461 © 2024 Conscientia Beam. All Rights Reserved. Examining the reliability and validity of measuring scales related to informatization and instructional leadership using the PLS-SEM approach Wei Li1 Yoon Fah Lay2+ 1College of International Cooperation, Xi’an International University710077, Shaanxi, China. 1Faculty of Psychology and Education, University Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia. 1Email: 450384173@qq.com 2University Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia; Faculty of Social Sciences and Liberal Arts, UCSI University, Kuala Lumpur; School of Liberal Arts & Sciences, Taylor’s University, Kuala Lumpur, Malaysia. 2Email: layyf@ums.edu.my (+ Corresponding author) ABSTRACT Article History Received: 7 November 2023 Revised: 31 January 2024 Accepted: 20 February 2024 Published: 28 June 2024 Keywords Blended teaching competence Computer self-efficacy Partial least squares structural equation modeling Reliability Teachers’ informatization instructional leadership Validity. This study focuses on six variables that impact teachers' use of technology in their instructional leadership: usage expectancy (UE), social influence (SI), enabling circumstances (FC), behavioural intention (BI), computer self-efficacy (CSE) and blended teaching competency. This study aimed to examine the reliability and validity of modified scales incorporating UE scales including the PE scale, EE scale, SI scale, FC scale, CSE scale, BTC scale, BI scale and TIIL scale. A total of 60 in-service university teachers participated in this research. The PLS-SEM approach was employed to examine the reliability and validity of all scales. Composite reliability (CR) and Cronbach’s alpha determine internal consistency and reliability. Convergent validity was assessed by the outer loading and the average variance extracted (AVE). Assessment of discriminant validity was conducted by the Fornell-Larcker criterion, cross-loadings and Heterotrait-Monotrait Ratio (HTMT). After deleting nine items that were lower than .40, Cronbach’s alpha and CR values were all higher than .70. All scales’ item values fulfilled the criteria of AVE (>.50), Fornell-Larcker criterion, cross- loading and HTMT(<.90). Assessment results indicate that all modified scales have established validity and reliability for in-depth research. This research contributed to the PLS-SEM research technique, examined TIIL's influencing elements in the Chinese environment, enhanced the theoretical model of TIIL and provided useful assistance for the field's advancement. Contribution/Originality: This study offers original insights into teachers’ informatization instructional leadership in China. It extends theoretical knowledge by additionally highlighting two intrinsic individual elements based on UTAUT using PLS-SEM to examine the validity and reliability of scales for further research. 1. INTRODUCTION The present teaching and learning environment is complex with blended teaching and learning. Blended teaching was defined by Graham (2006) as a models that “combines face-to-face instruction with computer mediated instruction”. The COVID-19 has made blended learning a standard teaching modality at universities worldwide (Ritimoni, Prasenjit, & Kandarpa, 2021). This presents an additional obstacle for university teachers who need to acquire computer technology in order to monitor or manage blended learning. Thus, the traditional face-to-face Humanities and Social Sciences Letters 2024 Vol. 12, No. 3, pp. 461-480 ISSN(e): 2312-4318 ISSN(p): 2312-5659 DOI: 10.18488/73.v12i3.3789 © 2024 Conscientia Beam. All Rights Reserved. https://orcid.org/0000-0002-5219-6696 mailto:450384173@qq.com mailto:layyf@ums.edu.my https://www.doi.org/10.18488/73.v12i3.3789 Humanities and Social Sciences Letters, 2024, 12(3): 461-480 462 © 2024 Conscientia Beam. All Rights Reserved. methods of teaching and monitoring university classes have been impacted resulting in a method for implementing university teachers' digitalization. TIIL needs to lead and manage blended teaching through computer technology, devices and a teaching management platform. Furthermore, the Chinese Education Informatization 2.0 action plan (Lei, 2018) pushed university teachers to integrate computer technology into leading and managing blended teaching. According to concepts in the leadership process, university teachers must constantly modify themselves and improve their proficiency in information-based instructional leadership in order to adapt to the evolving blended teaching and learning environment. What factors influence TIIL? It is a concerned for many educational researchers during COVID-19. Teachers’ informatization instructional leadership is a kind of comprehensive competence that teachers use to lead and manage blended teaching with the help of internet tools and devices. Zhao and Zhang (2019) proposed factors affecting TIIL from the perspective of extrinsic factors, intrinsic factors and individual ability factors i.e. blended teaching competence. The UTAUT model by Venkatesh, Morris, Davis, and Davis (2003) is increasingly applied in the educational domain for exploring influencing factors in behavioral intention to use a system or technology and investigating individual use behaviors. Previous research mainly adopted the first-generation analytical technique to conduct correlation analysis or regression analysis to explore factors influencing teachers’ instructional leadership (Zhao & Zhang, 2019). There is a lack of effectiveness in the field of instructional leadership when