UTAUT Application in South African ODeL Context: LMS Adoption among First-Year Students

UTAUT Application in South African ODeL Context: LMS Adoption among First-Year Students | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article UTAUT Application in South African ODeL Context: LMS Adoption among First-Year Students Jack This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9356688/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigated the adoption of learning management systems (LMS) in a South African open and distance e-learning (ODeL) context using the Unified Theory of Acceptance and Use of Technology (UTAUT). First-year students of English modules participated in the study. Structural equation modelling (SEM) was employed to analyse data and test hypotheses. Contrary to previous research, findings revealed that all UTAUT constructs (performance expectancy, effort expectancy, attitude, social influence, facilitating conditions, self-efficacy, and anxiety) had no significant relationship with behavioural intention to use the LMS. However, age, gender, experience, and computer access were identified as moderating variables. These results highlight the importance of context in e-learning research, suggesting that findings from the Global North may not always apply to the Global South context. The study contributes to the limited body of knowledge on UTAUT application in the South African ODeL context and emphasises the need for further research to inform stakeholders in implementing effective e-learning strategies. Educational Philosophy and Theory Acceptance e-learning learning management system online learning open distance e-learning structural equation modelling Unified Theory of Acceptance and Use of Technology first year students adoption hypothesis Figures Figure 1 Figure 2 INTRODUCTION The article reports on the findings of a doctoral study (Chokwe, 2022 ) that investigated the adoption of the learning management system (LMS) in the South African open and distance e-learning (ODeL) context. First-year students who were enrolled in the Department of English Studies participated in the study. The Unified Theory of Acceptance and Use of Technology (UTAUT) underpinned the study (Venkatesh et al., 2003 ). Structural equation modelling was used to analyse the data and evaluate the hypotheses, and it was found that none of the UTAUT constructs were related to behavioural intention. However, age, gender, experience and computer access were moderating variables (Chokwe, 2022 ; Bayaga, A., & du Plessis, A., 2024 ). Although UTAUT was mainly used in studies conducted in the Global North context, it was, however, rarely applied in the Global South context, particularly in the South African ODeL context (Mabunda & Rambe, 2020). As a result, this study sought to fill this lacuna in the body of knowledge. Accordingly, the findings of the study contribute to the body of knowledge on the use of UTAUT in the South African ODeL context and similar contexts in developing countries (Chokwe, 2022 ; Mabunda & Rambe, 2020). Furthermore, the findings indicate that UTAUT constructs do not correlate with behavioural intention of using the learning management system (LMS) for teaching and learning (Chokwe, 2022 ; Bayaga, A., & du Plessis, A. 2024 ). Therefore, UTAUT enables leaders and stakeholders of ODeL institutions to understand factors orchestrating the acceptance and usage of LMS for teaching and learning (Chokwe, 2022 ; Mabunda & Rambe, 2020; Bayaga, A., & du Plessis, A., 2024 ). LITERATURE REVIEW To understand students' perceptions about using Moodle, Sumak, Polancic and Hericko ( 2010 ) used the UTAUT research model and hypothesised relationships between UTAUT constructs, empirically testing them using the structural equation modelling (SEM) approach. They argue that UTAUT presents a more complete picture of technology acceptance than any other individual model is able to do. This comprehensive approach allows for a more nuanced understanding of the factors influencing technology adoption in educational settings (Alshehri et al., 2022 ). Furthermore, Sumak et al. ( 2010 ) report that performance expectancy and social influence positively affect attitudes towards Moodle use. This finding aligns with recent research by Jiang et al. ( 2023 ), who also found that performance expectancy plays a crucial role in technology adoption in educational contexts. In contrast, Sumak et al. ( 2010 ) found that effort expectancy does not influence students' attitudes towards using Moodle. This result challenges some assumptions about the importance of ease of use in technology adoption and highlights the complexity of factors influencing student behaviour (Martinez et al., 2023). However, Martinez et al. (2023) argue that there was no scientific proof that performance expectancy, effort expectancy, and attitudes towards using the e-learning system correlated with students' behavioural intention. This finding underscores the need for further research to better understand the relationship between these constructs and behavioural intentions in e-learning environments (Robitzsch, 2022 ). More importantly, Sumak et al. ( 2010 ) argue that students do not use LMS because it is easy to use but because they find it useful for their studies. This emphasis on usefulness over ease of use has been corroborated by recent studies, such as Jiang et al. ( 2022 ), which found that perceived usefulness is a stronger predictor of technology adoption than perceived ease of use in educational settings. The current study also used SEM as an inferential statistical technique, following the trend in recent research on technology adoption in education (Alshehri et al., 2022 ; Jiang et al., 2023 ). Williams, Rana and Dwivedi ( 2014 ) found that the original article (Venkatesh et al., 2003 ) was cited 5000 times. Nevertheless, they still found that regardless of a plethora of citations, little research has been conducted on UTAUT (Williams et al., 2014 ). This observation highlights the need for more empirical studies that apply UTAUT across various contexts, including education (Martinez et al., 2023). Williams et al. ( 2014 ) assert that UTAUT is the collection of technology user acceptance models. They highlight that UTAUT has four main constructs, namely, performance expectancy, effort expectancy, social influence and facilitating conditions. These constructs remain relevant in current research on technology adoption in education, as demonstrated by recent studies (Jiang et al., 2022 ; Robitzsch, 2022 ). The latter researchers underscore that these constructs are direct determinants of behavioural intention and eventual behaviour. Furthermore, Williams et al. ( 2014 ) indicate that these constructs are moderated by age, gender, experience and voluntariness of use. Recent research has continued to explore these moderating factors, with studies like Alshehri et al. ( 2022 ) examining the role of demographic factors in technology adoption in educational settings. Alshehri et al. ( 2022 ) indicate UTAUT outperforms all other eight individual models. This superiority of UTAUT in explaining technology adoption has been supported by recent meta-analyses and comparative studies (Jiang et al., 2023 ). Williams et al. ( 2014 ) also found that most research on UTAUT was conducted in the USA followed by the UK. However, they suggest that UTAUT research should be extended to other fields such as education, among others. This call for broader application of UTAUT has been heeded in recent years, with an increasing number of studies applying the model in diverse educational contexts and cultures (Martinez et al., 2023; Jiang et al., 2022 ). Accordingly, the current study conducted research in an ODeL environment using the UTAUT, which has been used in fewer studies in the field of education and ODeL, particularly in South Africa. This application of UTAUT in a specific educational context addresses the gap identified by Williams et al. ( 2014 ) and contributes to the growing body of research on technology adoption in diverse educational settings (Alshehri et al., 2022 ; Robitzsch, 2022 ). METHODS This study was cross-sectional, employing a survey questionnaire to solicit data from students. Among the tests conducted, this study used SEM to evaluate the hypotheses stated at the beginning of the study. Structural equation modelling (SEM) is a multivariate technique that combines aspects of factor analysis and multiple regression to enable the researcher to simultaneously examine a series of interrelated dependence relationships among measured variables and latent constructs (variates), as well as among several latent constructs (Robitzsch, 2022 ). In addition, SEM is a methodological technique that takes a confirmatory (that is hypothesis-testing) approach to the analysis of a structural theory bearing on some theory (Bryne, 2016 ). Moreover, SEM aims to measure a causal relationship among variables (Veltri, 2020 ). Therefore, it consists of two parts: the measurement model and the structural model. SEM was used to assess the model using the method of maximum likelihood estimation (MLE). Furthermore, multigroup analysis was used to determine whether the model differed by gender, age, experience in using computers and computer access. The measurement model was performed using confirmatory factor analysis (CFA) to assess the construct validity and test the model fit (Jiang et al., 2023 ). Conversely, the structural part was assessed using the SEM technique to test the hypothesised relationships between the