. His research interests are information system management, web technologies and content management. Mohd Zafian Mohd Zawawi is a senior lecturer at the Faculty of Information Management, Universiti Teknologi MARA Kelantan Branch. His research interests are information system management and user competencies. Article submitted 2021-06-04. Resubmitted 2021-07-22. Final acceptance 2021-07-23. Final version published as submitted by the authors. iJIM ‒ Vol. 15, No. 18, 2021 15