using second-generation techniques. Further investigation tools that use second-generation techniques such as PLS-SEM are still lacking. The goal of this study is to close the gap by evaluating the validity and reliability of an instrument that has been modified to look at the contributing elements that impact TIIL. 2. LITERATURE REVIEW 2.1. Definition of Teachers’ Informatization Instructional Leadership The term "informatization" originated in the Japanese translation of ”johoka”. Robert and Lavina (2012) defined educational informatization as a process pertaining to education and nurturing subsystems based on a task- oriented, that integrates methods, theories, technology and optimal use of ICT tools into the education domain to attempt to protect health and gain human education and development goals. Informatization leadership was one of the concepts that described and explained the leadership role shift which bridged two fields of leadership and technology. Informatization leadership is the ability to integrate information technology and management to facilitate the rapid absorption and use of information technology (Duan, 2020). Teachers’ informatization instructional leadership (TIIL) is a product of the combination of information technology and teachers’ instructional leadership in the context of the information age (Sun & Liu, 2015). Informatization Teaching Environment Construction (ITEC), Informatization Extracurricular Learning Leading (IELL) and Informatization Classroom Teaching Management (ICTM) are concepts that refer to teachers' informatization instructional leadership from the perspective of the leadership process (Zhao & Zhang, 2019). The term "teachers' informatization instructional leadership" in the context of current research refers to an information technology integration process with instructional management and leadership. Additionally, it also refers to the comprehensive competence with which teachers use information technology to manage and lead the blended teaching process. TIIL is not only related to the conventional e classroom but also extends beyond the classroom and their roles are diversified before, during, and after the classroom. This research will use survey questionnaires referring to Zhao and Zhang's (2019) measure of TIIL in three dimensions: ITEC, IELL and ICTM. 2.2. Factors Affecting Teachers’ Informatization and Instructional Leadership Instructional leadership involves a multidimensional research perspective. Zhao and Zhang (2019) discussed TIIL in terms of connotation, influencing factors and improving paths using first-generation data analysis methods Humanities and Social Sciences Letters, 2024, 12(3): 461-480 463 © 2024 Conscientia Beam. All Rights Reserved. (i.e., correlation analysis and regression analysis) and disclosed the correlated relationship between TIIL and its affecting factors such as the availability and accessibility of equipment and network conditions, the accessibility and value of extracurricular online learning resources, blended teaching competence, the ability to rationally control network autonomous learning time and informatization teaching evaluation ability. The Unified Theory of Acceptance and Use of Technology (UTAUT) originally measured the factors influencing employees who accept and employ information technology. These days UTAUT is increasingly applied to technology acceptance and use in educational contexts (Agudo-Peregrina, Hernández-García, & Pascual-Miguel, 2014; Attuquayefio & Addo, 2014; Bervell & Arkorful, 2020; Mukred, Yusof, Alotaibi, Mokhtar, & Fauzi, 2019; Arumugam Raman & Don, 2013). Dwivedi, Rana, Jeyaraj, Clement, and Williams (2019) asserted that the UTAUT model could measure many factors influencing the decision to use technology because it incorporated previous theories pertaining to the theory of reasoned action (TRA) by Fishbein and Ajzen (1975) the theory of planned behavior (TPB) by Venkatesh et al. (2003) and the Technology Acceptance Model (TAM) by Chuttur (2009). The UTAUT model by Venkatesh et al. (2003) was indicated to predict teachers’ behavioral intention in the classroom to use technology and use behavior in terms of PE, EE, SC and FC (Bian, Tian, & Meng, 2016; Li & Zhao, 2021; Malczyk, 2018; Raman & Thannimalai, 2021; Yeop, Yaakob, Wong, Don, & Zain, 2019). This study employed UTAUT to explain the TIIL behavior. Performance expectancy is adapted to suggest that university teachers will find computer technology useful in instructional leadership. According to this study's adaptation of effort expectations, university teachers will be more inclined to engage in instructional leadership if they find it simple to use computer technology for managing and directing the learning process. Social impact has been modified to show that in-service teachers using TIIL behaviour are influenced by their peers' views about their technology use. Facilitating conditions are adapted to consider if teachers’ adoption of TIIL is affected by the support from the technical and policy of a university. Furthermore, computer self-efficacy (CSE) and blended teaching competence (BTC) were attempted to become two additional direct determinants of university teachers' behavioural intention and informatization instructional leadership behaviour in a proposed structural model based on the UTAUT model in current research. This is grounded in the Theory of Planned Behavior (TPB) that blended teaching competence is one of technology skills and computer self-efficacy is one of self-efficacy beliefs. Moreover, the