independent and dependent variables (Martinez et al., 2023). Once the model was established, multigroup analysis was used to test the moderation effect of age, gender, experience in use of computers and computer access. The findings will be presented in three stages. The first stage is the CFA, the second stage is the SEM and the third stage is the multigroup analysis. The section ends with a summary of the findings (Alshehri et al., 2022 ; Jiang et al., 2022 ). FINDINGS AND DISCUSSION Analysis of the measurement model The study employed confirmatory factor analysis to assess how well the prespecified measurement theory consisting of measured variables and factors actually fit the data. The constructs used in the model were performance expectancy (PE), effort expectancy (EE), attitude (ATT), facilitating conditions (FC), social influence (SI), anxiety (ANX), self-efficacy (SE) and behavioural intention (BI). CFA does not have to distinguish between endogenous and exogeneous although it is necessary when performing the SEM. The CFA was done first by assessing the model fitness and determining the validity of the model using convergent validity and discriminant validity. Model fitness was assessed using several statistics and indices. Goodness of fit indices The model fit was assessed using a number of goodness of fit statistics and indices, namely, absolute fit indices, incremental fit indices and parsimony fit indices. The absolute fit indices are the chi-square ( \(\:{\chi\:}^{2})\) statistic, goodness-of-fit Index (GFI), Root Mean Square Error of Approximation (RMSEA), Root Mean Square Residual (RMR), Standardised Root Mean Residual (SRMR) and Normed Chi-square (CMIN/df). The incremental fit indices used were Normed FIT Index (NFI), Tucker Lewis Index (TLI), Incremental Fit Index (IFI) and Relative Non-centrality Index (RNI). The parsimonious indices were the Adjusted Goodness of Fit Index (AGFI) and the Parsimony Normed Fit Index (PNFI). In terms of the absolute fit index, the chi-square test statistic postulates a good model if it is non-significant, that is, the p-value is more than 05. Hair, et al. ( 2019 :637) indicate that the chi-square test is adversely affected by the size of the sample and the authors advice the use of ratio of the chi-square value to its associated degrees of freedom called CMIN/DF statistic. The threshold of the fit indices was based on the guidelines conceptualised by Hu and Bentler ( 1999 ), Gaskin and Lim ( 2016 ) and Hair, et al. ( 2019 ). The threshold level of some of the fit indices is shown in Table 1 . Table 1 Cut-off criteria for fit indices Measure Terrible Acceptable Excellent CMIN/DF > 5 > 3 > 1 RMSEA > .08 > .06 .10 > .08 < .08 PClose < .01 .05 CFI, GFI, TLI, NFI, IFI < .90 .95 Gaskin and Lim ( 2016 ) indicate that a combination of CFI, which is greater than .95 ( \(\:CFI>.95)\) and SRMR which is less than .08 ( \(\:SRMR<.08)\) is a good combination and this can be further solidified by RMSEA, which is less than .06 ( \(\:RMSEA<.06).\) However, Hair et al. ( 2019 :641) assert that using three or four indices provides adequate evidence of model fit and the process that a researcher should report at least one incremental index and one absolute index in addition to the \(\:{\chi\:}^{2}\) value and the associated degrees of freedom. In this case, model fitness will be determined by providing the following: the \(\:{\chi\:}^{2}\) value and the associated degrees of freedom. one absolute fit index (i.e., GFI, RMSEA, or SRMR). one incremental fit index (i.e., CFI or TLI). one goodness of fit index ((GFI, CFI, TLI, etc.). one badness of fit index (RMSEA, SRMR and etc.). Therefore, reporting CMIN/df, CFI and RMSEA or SRMR will provide sufficient unique information to evaluate a model. Confirmatory factor analysis (CFA) model Similar to studies by Sumak et al.’s ( 2010 ) and Abbad ( 2021 ), the CFA was conducted in this study to test the reliability and validity of the measurement model and structural model. The CFA is shown in Fig. 2 . Items with factor loadings of at least .5 were taken. According to Hair et al. ( 2019 ), the factor loadings should be .5 or ideally greater than .7. The model in Fig. 2 yielded the fit measures shown in Table 2 . Table 2 Model fit measures for CFA model Measure Estimate Threshold Interpretation CMIN 276.171 -- -- DF 202.000 -- -- CMIN/DF 1.367 Between 1 and 3 Excellent CFI .978 > .95 Excellent SRMR .042 < .08 Excellent RMSEA .041 .05 Excellent TLI .973 > .95 Excellent NFI .925 > .95 Acceptable IFI .979 > .95 Excellent GFI .903 > .95 Acceptable Looking at Table 3 , the value of the chi-squared ( \(\:{\chi\:}^{2}\) ) was 276.171 with 202 degrees of freedom, resulting in a CMIN/DF of 1.367, which is regarded as excellent. A CFI of .978 was obtained, SRMR and RMSEA were .042 and .041, respectively and all were excellent. The GFI was .903, which is acceptable and the incremental indices were TLI (.973), NFI (.925) and IFI (.979) and were excellent, acceptable and excellent respectively. Therefore, the model is a very good fit. Construct validity Construct validity encapsulates convergent validity, discriminant validity and nomological validity. Convergent validity is the extent to which a measure correlates positively with alternative measures of the same construct and discriminant validity is the extent to which a construct is truly distinct from other constructs by empirical standard (Hair, Hult, Ringle & Sarstedt, 2017:113–114). On the contrary, nomological validity is the extent to which the factors are correlated. Therefore, nomological validity will be assessed by examining correlations. The model validity is discussed in the next subsections. Convergent validity Convergent validity is assessed using Average Variance Extracted (AVE) and construct reliability (CR), and convergent validity is established if AVE is greater than .5 ( \(\:AVE>.5\) ) and the CR is greater than AVE ( \(\:CR>AVE)\) . The information is shown in Table 3 . Table 3 Standardised loadings, construct reliability and AVE Construct items Std. loading Construct reliability AVE Performance expectancy (PE) .924 .751 B1.3 .871 B1.4 .861 B1.5 .888 B1.6 .847 Effort expectancy (EE) .891 .674 B2.2 .698 B2.3 .829 B2.4 .867 B2.5 .876 Attitude (ATT) .797 .663 B3.4 .764 B3.5 .861 Social influence (SI) .891 .803 B4.1 .875 B4.2 .917 Facilitating conditions (FC) .841 .728 B5.1 .769 B5.2 .929 Self-efficacy (SE) .826 .613 B6.2 .750 B6.3 .821 B6.4 .776 Anxiety (ANX) .855 .665 B7.2 .770 B7.3 .947 B7.4 .711 Behavioural intention (BI) .954 .874 C1 .881 C2 .984 C3 .937 Convergent validity was established because all construct reliabilities (CRs) exceed their respective AVEs, and all AVEs exceed .5. Discriminant validity Discriminant validity was measured by comparing the square root of AVEs and inter-construct correlations. It is achieved if the square root of AVE is greater than the inter-construct correlations and the maximum shared variance (MSV) is less than AVE (Fornell & Larcker, 1981 ). Table 4 depicts the matrix with the square root of AVE on the diagonal, the top half of the matrix displays the squared inter-construct correlations (SICs), and the bottom half of the table displays the inter-construct correlations. Table 4 AVE, MSV, Inter-construct correlations and Squared Inter-construct correlations (SICs) Construct AVE MSV 1 2 3 4 5 6 7 8 1. PE .751 .596 .867 .332 .596 .297 .150 .029 .012 .009 2. EE .674 .497 .576*** .821 .497 .054 .356 .000 .073 .010 3. ATT .663 .596 .772*** .705*** .814 .275 .213 .027 .079 .003 4. SI .803 .297 .545*** .232*** .524*** .896 .019 .046 .004 .002 5. FC .728 .356 .387*** .597*** .462*** .137† .853 .121 .082 .000 6. SE .613 .092 .171* − .008 .163† .211*** .110 . 783 .092 .001 7. ANX .665 .092 .109 .270*** .281*** − .062 .286*** − .304*** .816 .003 8. BI .874 .010 − .095 − .101 − .058 − .045 − .006 .033 − .050 .935 † p < .1, * p < .05, ** p < .01, *** p < .001 Discriminant validity was established since the square root of the AVEs (along the diagonals) are all more than the corresponding inter-construct correlations (bottom half of the matrix) and all AVEs are more than the Squared Inter-construct Correlations (SICs) on the top half of the matrix. The structural equation modelling estimated summary statistics The criteria for goodness-of-fit statistics and indices used in the CFA were also applied to assess the path analysis (structural model) in the SEM. The first step was to determine model fitness before doing path analysis. In this case, no items were dropped from the CFA model during SEM fitting. The model fit measures are shown in Table 5 . Table 5 Model fit measures for SEM model Measure Estimate Threshold Interpretation CMIN 276.171 -- -- DF 202.000 -- -- CMIN/DF 1.367 Between 1 and 3 Excellent CFI .978 > .95 Excellent SRMR .042 < .08 Excellent RMSEA .041 .05 Excellent TLI .973 > .95 Excellent NFI .979 > .95 Excellent IFI .925 > .95 Acceptable GFI .903 > .95 Acceptable The fit indices indicated a good fit, with a chi-square value of 276.171 on 202 degrees of freedom, yielding a CMIN/DF of 1.367, which falls between 1 and 3, indicating an excellent fit. The fit indices were SRMR and RMSEA of .042 and .041, respectively, which are low, suggesting a good fit. The incremental fit indices were TLI (.973), NFI (.979) and IFI (.925), which were all greater than the minimum cut-off point of .90, therefore, indicating a good model. TLI and NFI were indicating an excellent fit while IFI was indicating an acceptable fit. The GFI is 0.903, which is acceptable. As per Hair et al.’s ( 2019 ) guidelines, at