research by Compeau, Higgins, and Huff (1999) in the information system research area has found that individual computer-related behaviors and attitudes are rooted in all or part of social cognitive theory (SCT) by Bandura (1997). Liaw, Chang, Hung, and Huang (2006) found that computer self-efficacy positively affects individual cognition and behaviors. The theory of planned behavior proved that blended teaching competence is a kind of control belief and perceived facilitation which is used to measure teachers’ informatization instructional leadership behavior. This argument was identified by Zhao and Zhang (2019) that teachers' blended teaching competency is one of the important influencing factors in predicting teachers' informatization instructional leadership (TIIL). Wong (2013) and Hair, Hult, Ringle, and Sarstedt (2017) evaluated SmartPLS as one of the second-generation leading software utilization for PLS-SEM considered it a powerful tool for analyzing multivariate data. However, data analysis in related research on informatization instructional leadership still mainly adopts correlation analysis and regression analysis (Li, 2020; Zhao & Zhang, 2019). Yu and Zhang (2020) used AMOS-SEM to assess the predicted correlation between teacher information technology leadership and teaching efficacy based on the Chinese education context. Nevertheless, the employment of PLS-SEM to validate the instruments is still scarce. In relation to the above review, prior to examining the interrelation between PE, EE, SC, FC, BI, CSE, BTC and teachers' informatization instructional leadership (TIIL) among university teachers explore contributing construct-factors to TIIL. This pilot study mainly assesses the reliability and validity of instruments using SmartPLS in terms of three criteria: internal consistency and reliability depending on composite reliability (CR) and Cronbach’s alpha, convergent validity leaning against outer loading and average variance extracted (AVE), and Humanities and Social Sciences Letters, 2024, 12(3): 461-480 464 © 2024 Conscientia Beam. All Rights Reserved. discriminant validity relying on the Fornell-Larcker criterion, cross-loadings and Heterotrait-Monotrait Ratio (HTMT). 3. MATERIALS AND METHODS 3.1. Participants Sample participants were randomly selected through purposive sampling techniques and random cluster sampling techniques with the recruitment of 60 in-service teachers from a total population of nine private undergraduate universities in Xi’an city Shaanxi Province China. According to the comprehensive ranking of private undergraduate universities from the Chinese Ministry of Education, Chinese private universities are divided into four clusters. The elected four private undergraduate universities respectively represent four different clusters of private undergraduate universities because they have carried on blended teaching which is a necessary condition for adopting teachers’ informatization instructional leadership. The goal of the purposive sample strategy is to eliminate private undergraduate institutions that have not used blended learning. The next step was using a random cluster selection approach to choose in-service teachers who represented various university clusters. In other words, each university cluster had an equal chance of being selected throughout the sampling process. A total of 60 in-service teachers were finally selected randomly from five left universities with 15 in-service teachers representing each of the four cluster universities. They represent top private universities in China (A), first-class private universities in China (B), first-class private universities in the area (C) and well-known private universities in the region (D). 3.2. Instrument Table 1 shows the code and all the items used in this research instrument. Buabeng-Andoh and Baah's (2020) evaluation scale known as the Use Expectancy (UE) instrument was first developed to assess pre-service teachers' intention to adopt learning management systems. It also referred to a five-point Likert scale (Wang, 2018). It consists of 5 items measuring the ‘Performance Expectancy’ (PE) dimension and 5 items adapted to measure the ‘Effort Expectancy’ (EE) dimension. A six-point Likert scale has been developed and adapted for use with the SI scale which measures social impact and the FC scale which measures enabling circumstances (Buabeng-Andoh & Baah, 2020) and a five-point Likert scale (Wang, 2018). The CSE scale was adapted from Compeau et al. (1999). Compeau et al. (1999) initially devised three questions for measuring self-efficacy: I feel comfortable using this system. I can easily operate any device on this system if I want to. I can use the devices in the system even if no one is around to tell me how to use them. The present study modified the computer self-efficacy scale, comprising five items to assess the computer self-efficacy of university teachers in the context of blended learning and information leadership. The original scale was based on this model. The Blended Teaching Competence (BTC) scale was developed by Graham, Borup, Pulham, and Larsen (2019) referring to Pulham and Graham (2018) which was originally to measure pre-service and in-service teachers’ blended teaching competence. The BTC scale by Graham et al. (2019) consisted of four global themes which were pedagogy, management, assessment and technology to measure 6 dimensions respectively which are technical literary, planning, personalizing instruction, facilitating interactions, evaluating and reflecting and managing