least three measures suggest a good fit. According to Gaskin and Lim ( 2016 ), a combination of CFI, which is greater than .95 (in this case, CFI=.978), and SRMR, which is less than .08 (SRMR=.041), is a good combination and is solidified by RMSEA, which is less than .06 (RMSEA=.041). The model is a good fit and one can proceed with path analysis as shown in Table 6 . Table 6 Regression weights from the model Hypothesis Standardised Estimate parameter Estimate S.E CR P H 1 BI ← PE − .125 -2.711 3.000 − .904 .366 H 2 BI ← EE − .159 -4.942 4.345 -1.137 .255 H 3 BI ← ATT .139 3.150 4.546 .693 488 H 4 BI ← SI − .032 − .599 1.897 − .316 .752 H 5 BI ← FC .093 2.211 2.386 .927 .354 H 6 BI ← SE .009 .187 1.855 .101 .920 H 7 BI ← ANX − .058 -1.135 1.774 − .640 .522 B1.3 ← PE .871 1.022 .062 16.476 *** B1.4 ← PE .861 .977 .060 16.175 *** B1.5 ← PE .888 .960 .056 17.019 *** B1.6 ← PE .847 1.000 B2.2 ← EE .698 1.000 B2.3 ← EE .829 1.279 .113 11.341 *** B2.4 ← EE .867 1.458 .124 11.791 *** B2.5 ← EE .876 1.371 .115 11.895 *** B3.4 ← ATT .764 1.000 B3.5 ← ATT .861 .981 .081 12.144 *** B4.1 ← SI .875 1.002 .081 12.297 *** B4.2 ← SI .917 1.000 B5.1 ← FC .769 .912 .093 9.769 *** B5.2 ← FC .929 1.000 B6.2 ← SE .750 1.056 .103 10.264 *** B6.3 ← SE .821 1.056 .099 10.691 *** B6.4 ← SE .776 1.000 B7.2 ← ANX .770 1.151 .106 10.866 *** B7.3 ← ANX .947 1.319 .114 11.526 *** B7.4 ← ANX .711 1.000 C1 ← BI .881 1.000 C2 ← BI .984 1.213 .050 24.369 *** C3 ← BI .937 1.166 .053 22.198 *** *** p<.001 All the paths had the hypothesis \(\:{H}_{0}:\:\beta\:=0\) not being rejected. This means that performance expectancy, effort expectancy, attitude, social influence, facilitating condition, self-efficacy and anxiety had no impact on the behavioural intention of students to use LMS. The hypotheses are discussed in detail as follows: H1: Performance expectancy has a positive influence on behavioural intention to use LMS. The results showed that performance expectancy had no impact on behavioural intention. This contrasts with Venkatesh et al. ’s (2003) findings, in which performance expectancy influenced behavioural intention. In addition, the estimate had a negative effect, contradicting the theory ( \(\:\beta\:=\:-2.711,\:p=.\:366)\) . Therefore, high values in performance expectation seem to be associated with low values in behavioural intention to use LMS, although insignificant. H2: Effort expectancy has a positive influence on behavioural intention to use LMS. Effort expectancy did not influence behavioural intention. A coefficient of \(\:\beta\:=-4.942\) with a p-value of .255 and the null hypothesis ( \(\:{H}_{0}:\:\beta\:=0\) ) of no impact was not rejected at the 5% level of significance. Effort expectancy did not influence behavioural intention to use an LMS. H3: Attitude has a positive influence on behavioural intention to use LMS. There was no association between attitude and behavioural intention ( \(\:\beta\:=\:3.150,\:p=.488)\:\) at the 5% level of significance. Although the effect was positive, it was not significant. H4: Social influence has a positive influence on behavioural intention to use LMS. Social influence had no effect on behavioural intention ( \(\:\beta\:=-.599,\:p=.\:752).\) Although insignificant, the coefficient was contrary to the literature, as it was negative. H5: Facilitating conditions have a positive influence on behavioural intention to use LMS. The results revealed that facilitating conditions had no influence on behavioural intention ( \(\:\beta\:=\:2.211,\:p=.\:354)\) . The hypothesis of no influence, that is, \(\:{H}_{0}:\:\beta\:=0\) was not rejected at the 5% level ofsignificance. \(\:\:\) H6: Self-efficacy has a positive influence on behavioural intention to use LMS. The hypothesis on whether self-efficacy had a positive influence on behavioural intention to use LMS was not rejected ( \(\:\beta\:=.187,\:p=.\:920)\) . Although the effect was positive, it was not significant. H7: Anxiety has an impact on behavioural intention to use LMS. Anxiety had no influence on behavioural intention ( \(\:\beta\:=\:-1.135,\:p.522)\) . The effect was negative but not significant. Summarised results of hypotheses tested and confirmed relationships The summary of the hypothesis results is presented in Table 7 . Table 7 Summarised results of hypotheses tested and confirmed relationships Hypothesis Construct Path Construct Hypothesis Result H 1 BI <--- PE Fail to reject null hypothesis at \(\:\varvec{\alpha\:}\:=\:.05\) H 2 BI <--- EE Fail to reject null hypothesis at \(\:\varvec{\alpha\:}\:=\:.05\) H 3 BI <--- ATT Fail to reject null hypothesis at \(\:\varvec{\alpha\:}\:=\:.05\) H 4 BI <--- SI Fail to reject null hypothesis at \(\:\varvec{\alpha\:}\:=\:.05\) H 5 BI <--- FC Fail to reject null hypothesis at \(\:\varvec{\alpha\:}\:=\:.05\) H 6 BI <--- SE Fail to reject null hypothesis at \(\:\varvec{\alpha\:}\:=\:.05\) H 7 BI <--- ANX Fail to reject null hypothesis at \(\:\varvec{\alpha\:}\:=\:.05\) The result of the hypothesis testing showed that no construct had an impact on behavioural intention. The summarised decision of whether the model was supported is shown in Table 8 . Table 8 The result of the hypothesis testing Hypothesis Result H1: Performance expectancy has a positive influence on behavioural intention to use LMS Not Supported H2: Effort expectancy has a positive influence on behavioural intention to use LMS Not supported H3: Attitude has a positive influence on behavioural intention to use LMS Not supported H4: Social influence has a positive influence on behavioural intention to use LMS. Not supported H5: Facilitating conditions have a positive influence on behavioural intention to use LMS Not supported H6: Self-efficacy has a positive influence on behavioural intention to use LMS. Not supported H7: Anxiety has an impact on behavioural intention to use LMS Not Supported No construct had an effect on behavioural intention. Since behavioural intention was measured in months, this may lead to high variability owing to the year of the study. A final-year student has a lower intention of using the LMS than a first-year student. CONCLUSION, IMPLICATIONS AND SUGGESTIONS This study used SEM to evaluate UTAUT constructs and found that all constructs had virtually no significant relationship with behavioural intention; although some had a positive effect, it was not significant. These findings are unique to the South African ODeL context. They contradict the findings of Venkatesh et al. ( 2003 ), who reported a correlation between UTAUT constructs and behavioural intention. Many other studies (Abbad, 2021 ; Attiquayefio & Addo, 2014 ; Chen & Hwang, 2019 ; Dulle & Minishi-Majanja, 2012 ; Dwivedi et al., 2019 ) conducted elsewhere in the world and within the African context found that there is correlation between performance expectancy, effort expectancy, social influence and facilitating conditions and behavioural intention and actual use of technology, in this case, the learning management system. The findings of this study are a clear testament to the importance of context in research on online learning. Issues such as access to technological devices and the facilitating conditions taken for granted in the Global North may look different when studied in the Global South. Therefore, research findings that may be applicable elsewhere might be the opposite in the African ODeL context. There should be more studies on UTAUT in the African ODeL context whose findings will help stakeholders, including policy makers, decision-makers, management, online learning instructional designers, and lecturers, to ensure that e-learning is implemented efficiently (Maphosa et al., 2023). Facilitating conditions such as the provision of technological infrastructure, effort expectancy and performance expectancy are key in enabling the successful implementation of e-learning in the African context. The digital divide needs to be addressed so that students, regardless of their socio-economic status, can fully participate in e-learning (Maphosa et al., 2023). The findings of this study are similar to those of Abbad ( 2021 ), who argues that social influence did not have an effect owing to the students being born in a world proliferated with technology and therefore, need no one per se to influence them to use technology, as the environment itself dictates them to adopt using the LMS. SEM was used to explore relationships of UTAUT constructs and behavioural intention. To understand perceptions about using the LMS (sakai), the UTAUT research model and hypothesised relationships between UTAUT constructs were empirically tested using SEM. Moreover, SEM showed that performance expectancy, effort expectancy, attitude, facilitating condition, anxiety, and social influence did not affect students' behavioural intention to use LMS. However, the relationship may be improved by age, computer experience and computer literacy if they act as moderators. More importantly, decision-makers need to ensure that the LMS is efficient and effective to support students’ successful adoption and continued use of the system. This study on the use of UTAUT in the Global South clearly attests that the adoption of LMS should not be uncritical but should be interrogated and robustly piloted to ensure they are fit for purpose in a developing-world context. Declarations Both the College of Human Sciences Research Ethics Committee of the University of South Africa [Ref. 2016-CHS-021] and the University of Leicester [University Ethics Sub-Committee for Sociology; Politics and IR; Lifelong Learning; Criminology; Economics and the School of Education: Ref. 7680-jmc79-education] gave ethical clearance before I started collecting data. Students also signed consent forms and a participant information sheet prior to participating in the online Lime Survey. 