blended learning environments. In this research, the blended teaching competence scale was modified and consisted of eight dimensions with a total of 32 items in terms of pedagogy, management, assessment and technology. The following eight dimensions were respectively measured as four items: Technical Literacy (TL), Planning Blended Activities (PBA), Planning Blended Assessments (PBAS), Personalizing Instruction(PI) Facilitating Student- Student Interaction (FSSI), Facilitating Teacher-Student Interaction (FTSI), Evaluating and Reflecting (ER) and Managing the Blended Learning Environment (MBLE). Humanities and Social Sciences Letters, 2024, 12(3): 461-480 465 © 2024 Conscientia Beam. All Rights Reserved. Behavioral Intention (BI) is the mediating variable in this research model. The BI scale also refers to the scale to measure pre-service teachers’ intention to use a learning management system (Buabeng-Andoh & Baah, 2020) and a 5-point Likert scale (Wang, 2018). The TIIL scale was adapted from Zhao and Zhang (2019) which involved three dimensions with four items for each dimension and in current research, it has been modified into three dimensions with five items for each dimension. They were respectively Informatization Instructional Environment Construction (IIEC) with 5 items, Informatization Extracurricular Learning Leading (IELL) with 5 items and Informatization Classroom Instructional Management (ICIM) with 5 items. Table 1. Number of items in the survey questionnaire. Section Items Total items A Use expectancy (UE) Dimension 1: Performance expectancy 5 items Dimension 2: Effort expectancy 5 items B Social influence (SI) 5 items C Facilitating conditions (FC) 5 items D Computer self-efficacy (CSE) 5 items E Blended teaching competency (BTC) Dimension 1: Technical literary 4 items Dimension 2: Planning blended activities 4 items Dimension 3: Planning blended assessments 4 items Dimension 4: Personalizing instruction 4 items Dimension 5: Facilitating student-student interaction 4 items Dimension 6: Facilitating student-teacher interaction 4 items Dimension 7: Evaluating and reflecting 4 items Dimension 8: Managing a blended learning environment 4 items F Behavioral intention (BI) 5 items G Teachers’ informatization instructional leadership (TIIL) Dimension 1: Informatization teaching environment construction (ITEC) 5 items Dimension 2: Informatization extracurricular learning leading (IELL) 5 items Dimension 3: Informatization classroom teaching management (ICTM) 5 items Total items 77 items The above seven scales were all adapted, modified and translated from original scales into 11-point semantic differential scales starting from 0 (strongly disagree) to 10 (strongly agree) to fulfil the requirement of employing the PLS-SEM approach to conduct data analysis in this research context. 3.3. Procedures The procedure for carrying out the research was first permitted by the university teachers’ development center at four universities (A, B, C and D). The process of data collection was carried out in four sampled universities from November to December 2022. The questionnaires were administered during teacher routine meetings weekly on Wednesday afternoon. The survey questionnaires made through the Chinese questionnaire-star platform were distributed online to 15 in-service teachers from each of four private undergraduate universities (A, B, C and D) in Xi’an city of Shaanxi Province of China by survey questionnaire through social media (i.e., QQ, We-chat) with the help of peer teachers. None of the respondents were forced to answer the questionnaire but voluntarily and anonymously responded to the questions. The respondents were also given adequate time (20 minutes) to answer the questionnaire. 3.4. Data Analysis It is essential to evaluate the data collected to resolve missing values, questionable response patterns and outliers before using PLS-SEM for data analysis. The assessment of reliability and validity of the survey Humanities and Social Sciences Letters, 2024, 12(3): 461-480 466 © 2024 Conscientia Beam. All Rights Reserved. questionnaire relies on three important criteria (see Table 2): internal consistency reliability, convergent validity and discriminant validity (Hair et al., 2017). Table 2. Criteria for reliability and validity in PLS-SEM. Assessment Criteria Threshold value Reference Internal consistency reliability Composite reliability (CR) • 0.7-0.9 satisfied • 0.6 – 0.7 accepted • < 0.60 rejected Hair et al. (2017) Cronbach’s alpha (CA) 0.6-1 accepted Convergent validity Outer loading (OL) • 0.70 accepted • 0.4-0.7 (Acceptable with certain conditions) • < 0.40 rejected Average variance extracted (AVE) • > 0.50 Discriminant validity Cross loading • The indicator’s outer loading on the associated construct should be greater than any of its cross-loadings on other constructs. Fornell-Larcker criterion • The square root of each construct’s AVE should be greater than its highest correlation with any other construct. Heterotrait-Monotrait ratio (HTMT) • HTMT < 0.90 accepted • HTMT > 0.90 lack of discriminant validity 4. RESULTS This study examined the reliability and validity of seven adapted scales based on the survey questionnaires and the results of the findings are as follows: 4.1. Data Distribution Table 3 shows the results of the Kolmogorov-Smirnov normality test. The significant level is as follows: UE (.200, p > .05), SI (.008, p< .05), FC (.032 , p < .05), CSE (024, p < .05), BTC (028, p < .05), BI (.034, p < .05), and TIIL (.200, p > .05). Results indicate that UE and TIIL constructs show a normal data distribution. In contrast, it is not a normal distribution for the constructs of social influence, facilitating conditions, computer self-efficacy, blended teaching competence and behavioral intention. The PLS-SEM approach is still appropriate for non- normal data distribution since it is a non-parameter data analysis and modelling approach with less severe criteria than CB-SEM, which needs normal data distribution (Hair et al., 2017; Wong, 2013). Table 3. Kolmogorov-Smirnov normality test. Construct Kolmogorov-Smirnov Statistic Df Sig. UE 0.058 60 0.200* SI 0.136 60 0.008 FC 0.12 60 0.032 CSE 0.123 60 0.024 BTC 0.121 60 0.028 BI 0.119 60 0.034 TIIL 0.099 60 0.200* Note: *p <0.05. 