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SEM 6(1), pp. 1–55. https://doi.org/10.1080/10705519909540118 Jiang H, Jiang X, Sun P, Li X (2022) Comparing Bayesian and Maximum Likelihood Methods in Structural Equation Modelling of University Student Satisfaction: An Empirical Analysis. Educ Sci 12(3):207. https://doi.org/10.1155/2022/3665669 Jiang Y, Xiao L, Jalees T, Naqvi MH, Zaman SI (2023) Multilevel SEM with random slopes in discrete data using the pairwise maximum likelihood. Struct Equation Modeling: Multidisciplinary J 30(1):118–131. https://doi.org/10.1111/bmsp.12294 Maphalala MC, Adigun OT (2021) Academics' Experience of Implementing E-Learning in a South African Higher Education Institution. Int J High Educ 10(1):1–13. https://eric.ed.gov/?id=EJ1285614 Martínez-Falcó J, Sánchez-García E, Millan-Tudela LA, Marco-Lajara B (2023) The role of green agriculture and green supply chain management in the green intellectual capital–Sustainable performance relationship: A structural equation modeling analysis applied to the Spanish wine industry. Agriculture 13(2):425. https://doi.org/10.3390/agriculture13020425 Or CCP (2023) Towards an integrated model: Task-Technology fit in Unified Theory of Acceptance and Use of Technology 2 in education contexts. J Appl Learn Teach 6(1):151–163. https://doi.org/10.37074/jalt.2023.6.1.8 Robitzsch A (2022) Comparing the robustness of the structural after measurement (SAM) approach to structural equation modeling (SEM) against local model misspecifications with alternative estimation approaches. Stats 5(3):631–672 Sumak B, Polancic G, Hericko M (2010) An empirical study of virtual learning environment adoption using UTAUT. IEEE (2010) 17–22 23. https://doi.org/10.1109/eLmL.2010.11 Veltri GA (2020) Digital social research. Polity, Cambridge Venkatesh V, Morris MG, Davis JB, Morris FB (2003) User acceptance of information technology: Toward a unified view. MIS Q 27(3):425–478. https://doi.org/10.2307/30036540 . /September 2003 Williams MD, Rana NP, Dwivedi YK (2014) The unified theory of acceptance and use of technology (UTAUT): A literature review. J Enterp Inform Manage 26(3):443–448. https://doi.org/10.1108/JEIM-09-2014-0088 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9356688","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619640043,"identity":"e5824ca9-dd2c-47bb-a098-6e66b00ad348","order_by":0,"name":"Jack","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYDACCQZmIMkmx8DA2ECaFmOStTAkEquegYF/dvNhY94cvvQNt5ubP/xgsJNn4D/8AL8ld44lJ/NuY8vdcOdgm2QPQ7JhA8MxA7xaDCRyjA/ngrTcSGxj4GFgTgD6iTgt6QY3Eps//mGoT2BgZv9AUEsyUEsCUEuDNA/D4QQGNh78tkjcSEs2/ruNzXAm0GHSMgbHDdt4eArwauGfkXxYcua2Y/J8N9Iff3xTUS3Pz398A14tUHAM5k5grBKjHghqiFQ3CkbBKBgFIxIAAO/YQEepnJvNAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0005-9996-9632","institution":"University of South Africa","correspondingAuthor":true,"prefix":"","firstName":"","middleName":"","lastName":"Jack","suffix":""}],"badges":[],"createdAt":"2026-04-08 12:04:16","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9356688/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9356688/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106641221,"identity":"1ead9a5a-1726-415a-9142-ef4d32ae980c","added_by":"auto","created_at":"2026-04-10 18:18:16","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":77446,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHypothesised UTAUT model (Chokwe, 2022)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9356688/v1/e376824d7fe8729cb05bc89b.jpeg"},{"id":106641222,"identity":"68cea676-4d9a-4cfd-afe6-f04816005067","added_by":"auto","created_at":"2026-04-10 18:18:16","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":720090,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConfirmatory factor analysis of the model (Chokwe, 2022)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9356688/v1/bb11df46d4d69b7675f110bb.jpeg"},{"id":106727167,"identity":"821dc978-72fc-4c7b-86d3-3162bd40de28","added_by":"auto","created_at":"2026-04-12 18:38:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2045743,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9356688/v1/f8a9e66f-53b8-4ea3-970f-5427ba108b12.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eUTAUT Application in South African ODeL Context: LMS Adoption among First-Year Students\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe article reports on the findings of a doctoral study (Chokwe, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) that investigated the adoption of the learning management system (LMS) in the South African open and distance e-learning (ODeL) context. First-year students who were enrolled in the Department of English Studies participated in the study. The Unified Theory of Acceptance and Use of Technology (UTAUT) underpinned the study (Venkatesh et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Structural equation modelling was used to analyse the data and evaluate the hypotheses, and it was found that none of the UTAUT constructs were related to behavioural intention. However, age, gender, experience and computer access were moderating variables (Chokwe, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bayaga, A., \u0026amp; du Plessis, A., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although UTAUT was mainly used in studies conducted in the Global North context, it was, however, rarely applied in the Global South context, particularly in the South African ODeL context (Mabunda \u0026amp; Rambe, 2020). As a result, this study sought to fill this lacuna in the body of knowledge. Accordingly, the findings of the study contribute to the body of knowledge on the use of UTAUT in the South African ODeL context and similar contexts in developing countries (Chokwe, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mabunda \u0026amp; Rambe, 2020). Furthermore, the findings indicate that UTAUT constructs do not correlate with behavioural intention of using the learning management system (LMS) for teaching and learning (Chokwe, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bayaga, A., \u0026amp; du Plessis, A. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, UTAUT enables leaders and stakeholders of ODeL institutions to understand factors orchestrating the acceptance and usage of LMS for teaching and learning (Chokwe, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mabunda \u0026amp; Rambe, 2020; Bayaga, A., \u0026amp; du Plessis, A., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e"},{"header":"LITERATURE REVIEW","content":"\u003cp\u003eTo understand students' perceptions about using Moodle, Sumak, Polancic and Hericko (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) used the UTAUT research model and hypothesised relationships between UTAUT constructs, empirically testing them using the structural equation modelling (SEM) approach. They argue that UTAUT presents a more complete picture of technology acceptance than any other individual model is able to do. This comprehensive approach allows for a more nuanced understanding of the factors influencing technology adoption in educational settings (Alshehri et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, Sumak et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) report that performance expectancy and social influence positively affect attitudes towards Moodle use. This finding aligns with recent research by Jiang et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who also found that performance expectancy plays a crucial role in technology adoption in educational contexts. In contrast, Sumak et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) found that effort expectancy does not influence students' attitudes towards using Moodle. This result challenges some assumptions about the importance of ease of use in technology adoption and highlights the complexity of factors influencing student behaviour (Martinez et al., 2023).\u003c/p\u003e \u003cp\u003eHowever, Martinez et al. (2023) argue that there was no scientific proof that performance expectancy, effort expectancy, and attitudes towards using the e-learning system correlated with students' behavioural intention. This finding underscores the need for further research to better understand the relationship between these constructs and behavioural intentions in e-learning environments (Robitzsch, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). More importantly, Sumak et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) argue that students do not use LMS because it is easy to use but because they find it useful for their studies. This emphasis on usefulness over ease of use has been corroborated by recent studies, such as Jiang et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which found that perceived usefulness is a stronger predictor of technology adoption than perceived ease of use in educational settings.\u003c/p\u003e \u003cp\u003eThe current study also used SEM as an inferential statistical technique, following the trend in recent research on technology adoption in education (Alshehri et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Williams, Rana and Dwivedi (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found that the original article (Venkatesh et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) was cited 5000 times. Nevertheless, they still found that regardless of a plethora of citations, little research has been conducted on UTAUT (Williams et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This observation highlights the need for more empirical studies that apply UTAUT across various contexts, including education (Martinez et al., 2023).