4.2. Examination of Reliability and Validity Hair et al. (2017) posited that three crucial criteria were used to assess the reliability and validity of the survey questionnaire: internal consistency reliability, convergent validity and discriminant validity. Humanities and Social Sciences Letters, 2024, 12(3): 461-480 467 © 2024 Conscientia Beam. All Rights Reserved. 4.2.1. Internal Consistency Reliability Internal consistency reliability is the first criterion for evaluating how all factors on the test relate to all other factors. Cronbach’s alpha, as a first-generation statistical technique is the most conventional method used to show the degree of internal consistency reliability. Cronbach’s alpha follows the principle that if all factors intend to measure the same variable, then they are highly related and the value of alpha must be high; otherwise, they are not related and the value of alpha must be low. In addition, Cronbach’s alpha tends to underestimate internal consistency reliability. It assumes all items have equal outer loading on the constructs. Conversely, composite reliability fills up the Cronbach’s alpha limitations. It tends to overestimate the internal consistency reliability taking the different outer loadings of all items into account. Table 4's composite reliability value indicated that it was.880 for UE_PE, .885 for UE_EE, .941 for SI, .959 for FC, .864 for CSE, .866 for BTC_TL, .924 for BTC_PBA, .874 for BTC_PBAS, .890 for BTC_PI, .865 for BTC_FSSI, .879 for BTC_FTSI, .879 for BTC_ER, .884 for BTC_MBLE, .831 for BI, .893 for TIIL_ITEC, .928 for TIIL_IELL, and .899 for TIIL_ICTM. Additionally, all Cronbach’s alpha values shown in Table 4 exceed .70 which falls within the threshold range of.60 to 1. The specific Cronbach’s alpha showed UE_PE (.819), UE_EE (829), SI (.920), FC (.945), CSE (.804), BTC_ER (.818), BTC_FSSI (.767), BTC_FTSI (.826), BTC_MBLE (.829), BTC_PBAS (.806), .876 for BTC_PBA, .877 for BTC_PI, .796 for BTC_TL, .831 for BI, .870 for TIIL_ICTM, .914 for TIIL_IELL, .856 for TIIL_ITEC. The results of the composite reliability and Cronbach's alpha estimates show that all of the modified scales have attained a high level of internal consistency reliability and are reliable for evaluating each of the complex ideas included in this study. Table 4. The internal consistency reliability of scales based on constructs UE, SI, FC, CSE, BTC, BI and TIIL after item deletion. Matrix Cronbach’s alpha Composite reliability Average variance extracted (AVE) UE_PE 0.819 0.880 0.648 UE_EE 0.829 0.885 0.659 SI 0.920 0.941 0.799 FC 0.945 0.959 0.855 CSE 0.804 0.864 0.614 BTC_ER 0.818 0.879 0.681 BTC_FSSI 0.767 0.865 0.647 BTC_FTSI 0.826 0.879 0.647 BTC_MBLE 0.829 0.884 0.656 BTC_PBAS 0.806 0.874 0.656 BTC_PBA 0.876 0.924 0.802 BTC_PI 0.877 0.890 0.673 BTC_TL 0.796 0.866 0.617 BI 0.831 0.734 0.552 TIIL_ICTM 0.870 0.899 0.642 TIIL_IELL 0.914 0.928 0.721 TIIL_ITEC 0.856 0.893 0.677 4.2.2. Convergent Validity Convergent validity is used to measure the extent to which a measure correlates positively with an alternative measure of the same construct. Outer loading is also referred to as item and indicator reliability. Item loadings reflect the relationship between an item and its corresponding latent variable. According to Hair et al. (2017), Average Variance Extracted (AVE) describes the way the constructs explain the items or indicators. Table 5 shows specific outer loads that failed. Nine items were deleted due to outer loadings that was lower than .40 including outer loading values for BI_4 (-.375), BTC_FSSI_2 (.302), CSE_2 (.006), BTC_PBA_2 (.263), FC_4 (.143), SI_5 (.161), TIIL_ITEC_3 (.193), UE_EE_3 (.165) and UE_PE_4 (.290). After item deletion, the calculation process is conducted again until the AVE values reach the acceptance level of .50. The Average Variance Extracted (AVE) Humanities and Social Sciences Letters, 2024, 12(3): 461-480 468 © 2024 Conscientia Beam. All Rights Reserved. values of the items in Table 4 exceed the necessary threshold value of.50 following item elimination. The specific AVE values are UE_PE (0.648), UE_EE (0.659), SI (0.799), FC (0.855), CSE (0.614), BTC_ER (0.681), BTC_FSSI (0.647), BTC_FTSI (0.647), BTC_MBLE (0.656), BTC_PBAS (0.656), BTC_PBA (0.802), BTC_PI (0.673), BTC_TL (0.617), BI (0.552), TIIL_ICTM (0.642), TIIL_IELL (0.721), and TIIL_ITEC (0.677). The data analysis presented above indicates that AVE and outer loading both satisfied the threshold requirements. It is said that this study has proven convergent validity. Table 5. Research items for outer loading assessment. Constructs No. of items Outer loading (OL) Item deletion UE_PE 5 4 items with OL＞0.7 UE_PE_4 1 item with OL＜0.4 UE_EE 5 4 items with OL＞0.7 UE_EE_3 1 item with OL＜0.4 SI 5 4 items with OL＞0.7 SI_5 1 item with OL＜0.4 FC 5 4 items with OL＞0.7 FC_4 1 item with OL＜0.4 CSE 5 4 items with OL＞0.7 CSE_2 1 item with OL＜0.4 BTC_TL 4 4 items with OL＞0.7 - 0 item with OL＜0.4 BTC_PBA 4 3 items with OL＞0.7 BTC_PBA_2 1 item with OL＜0.4 BTC_PBAS 4 4 items with OL＞0.7 - 0 item with OL＜0.7 BTC_PI 4 4 items with OL＞0.7 - 0 item with OL＜0.7 BTC_FSSI 4 3 items with OL＞0.7 BTC_FSSI_2 1 items with OL＜0.4 BTC_FTSI 4 4 items with OL＞0.7 - 0 item with OL＜0.7 BTC_ER 4 4 items with OL＞0.7 - 0 items with OL＜0.7 BTC_MBLE 4 4 items with OL＞0.7 - 0 item with OL＜0.7 BI 5 4 items with OL＞0.7 BI_4 1 item with OL＜0.4 TIIL_ITEC 5 4 items with OL＞0.7 TIIL_ITEC_3 1 item with OL＜0.4 TIIL_IELL 5 5 items with OL＞0.7 - 0 items with OL＜0.7 TIIL_ICTM 5 5 items with OL＞0.7 - 0 item with OL＜0.7 Humanities and Social Sciences Letters, 2024, 12(3): 461-480 469 © 2024 Conscientia Beam. All Rights Reserved. Table 6. Fornell-Larcker criterion for the constructs UE, SI, FC, CSE, BTC, BI and TIIL. Fornell-Larcker criterion BI BTC_ER BTC_ FSSI BTC_ FTSI BTC_ MBLE BTC_ PBA BTC_ PBAS BTC_ PI BTC_ TL CSE FC SI TIIL_ ICTM TIIL_ IELL TIIL_ ITEC UE_ EE UE_ PE BI 0.743 BTC_ER 0.362 0.803 BTC_FSSI 0.310 0.624 0.825 BTC_FTSI 0.308 0.614 0.387 0.804 BTC_MBLE 0.439 0.680 0.366 0.650 0.810 BTC_PBA 0.470 0.545 0.361 0.519 0.583 0.896 BTC_PBAS 0.469 0.628 0.403 0.562 0.606 0.639 0.797 BTC_PI 0.193 0.160 0.012 0.427 0.291 0.272 0.209 0.820 BTC_TL 0.466 0.360 0.242 0.402 0.388 0.577 0.663 0.129 0.786 CSE 0.252 0.329 0.383 0.311 0.366 0.328 0.408 0.072 0.557 0.784 FC 0.243 -0.013 0.013 0.203 0.107 0.252 0.282 0.078 0.423 0.160 0.925 SI 0.230 0.363 0.076 0.186 0.233 0.179 0.395 0.127 0.207 0.201 -0.009 0.894 TIIL_ICTM 0.236 0.283 0.152 0.276 0.234 0.202 0.373 0.140 0.353 0.278 0.159 0.293 0.801 TIIL_IELL 0.282 0.302 0.332 0.265 0.253 0.283 0.318 0.112 0.282 0.263 0.101 0.161 0.528 0.849 TIIL_ITEC 0.275 0.367 0.393 0.323 0.393 0.320 0.389 0.164 0.278 0.470 0.096 0.239 0.589 0.642 0.823 UE_EE 0.442 0.318 0.343 0.373 0.354 0.440 0.276 0.257 0.269 0.347 0.092 0.206 0.012 0.164 0.209 0.812 UE_PE 0.459 0.393 0.455 0.215 0.408 0.570 0.495 0.094 0.434 0.378 0.225 0.142 0.092 0.271 0.309 0.738 0.805 Humanities and Social Sciences Letters, 2024, 12(3): 461-480 470 © 2024 Conscientia Beam. All Rights Reserved. 4.2.3. Discriminant Validity Discriminant validity assesses the uniqueness of each construct which is distinct from other constructs in the structural model (Hair et al., 2017). Henseler, Ringle, and Sarstedt (2015) assert that the HTMT approach is the mean value of all relationships of items across constructs measuring different constructs (i.e., the heterotrait- heteromethod correlations) relative to the mean of the average correlations of items measuring the same construct (i.e., the monotrait-heteromethods correlations). The threshold value for HTMT is .90. The term "lack of discriminant validity" refers to any HTMT score greater than .90. Table 6 displays the Fornell-Larcker criterion results with the square root of the AVE value for the BI construct (.743). It exceeds the BTC_ER (.362), BTC_FSSI (.310), BTC_FTSI (.308), BTC_MBLE (.439), BTC_PBA (.470), BTC_PBAS (.469), BTC_PI (.193), BTC_TL (.466), CSE (.252), FC (.243), SI (.230), TIIL_ICTM (.236), TIIL_IELL (.282), and TIIL_ITEC (.275), UE_PE (.459), UE_EE (.442). They also have the highest values for the square root of their AVE values which are higher than the values in the same row and column for the other reflective constructs. Thus, Table 6 indicates that discriminant validity was proven for all seven constructs. Table 7 shows the cross-loadings for each item reflected on the latent constructs BI, UE, CSE, FC and SI. Items of BI_1, BI_2, BI_3, and BI_5 have high loading on their corresponding construct BI and far exceed other constructs BTC_ER, BTC_FSSI, BTC_FTSI, BTC_MBLE, BTC_PBA, BTC_PBAS, BTC_PI, BTC_TL, CSE, FC, SI, TIIL_ICTM, TIIL_IELL, TIIL_ITEC, UE_EE and UE_PE. Similarly, items UE_PE and UE_EE also load higher than other constructs for each item of BI, BTC, SI, CSE, FC and TIIL. Similarly, items CSE_1, CSE_3, CSE_4, and CSE_5 also appeared to load high on their corresponding construct CSE but much higher on other constructs for each item of BI, BTC, FC, SI, UE and TIIL. Items FC_1, FC_2, FC_3, and FC_5 load high and also much higher on other constructs for each item of BI, BTC, SI, CSE, UE and TIIL. Items SI_1, SI_2, SI_3, and SI_4 also load higher than other constructs for each item of BI, BTC, FC, CSE, UE and TIIL. Table 8 displays the cross-loadings for items of the latent construct BTC. Items BTC_ER, BTC_FSSI, BTC_FTSI, BTC_MBLE, BTC_PBA, BTC_PBAS, BTC_PI, BTC_TL all have high loading on their corresponding construct BTC and also far exceed each item of other constructs BI, CSE, FC, SI, TIIL and UE. Humanities and Social Sciences Letters, 2024, 12(3): 461-480 471 © 2024 Conscientia Beam. All Rights Reserved. Table 7. Cross loadings for the constructs BI, UE, CSE, FC and SI. Cross loadings BI BTC_ ER BTC_F SSI BTC_ FTSI BTC_M BLE BTC_ PBA BTC_P BAS BTC_ PI BTC_ TL CSE FC SI TIIL_I CTM TIIL_I ELL TIIL_ ITEC UE_EE UE_PE BI_1 0.737 0.260 0.158 0.241 0.391 0.357 0.420 0.173 0.293 0.156 0.244 0.201 0.174 0.164 0.169 0.182 0.311 BI_2 0.715 0.203 0.275 0.228 0.268 0.384 0.261 0.066 0.419 0.232 0.125 0.061 0.143 0.216 0.248 0.349 0.295 BI_3 0.735 0.258 0.184 0.188 0.283 0.295 0.319 0.082 0.260 0.144 0.040 0.293 0.122 0.161 0.151 0.254 0.327 BI_5 0.785 0.339 0.275 0.250 0.359 0.354 0.394 0.225 0.381 0.200 0.270 0.167 0.237 0.267 0.230 0.463 0.414 UE_EE_1 0.406 0.231 0.457 0.302 0.248 0.347 0.208 0.194 0.371 0.370 0.115 0.126 -0.008 0.187 0.099 0.811 0.605 