\u003c/p\u003e \u003cp\u003eWilliams et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) assert that UTAUT is the collection of technology user acceptance models. They highlight that UTAUT has four main constructs, namely, performance expectancy, effort expectancy, social influence and facilitating conditions. These constructs remain relevant in current research on technology adoption in education, as demonstrated by recent studies (Jiang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Robitzsch, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The latter researchers underscore that these constructs are direct determinants of behavioural intention and eventual behaviour. Furthermore, Williams et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) indicate that these constructs are moderated by age, gender, experience and voluntariness of use. Recent research has continued to explore these moderating factors, with studies like Alshehri et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) examining the role of demographic factors in technology adoption in educational settings.\u003c/p\u003e \u003cp\u003eAlshehri et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) indicate UTAUT outperforms all other eight individual models. This superiority of UTAUT in explaining technology adoption has been supported by recent meta-analyses and comparative studies (Jiang et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Williams et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) also found that most research on UTAUT was conducted in the USA followed by the UK. However, they suggest that UTAUT research should be extended to other fields such as education, among others. This call for broader application of UTAUT has been heeded in recent years, with an increasing number of studies applying the model in diverse educational contexts and cultures (Martinez et al., 2023; Jiang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccordingly, the current study conducted research in an ODeL environment using the UTAUT, which has been used in fewer studies in the field of education and ODeL, particularly in South Africa. This application of UTAUT in a specific educational context addresses the gap identified by Williams et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and contributes to the growing body of research on technology adoption in diverse educational settings (Alshehri et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Robitzsch, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e "},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003cp\u003eThis study was cross-sectional, employing a survey questionnaire to solicit data from students. Among the tests conducted, this study used SEM to evaluate the hypotheses stated at the beginning of the study. Structural equation modelling (SEM) is a multivariate technique that combines aspects of factor analysis and multiple regression to enable the researcher to simultaneously examine a series of interrelated dependence relationships among measured variables and latent constructs (variates), as well as among several latent constructs (Robitzsch, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, SEM is a methodological technique that takes a confirmatory (that is hypothesis-testing) approach to the analysis of a structural theory bearing on some theory (Bryne, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Moreover, SEM aims to measure a causal relationship among variables (Veltri, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, it consists of two parts: the measurement model and the structural model.\u003c/p\u003e \u003cp\u003eSEM was used to assess the model using the method of maximum likelihood estimation (MLE). Furthermore, multigroup analysis was used to determine whether the model differed by gender, age, experience in using computers and computer access. The measurement model was performed using confirmatory factor analysis (CFA) to assess the construct validity and test the model fit (Jiang et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Conversely, the structural part was assessed using the SEM technique to test the hypothesised relationships between the independent and dependent variables (Martinez et al., 2023). Once the model was established, multigroup analysis was used to test the moderation effect of age, gender, experience in use of computers and computer access. The findings will be presented in three stages. The first stage is the CFA, the second stage is the SEM and the third stage is the multigroup analysis. The section ends with a summary of the findings (Alshehri et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"FINDINGS AND DISCUSSION","content":"\u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eAnalysis of the measurement model\u003c/h3\u003e\n\u003cp\u003eThe study employed confirmatory factor analysis to assess how well the prespecified measurement theory consisting of measured variables and factors actually fit the data. The constructs used in the model were performance expectancy (PE), effort expectancy (EE), attitude (ATT), facilitating conditions (FC), social influence (SI), anxiety (ANX), self-efficacy (SE) and behavioural intention (BI).\u003c/p\u003e \u003cp\u003eCFA does not have to distinguish between endogenous and exogeneous although it is necessary when performing the SEM. The CFA was done first by assessing the model fitness and determining the validity of the model using convergent validity and discriminant validity. Model fitness was assessed using several statistics and indices.\u003c/p\u003e\n\u003ch3\u003eGoodness of fit indices\u003c/h3\u003e\n\u003cp\u003eThe model fit was assessed using a number of goodness of fit statistics and indices, namely, absolute fit indices, incremental fit indices and parsimony fit indices. The absolute fit indices are the chi-square (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2})\\)\u003c/span\u003e\u003c/span\u003e statistic, goodness-of-fit Index (GFI), Root Mean Square Error of Approximation (RMSEA), Root Mean Square Residual (RMR), Standardised Root Mean Residual (SRMR) and Normed Chi-square (CMIN/df). The incremental fit indices used were Normed FIT Index (NFI), Tucker Lewis Index (TLI), Incremental Fit Index (IFI) and Relative Non-centrality Index (RNI). The parsimonious indices were the Adjusted Goodness of Fit Index (AGFI) and the Parsimony Normed Fit Index (PNFI).\u003c/p\u003e \u003cp\u003eIn terms of the absolute fit index, the chi-square test statistic postulates a good model if it is non-significant, that is, the p-value is more than 05. Hair, et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e:637) indicate that the chi-square test is adversely affected by the size of the sample and the authors advice the use of ratio of the chi-square value to its associated degrees of freedom called CMIN/DF statistic.\u003c/p\u003e \u003cp\u003eThe threshold of the fit indices was based on the guidelines conceptualised by Hu and Bentler (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), Gaskin and Lim (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Hair, et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The threshold level of some of the fit indices is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCut-off criteria for fit indices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTerrible\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcceptable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMIN/DF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePClose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFI, GFI, TLI, NFI, IFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eGaskin and Lim (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) indicate that a combination of CFI, which is greater than .95 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:CFI\u0026gt;.95)\\)\u003c/span\u003e\u003c/span\u003e and SRMR which is less than .08 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:SRMR\u0026lt;.08)\\)\u003c/span\u003e\u003c/span\u003e is a good combination and this can be further solidified by RMSEA, which is less than .06 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:RMSEA\u0026lt;.06).\\)\u003c/span\u003e\u003c/span\u003e However, Hair et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e:641) assert that using three or four indices provides adequate evidence of model fit and the process that a researcher should report at least one incremental index and one absolute index in addition to the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e value and the associated degrees of freedom. In this case, model fitness will be determined by providing the following:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ethe \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e value and the associated degrees of freedom.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eone absolute fit index (i.e., GFI, RMSEA, or SRMR).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eone incremental fit index (i.e., CFI or TLI).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eone goodness of fit index ((GFI, CFI, TLI, etc.).