UE_EE_2 0.273 0.168 0.253 0.155 0.164 0.302 0.145 0.086 0.110 0.142 0.077 0.048 -0.027 0.017 0.191 0.773 0.629 UE_EE_4 0.347 0.188 0.141 0.303 0.329 0.346 0.234 0.333 0.093 0.275 0.071 0.274 -0.066 0.015 0.219 0.837 0.589 UE_EE_5 0.383 0.418 0.236 0.410 0.380 0.419 0.292 0.199 0.250 0.296 0.036 0.201 0.124 0.268 0.187 0.825 0.586 UE_PE_1 0.446 0.205 0.304 0.145 0.327 0.482 0.312 0.130 0.272 0.292 0.151 0.111 -0.037 0.160 0.248 0.686 0.844 UE_PE_2 0.328 0.198 0.280 0.111 0.319 0.492 0.407 0.127 0.316 0.340 0.187 -0.014 0.027 0.132 0.231 0.562 0.793 UE_PE_3 0.351 0.454 0.462 0.142 0.249 0.378 0.385 -0.012 0.386 0.201 0.174 0.144 0.129 0.350 0.160 0.518 0.793 UE_PE_5 0.334 0.440 0.439 0.307 0.428 0.485 0.526 0.046 0.450 0.399 0.224 0.213 0.211 0.245 0.364 0.589 0.788 CSE_1 0.083 0.188 0.189 0.155 0.294 0.252 0.338 -0.002 0.451 0.740 -0.041 0.109 0.200 0.182 0.294 0.203 0.214 CSE_3 0.270 0.368 0.434 0.359 0.412 0.338 0.383 0.105 0.389 0.851 0.088 0.115 0.291 0.252 0.528 0.309 0.340 CSE_4 0.185 0.236 0.313 0.263 0.240 0.169 0.320 0.119 0.490 0.772 0.190 0.214 0.253 0.250 0.389 0.262 0.293 CSE_5 0.163 0.154 0.142 0.094 0.150 0.247 0.230 -0.060 0.481 0.768 0.209 0.199 0.083 0.108 0.143 0.277 0.293 FC_1 0.100 0.068 0.026 0.245 0.130 0.190 0.271 -0.009 0.281 0.057 0.837 0.074 0.183 0.039 0.028 0.108 0.237 FC_2 0.274 -0.023 0.002 0.197 0.111 0.220 0.284 0.038 0.409 0.162 0.945 -0.009 0.162 0.138 0.157 0.036 0.196 FC_3 0.222 -0.016 0.035 0.197 0.069 0.225 0.244 0.068 0.406 0.160 0.970 0.000 0.157 0.107 0.112 0.072 0.163 FC_5 0.236 -0.031 -0.005 0.155 0.105 0.284 0.258 0.154 0.423 0.165 0.942 -0.052 0.112 0.057 0.017 0.150 0.260 SI_1 0.163 0.184 -0.047 0.037 0.072 0.139 0.228 0.065 0.166 0.063 0.005 0.840 0.058 -0.030 0.140 0.221 0.147 SI_2 0.087 0.271 0.095 0.218 0.183 0.113 0.382 0.128 0.084 0.247 -0.036 0.851 0.213 0.103 0.234 0.090 0.040 SI_3 0.208 0.365 0.107 0.177 0.243 0.186 0.385 0.138 0.158 0.234 -0.041 0.938 0.283 0.161 0.271 0.221 0.166 SI_4 0.274 0.406 0.102 0.225 0.280 0.174 0.408 0.124 0.256 0.193 0.017 0.943 0.395 0.253 0.217 0.172 0.117 Table 8. Cross loadings for the construct BTC. Cross loadings BI BTC_ER BTC_ FSSI BTC_F TSI BTC_ MBLE BTC_ PBA BTC_ PBAS BTC_ PI BTC_ TL CSE FC SI TIIL_I CTM TIIL_ IELL TIIL_ ITEC UE_EE UE_ PE BTC_ER_1 0.363 0.878 0.579 0.570 0.584 0.537 0.625 0.252 0.326 0.295 -0.087 0.297 0.212 0.239 0.265 0.281 0.354 BTC_ER_2 0.281 0.798 0.530 0.445 0.507 0.494 0.615 -0.031 0.393 0.303 0.133 0.382 0.237 0.236 0.447 0.211 0.366 BTC_ER_3 0.214 0.758 0.406 0.560 0.518 0.333 0.402 0.183 0.343 0.322 0.057 0.271 0.233 0.174 0.233 0.387 0.334 BTC_ER_4 0.275 0.775 0.460 0.408 0.578 0.346 0.330 0.089 0.105 0.148 -0.110 0.217 0.241 0.317 0.236 0.172 0.211 BTC_FSSI_1 0.284 0.578 0.834 0.289 0.347 0.401 0.360 -0.021 0.193 0.286 -0.114 -0.001 -0.025 0.229 0.284 0.362 0.466 BTC_FSSI_3 0.241 0.427 0.826 0.370 0.207 0.163 0.285 -0.016 0.090 0.180 0.086 -0.008 0.172 0.367 0.353 0.241 0.308 BTC_FSSI_4 0.238 0.530 0.816 0.305 0.346 0.312 0.349 0.074 0.318 0.489 0.082 0.211 0.259 0.233 0.344 0.233 0.338 BTC_FTSI_1 0.310 0.515 0.325 0.845 0.532 0.365 0.523 0.331 0.386 0.261 0.216 0.120 0.287 0.208 0.242 0.286 0.180 BTC_FTSI_2 0.198 0.451 0.269 0.779 0.471 0.440 0.333 0.339 0.245 0.251 -0.056 0.098 0.186 0.260 0.287 0.391 0.161 BTC_FTSI_3 0.284 0.555 0.371 0.866 0.575 0.513 0.509 0.365 0.362 0.265 0.300 0.250 0.249 0.162 0.297 0.274 0.191 Humanities and Social Sciences Letters, 2024, 12(3): 461-480 472 © 2024 Conscientia Beam. All Rights Reserved. Cross loadings BI BTC_ER BTC_ FSSI BTC_F TSI BTC_ MBLE BTC_ PBA BTC_ PBAS BTC_ PI BTC_ TL CSE FC SI TIIL_I CTM TIIL_ IELL TIIL_ ITEC UE_EE UE_ PE BTC_FTSI_4 0.123 0.443 0.253 0.720 0.544 0.349 0.396 0.383 0.249 0.223 0.095 0.098 0.084 0.300 0.214 0.292 0.159 BTC_MBLE_1 0.336 0.678 0.371 0.558 0.854 0.613 0.536 0.305 0.354 0.314 0.000 0.282 0.284 0.229 0.319 0.284 0.307 BTC_MBLE_2 0.405 0.546 0.319 0.541 0.802 0.448 0.467 0.084 0.350 0.321 0.130 0.151 0.155 0.236 0.383 0.214 0.234 BTC_MBLE_3 0.213 0.494 0.186 0.448 0.748 0.254 0.413 0.229 0.228 0.201 0.189 0.250 0.125 0.071 0.194 0.151 0.229 BTC_MBLE_4 0.405 0.492 0.278 0.540 0.832 0.504 0.530 0.338 0.298 0.314 0.063 0.120 0.185 0.229 0.326 0.438 0.507 BTC_PBAS_1 0.363 0.527 0.329 0.385 0.501 0.415 0.792 0.020 0.551 0.354 0.243 0.208 0.171 0.251 0.231 0.155 0.426 BTC_PBAS_2 0.390 0.446 0.231 0.373 0.438 0.472 0.729 0.176 0.477 0.276 0.215 0.368 0.222 0.279 0.363 0.253 0.385 BTC_PBAS_3 0.343 0.493 0.383 0.539 0.559 0.581 0.778 0.361 0.540 0.329 0.145 0.252 0.344 0.128 0.347 0.236 0.372 BTC_PBAS_4 0.394 0.534 0.347 0.497 0.443 0.567 0.880 0.123 0.544 0.342 0.285 0.413 0.446 0.340 0.298 0.233 0.393 BTC_PBA_1 0.446 0.646 0.465 0.535 0.551 0.941 0.622 0.266 0.546 0.347 0.180 0.197 0.201 0.227 0.306 0.376 0.491 BTC_PBA_3 0.428 0.371 0.224 0.510 0.468 0.887 0.535 0.414 0.522 0.222 0.277 0.225 0.275 0.251 0.232 0.485 0.492 BTC_PBA_4 0.386 0.437 0.272 0.335 0.551 0.856 0.560 0.029 0.478 0.314 0.222 0.047 0.053 0.288 0.328 0.315 0.555 BTC_PI_1 0.067 0.323 0.200 0.489 0.406 0.350 0.334 0.778 0.134 0.194 0.057 0.170 0.152 0.130 0.184 0.274 0.183 BTC_PI_2 0.080 0.074 0.039 0.314 0.207 0.087 0.105 0.812 -0.006 0.072 0.000 0.099 0.103 0.040 0.093 0.230 0.070 BTC_PI_3 -0.006 0.145 0.110 0.358 0.238 0.192 0.208 0.706 0.032 0.066 0.043 0.146 0.082 0.286 0.224 0.214 0.049 BTC_PI_4 0.233 0.116 -0.050 0.379 0.239 0.268 0.176 0.964 0.151 0.026 0.098 0.105 0.126 0.118 0.158 0.219 0.060 BTC_TL_1 0.384 0.359 0.160 0.347 0.342 0.440 0.530 0.063 0.834 0.469 0.331 0.132 0.313 0.180 0.189 0.057 0.188 BTC_TL_2 0.323 0.272 