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eone badness of fit index (RMSEA, SRMR and etc.).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTherefore, reporting CMIN/df, CFI and RMSEA or SRMR will provide sufficient unique information to evaluate a model.\u003c/p\u003e\n\u003ch3\u003eConfirmatory factor analysis (CFA) model\u003c/h3\u003e\n\u003cp\u003eSimilar to studies by Sumak et al.\u0026rsquo;s (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Abbad (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the CFA was conducted in this study to test the reliability and validity of the measurement model and structural model. The CFA is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Items with factor loadings of at least .5 were taken. According to Hair et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), the factor loadings should be .5 or ideally greater than .7.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe model in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e yielded the fit measures shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel fit measures for CFA model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThreshold\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e276.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e202.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMIN/DF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBetween 1 and 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePClose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAcceptable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAcceptable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLooking at Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the value of the chi-squared (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e) was 276.171 with 202 degrees of freedom, resulting in a CMIN/DF of 1.367, which is regarded as excellent. A CFI of .978 was obtained, SRMR and RMSEA were .042 and .041, respectively and all were excellent. The GFI was .903, which is acceptable and the incremental indices were TLI (.973), NFI (.925) and IFI (.979) and were excellent, acceptable and excellent respectively. Therefore, the model is a very good fit.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eConstruct validity\u003c/h2\u003e \u003cp\u003eConstruct validity encapsulates convergent validity, discriminant validity and nomological validity. Convergent validity is the extent to which a measure correlates positively with alternative measures of the same construct and discriminant validity is the extent to which a construct is truly distinct from other constructs by empirical standard (Hair, Hult, Ringle \u0026amp; Sarstedt, 2017:113\u0026ndash;114). On the contrary, nomological validity is the extent to which the factors are correlated. Therefore, nomological validity will be assessed by examining correlations. The model validity is discussed in the next subsections.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConvergent validity\u003c/h3\u003e\n\u003cp\u003eConvergent validity is assessed using Average Variance Extracted (AVE) and construct reliability (CR), and convergent validity is established if AVE is greater than .5 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:AVE\u0026gt;.5\\)\u003c/span\u003e\u003c/span\u003e) and the CR is greater than AVE (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:CR\u0026gt;AVE)\\)\u003c/span\u003e\u003c/span\u003e. The information is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStandardised loadings, construct reliability and AVE\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStd. loading\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConstruct reliability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePerformance expectancy (PE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e.751\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEffort expectancy (EE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e.674\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.829\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.876\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAttitude (ATT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSocial influence (SI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.803\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.875\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFacilitating conditions (FC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.728\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSelf-efficacy (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e.613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAnxiety (ANX)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e.665\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.770\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.947\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBehavioural intention (BI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e.874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.984\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.937\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eConvergent validity was established because all construct reliabilities (CRs) exceed their respective AVEs, and all AVEs exceed .5.\u003c/p\u003e\n\u003ch3\u003eDiscriminant validity\u003c/h3\u003e\n\u003cp\u003eDiscriminant validity was measured by comparing the square root of AVEs and inter-construct correlations. It is achieved if the square root of AVE is greater than the inter-construct correlations and the maximum shared variance (MSV) is less than AVE (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e depicts the matrix with the square root of AVE on the diagonal, the top half of the matrix displays the squared inter-construct correlations (SICs), and the bottom half of the table displays the inter-construct correlations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAVE, MSV, Inter-construct correlations and Squared Inter-construct correlations (SICs)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMSV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. PE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.867\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. EE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.576***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.821\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. ATT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.772***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.705***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e.814\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. SI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.545***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.232***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.524***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e.896\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. FC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.387***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.597***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.462***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.137\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e.853\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. SE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.171*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.163\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.211***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.\u003cb\u003e783\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. ANX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.270***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.281***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.286***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.304***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e.816\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8. BI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.935\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e\u0026dagger; p \u0026lt; .1, \u003csup\u003e*\u003c/sup\u003ep \u0026lt; .05, \u003csup\u003e**\u003c/sup\u003e p \u0026lt; .01, \u003csup\u003e***\u003c/sup\u003e p \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDiscriminant validity was established since the square root of the AVEs (along the diagonals) are all more than the corresponding inter-construct correlations (bottom half of the matrix) and all AVEs are more than the Squared Inter-construct Correlations (SICs) on the top half of the matrix.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe structural equation modelling estimated summary statistics\u003c/h2\u003e \u003cp\u003eThe criteria for goodness-of-fit statistics and indices used in the CFA were also applied to assess the path analysis (structural model) in the SEM. The first step was to determine model fitness before doing path analysis. In this case, no items were dropped from the CFA model during SEM fitting. The model fit measures are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel fit measures for SEM model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThreshold\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e276.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMIN/DF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBetween 1 and 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePClose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAcceptable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAcceptable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe fit indices indicated a good fit, with a chi-square value of 276.171 on 202 degrees of freedom, yielding a CMIN/DF of 1.367, which falls between 1 and 3, indicating an excellent fit. The fit indices were SRMR and RMSEA of .042 and .041, respectively, which are low, suggesting a good fit. The incremental fit indices were TLI (.973), NFI (.979) and IFI (.925), which were all greater than the minimum cut-off point of .90, therefore, indicating a good model. TLI and NFI were indicating an excellent fit while IFI was indicating an acceptable fit. The GFI is 0.903, which is acceptable. As per Hair et al.\u0026rsquo;s (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) guidelines, at least three measures suggest a good fit. According to Gaskin and Lim (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), a combination of CFI, which is greater than .95 (in this case, CFI=.978), and SRMR, which is less than .08 (SRMR=.041), is a good combination and is solidified by RMSEA, which is less than .06 (RMSEA=.041). The model is a good fit and one can proceed with path analysis as shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression weights from the model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandardised Estimate parameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI \u0026larr; PE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI \u0026larr; EE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI \u0026larr; ATT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e488\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI \u0026larr; SI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI \u0026larr; FC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI \u0026larr; SE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.920\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI \u0026larr; ANX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB1.3 \u0026larr; PE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB1.4 \u0026larr; PE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB1.5 \u0026larr; PE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB1.6 \u0026larr; PE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB2.2 \u0026larr; EE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB2.3 \u0026larr; EE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB2.4 \u0026larr; EE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB2.5 \u0026larr; EE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB3.4 \u0026larr; ATT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB3.5 \u0026larr; ATT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB4.1 \u0026larr; SI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB4.2 \u0026larr; SI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB5.1 \u0026larr; FC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB5.2 \u0026larr; FC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB6.2 \u0026larr; SE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB6.3 \u0026larr; SE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB6.4 \u0026larr; SE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB7.2 \u0026larr; ANX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB7.3 \u0026larr; ANX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB7.4 \u0026larr; ANX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC1 \u0026larr; BI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC2 \u0026larr; BI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC3 \u0026larr; BI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*** p\u0026lt;.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAll the paths had the hypothesis \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{H}_{0}:\\:\\beta\\:=0\\)\u003c/span\u003e\u003c/span\u003e not being rejected. This means that performance expectancy, effort expectancy, attitude, social influence, facilitating condition, self-efficacy and anxiety had no impact on the behavioural intention of students to use LMS. The hypotheses are discussed in detail as follows:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH1: Performance expectancy has a positive influence on behavioural intention to use LMS.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe results showed that performance expectancy had no impact on behavioural intention. This contrasts with Venkatesh et al. \u0026rsquo;s (2003) findings, in which performance expectancy influenced behavioural intention. In addition, the estimate had a negative effect, contradicting the theory \u003csup\u003e(\u003c/sup\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:=\\:-2.711,\\:p=.\\:366)\\)\u003c/span\u003e\u003c/span\u003e. Therefore, high values in performance expectation seem to be associated with low values in behavioural intention to use LMS, although insignificant.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH2: Effort expectancy has a positive influence on behavioural intention to use LMS.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eEffort expectancy did not influence behavioural intention. A coefficient of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:=-4.942\\)\u003c/span\u003e\u003c/span\u003e with a p-value of .255 and the null hypothesis (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{H}_{0}:\\:\\beta\\:=0\\)\u003c/span\u003e\u003c/span\u003e) of no impact was not rejected at the 5% level of significance. Effort expectancy did not influence behavioural intention to use an LMS.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH3: Attitude has a positive influence on behavioural intention to use LMS.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThere was no association between attitude and behavioural intention (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:=\\:3.150,\\:p=.488)\\:\\)\u003c/span\u003e\u003c/span\u003eat the 5% level of significance. Although the effect was positive, it was not significant.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH4: Social influence has a positive influence on behavioural intention to use LMS.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSocial influence had no effect on behavioural intention (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:=-.599,\\:p=.\\:752).\\)\u003c/span\u003e\u003c/span\u003e Although insignificant, the coefficient was contrary to the literature, as it was negative.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH5: Facilitating conditions have a positive influence on behavioural intention to use LMS.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe results revealed that facilitating conditions had no influence on behavioural intention (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:=\\:2.211,\\:p=.\\:354)\\)\u003c/span\u003e\u003c/span\u003e. The hypothesis of no influence, that is, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{H}_{0}:\\:\\beta\\:=0\\)\u003c/span\u003e\u003c/span\u003e was not rejected at the 5% level ofsignificance.\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eH6: Self-efficacy has a positive influence on behavioural intention to use LMS.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe hypothesis on whether self-efficacy had a positive influence on behavioural intention to use LMS was not rejected (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:=.187,\\:p=.\\:920)\\)\u003c/span\u003e\u003c/span\u003e. Although the effect was positive, it was not significant.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH7: Anxiety has an impact on behavioural intention to use LMS.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAnxiety had no influence on behavioural intention (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:=\\:-1.135,\\:p.522)\\)\u003c/span\u003e\u003c/span\u003e. The effect was negative but not significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSummarised results of hypotheses tested and confirmed relationships\u003c/h2\u003e \u003cp\u003eThe summary of the hypothesis results is presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummarised results of hypotheses tested and confirmed relationships\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHypothesis Result\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFail to reject null hypothesis at \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\alpha\\:}\\:=\\:.05\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFail to reject null hypothesis at \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\alpha\\:}\\:=\\:.05\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFail to reject null hypothesis at \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\alpha\\:}\\:=\\:.05\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFail to reject null hypothesis at \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\alpha\\:}\\:=\\:.05\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFail to reject null hypothesis at \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\alpha\\:}\\:=\\:.05\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFail to reject null hypothesis at \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\alpha\\:}\\:=\\:.05\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eANX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFail to reject null hypothesis at \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\alpha\\:}\\:=\\:.05\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe result of the hypothesis testing showed that no construct had an impact on behavioural intention. The summarised decision of whether the model was supported is shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe result of the hypothesis testing\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResult\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eH1: Performance expectancy has a positive influence on behavioural intention to use LMS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot Supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eH2: Effort expectancy has a positive influence on behavioural intention to use LMS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eH3: Attitude has a positive influence on behavioural intention to use LMS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eH4: Social influence has a positive influence on behavioural intention to use LMS.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eH5: Facilitating conditions have a positive influence on behavioural intention to use LMS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eH6: Self-efficacy has a positive influence on behavioural intention to use LMS.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eH7: Anxiety has an impact on behavioural intention to use LMS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot Supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNo construct had an effect on behavioural intention. Since behavioural intention was measured in months, this may lead to high variability owing to the year of the study. A final-year student has a lower intention of using the LMS than a first-year student.\u003c/p\u003e \u003c/div\u003e "},{"header":"CONCLUSION, IMPLICATIONS AND SUGGESTIONS","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003cp\u003eThis study used SEM to evaluate UTAUT constructs and found that all constructs had virtually no significant relationship with behavioural intention; although some had a positive effect, it was not significant. These findings are unique to the South African ODeL context. They contradict the findings of Venkatesh et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), who reported a correlation between UTAUT constructs and behavioural intention. Many other studies (Abbad, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Attiquayefio \u0026amp; Addo, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Chen \u0026amp; Hwang, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dulle \u0026amp; Minishi-Majanja, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Dwivedi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) conducted elsewhere in the world and within the African context found that there is correlation between performance expectancy, effort expectancy, social influence and facilitating conditions and behavioural intention and actual use of technology, in this case, the learning management system.\u003c/p\u003e \u003cp\u003eThe findings of this study are a clear testament to the importance of context in research on online learning. Issues such as access to technological devices and the facilitating conditions taken for granted in the Global North may look different when studied in the Global South. Therefore, research findings that may be applicable elsewhere might be the opposite in the African ODeL context. There should be more studies on UTAUT in the African ODeL context whose findings will help stakeholders, including policy makers, decision-makers, management, online learning instructional designers, and lecturers, to ensure that e-learning is implemented efficiently (Maphosa et al., 2023). Facilitating conditions such as the provision of technological infrastructure, effort expectancy and performance expectancy are key in enabling the successful implementation of e-learning in the African context. The digital divide needs to be addressed so that students, regardless of their socio-economic status, can fully participate in e-learning (Maphosa et al., 2023). The findings of this study are similar to those of Abbad (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who argues that social influence did not have an effect owing to the students being born in a world proliferated with technology and therefore, need no one per se to influence them to use technology, as the environment itself dictates them to adopt using the LMS.\u003c/p\u003e \u003cp\u003eSEM was used to explore relationships of UTAUT constructs and behavioural intention. To understand perceptions about using the LMS (sakai), the UTAUT research model and hypothesised relationships between UTAUT constructs were empirically tested using SEM. Moreover, SEM showed that performance expectancy, effort expectancy, attitude, facilitating condition, anxiety, and social influence did not affect students' behavioural intention to use LMS. However, the relationship may be improved by age, computer experience and computer literacy if they act as moderators. More importantly, decision-makers need to ensure that the LMS is efficient and effective to support students\u0026rsquo; successful adoption and continued use of the system. This study on the use of UTAUT in the Global South clearly attests that the adoption of LMS should not be uncritical but should be interrogated and robustly piloted to ensure they are fit for purpose in a developing-world context.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u0026nbsp;Both the College of Human Sciences Research Ethics Committee of the University of South Africa [Ref. 2016-CHS-021] and the University of Leicester [University Ethics Sub-Committee for Sociology; Politics and IR; Lifelong Learning; Criminology; Economics and the School of Education: Ref. 7680-jmc79-education] gave ethical clearance before I started collecting data.\u003c/p\u003e\n\u003cp\u003eStudents also signed consent forms and a participant information sheet prior to participating in the online Lime Survey.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbad MMM (2021) Using the UTAUT model to understand students\u0026rsquo; usage of e-learning systems in developing countries. \u003cem\u003eEducation and Information Technologies\u003c/em\u003e (2021) 26:7205\u0026ndash;7224. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10639-021-10573-5\u003c/span\u003e\u003cspan address=\"10.1007/s10639-021-10573-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlshehri M, Alshehri A, Alarifi A (2022) Dimensionality Analysis of Entrepreneurial Resilience amid the COVID-19 Pandemic: Comparative Models with Confirmatory Factor Analysis and Structural Equation Modeling. \u003cem\u003eSustainability\u003c/em\u003e, 14(13), 7945. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mdpi.com/2227-7390/10/13/2298\u003c/span\u003e\u003cspan address=\"https://www.mdpi.com/2227-7390/10/13/2298\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAttiquayefio SN, Addo H (2014) Using UTAUT to analyse students\u0026rsquo; ICT adoption. 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J Enterp Inform Manage 26(3):443\u0026ndash;448. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/JEIM-09-2014-0088\u003c/span\u003e\u003cspan address=\"10.1108/JEIM-09-2014-0088\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"5e9eeb02-f058-4f90-a201-5dccdd435b72","identifier":"10.13039/501100008227","name":"University of South Africa","awardNumber":"N/A","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of South Africa","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acceptance, e-learning, learning management system, online learning, open distance e-learning, structural equation modelling, Unified Theory of Acceptance and Use of Technology, first year students, adoption, hypothesis","lastPublishedDoi":"10.21203/rs.3.rs-9356688/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9356688/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigated the adoption of learning management systems (LMS) in a South African open and distance e-learning (ODeL) context using the Unified Theory of Acceptance and Use of Technology (UTAUT). First-year students of English modules participated in the study. Structural equation modelling (SEM) was employed to analyse data and test hypotheses. Contrary to previous research, findings revealed that all UTAUT constructs (performance expectancy, effort expectancy, attitude, social influence, facilitating conditions, self-efficacy, and anxiety) had no significant relationship with behavioural intention to use the LMS. However, age, gender, experience, and computer access were identified as moderating variables. These results highlight the importance of context in e-learning research, suggesting that findings from the Global North may not always apply to the Global South context. The study contributes to the limited body of knowledge on UTAUT application in the South African ODeL context and emphasises the need for further research to inform stakeholders in implementing effective e-learning strategies.\u003c/p\u003e","manuscriptTitle":"UTAUT Application in South African ODeL Context: LMS Adoption among First-Year Students","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 18:18:12","doi":"10.21203/rs.3.rs-9356688/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"abca8363-faf9-455f-9612-e1f9c659c680","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65939425,"name":"Educational Philosophy and Theory"}],"tags":[],"updatedAt":"2026-04-10T18:18:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-10 18:18:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9356688","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9356688","identity":"rs-9356688","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}