0.160 0.322 0.279 0.429 0.510 0.040 0.796 0.373 0.435 0.099 0.221 0.228 0.188 -0.001 0.229 BTC_TL_3 0.449 0.282 0.238 0.314 0.325 0.542 0.556 0.144 0.789 0.452 0.258 0.288 0.317 0.223 0.250 0.493 0.548 BTC_TL_4 0.258 0.195 0.195 0.275 0.256 0.363 0.474 0.164 0.720 0.459 0.346 0.075 0.236 0.279 0.251 0.224 0.356 Table 9. Cross loadings for the construct TIIL. Cross loadings BI BTC_ ER BTC_ FSSI BTC_F TSI BTC_ MBLE BTC_ PBA BTC_ PBAS BTC_ PI BTC_ TL CSE FC SI TIIL_ ICTM TIIL_I ELL TIIL_I TEC UE_EE UE_ PE TIIL_ICTM_1 0.120 0.197 0.160 0.309 0.153 0.218 0.368 0.185 0.361 0.119 0.264 0.045 0.767 0.515 0.479 0.030 0.140 TIIL_ICTM_2 0.050 0.080 0.003 0.055 0.095 0.035 0.140 0.106 0.056 0.159 0.006 0.205 0.782 0.380 0.444 -0.219 -0.072 TIIL_ICTM_3 0.143 0.265 0.194 0.253 0.160 0.002 0.278 0.022 0.107 0.102 -0.005 0.380 0.716 0.465 0.434 -0.013 0.028 TIIL_ICTM_4 0.218 0.195 0.122 0.209 0.204 0.135 0.320 0.105 0.321 0.228 0.287 0.152 0.897 0.527 0.537 -0.029 0.109 TIIL_ICTM_5 0.259 0.287 0.095 0.218 0.235 0.280 0.308 0.142 0.373 0.359 0.033 0.332 0.832 0.301 0.467 0.090 0.070 TIIL_IELL_1 0.324 0.345 0.371 0.305 0.272 0.363 0.378 0.130 0.353 0.297 0.100 0.197 0.585 0.930 0.645 0.259 0.307 TIIL_IELL_2 0.115 0.208 0.195 0.162 0.249 0.123 0.204 0.039 0.150 0.132 0.029 0.191 0.375 0.770 0.495 0.061 0.174 TIIL_IELL_3 0.300 0.229 0.312 0.231 0.145 0.251 0.266 0.105 0.246 0.227 0.093 0.083 0.416 0.905 0.529 0.147 0.260 TIIL_IELL_4 0.056 0.231 0.234 0.290 0.269 0.226 0.234 0.152 0.122 0.172 0.085 0.150 0.400 0.822 0.587 0.175 0.188 TIIL_IELL_5 0.146 0.220 0.169 0.108 0.230 0.096 0.162 0.038 0.139 0.193 0.104 0.095 0.384 0.808 0.496 -0.064 0.107 TIIL_ITEC_1 0.160 0.134 0.275 0.129 0.152 0.146 0.222 0.043 0.163 0.414 0.028 0.147 0.459 0.555 0.800 0.042 0.188 TIIL_ITEC_2 0.329 0.409 0.390 0.394 0.396 0.338 0.416 0.177 0.335 0.347 0.126 0.199 0.549 0.482 0.858 0.277 0.280 TIIL_ITEC_4 0.180 0.309 0.366 0.208 0.420 0.313 0.284 0.082 0.150 0.420 0.064 0.151 0.471 0.555 0.840 0.151 0.336 TIIL_ITEC_5 0.122 0.241 0.168 0.202 0.232 0.163 0.269 0.227 0.160 0.436 0.048 0.339 0.400 0.618 0.790 0.103 0.170 Humanities and Social Sciences Letters, 2024, 12(3): 461-480 473 © 2024 Conscientia Beam. All Rights Reserved. Table 10. Heterotrait-Monotrait Ratio (HTMT). HTMT 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1.BI 2.BTC_ER 0.443 3.BTC_FSSI 0.396 0.769 4.BTC_FTSI 0.359 0.739 0.471 5.BTC_MBLE 0.531 0.825 0.443 0.776 6.BTC_PBA 0.581 0.624 0.427 0.600 0.659 7.BTC_PBAS 0.606 0.756 0.513 0.669 0.737 0.762 8.BTC_PI 0.182 0.267 0.190 0.551 0.372 0.323 0.299 9.BTC_TL 0.574 0.439 0.307 0.469 0.458 0.673 0.822 0.157 10.CSE 0.286 0.375 0.461 0.334 0.410 0.378 0.499 0.161 0.713 11.FC 0.254 0.138 0.142 0.245 0.155 0.273 0.324 0.075 0.485 0.184 12.SI 0.277 0.393 0.156 0.222 0.273 0.191 0.448 0.172 0.215 0.249 0.058 13.TIIL_ICTM 0.239 0.304 0.297 0.287 0.246 0.211 0.415 0.160 0.361 0.281 0.187 0.308 14.TIIL_IELL 0.266 0.330 0.358 0.325 0.295 0.281 0.336 0.203 0.286 0.261 0.098 0.180 0.575 15.TIIL_ITEC 0.292 0.393 0.446 0.335 0.409 0.335 0.429 0.223 0.296 0.537 0.101 0.283 0.650 0.749 16.UE_EE 0.527 0.386 0.411 0.448 0.393 0.509 0.329 0.308 0.371 0.389 0.119 0.234 0.160 0.186 0.217 17.UE_PE 0.573 0.492 0.573 0.261 0.481 0.677 0.623 0.135 0.530 0.450 0.268 0.175 0.184 0.286 0.350 0.891 Humanities and Social Sciences Letters, 2024, 12(3): 461-480 474 © 2024 Conscientia Beam. All Rights Reserved. Table 9 displays the cross-loadings for all items of the latent construct TIIL. Each TIIL item loads more quickly than each BI, BTC, SI, CSE, UE, and FC item in other constructs. According to the Fornell-Larcker criterion and the cross-loadings criterion, it can be concluded from the research findings shown in Tables 6 to 9 that all reflective constructs have the highest values for the square root of their AVE values which are respectively greater than values in the same row and column and that all loadings of items are greater than the corresponding cross-loadings. Thus, this indicates all constructs have established discriminant validity. The Heterotrait-Monotrait Ratio (HTMT) shown in Table 10 was the last criterion used to measure the discriminant validity. The HTMT of all constructs is below the threshold value of .85. It illustrates that the HTMT for BTC_ER à BI is .443, BTC_FSSI à BTC_ER is .769, BTC_FTSI à BTC_FSSI is .471, BTC_MBLE à BTC_FTSI is .776, BTC_PBA à BTC_MBLE is .659, BTC_PBAS à BTC_PBA is .762. BTC_PI à BTC_PBAS is .299, BTC_TL à BTC_PI is .157, CSE à BTC_TL is .713, FC à CSE is .184, SI à FC is .058, TIIL_ICTM à SI is .308, TIIL_IELL à TIIL_ICTM is .575, TIIL_ITEC à TIIL_IELL is .749, UE_EE à TIIL_ITEC is .217, and UE_PE à UE_EE is .891. The above data analysis clearly indicates that discriminant validity has been established. 4.3. Comparison of Structural Equation Modeling Figures 1 and 2 display a comparison of the structural equation model for composite reliability and outer loading values for all constructs before and after item deletion. The outer loading values for each item were displayed by the arrow respectively. Meanwhile, the number shown in the circular shape is the composite reliability for each construct. The outer loadings of nine items were found to be less than.40; hence, they must be eliminated in order to satisfy the outer loading criterion. All composite reliability values reach a satisfactory level following item elimination despite the fact that they all exceed the acceptance minimum requirement of.60 in both figures. Figure 1. Composite reliability and outer loading before item deletion. Humanities and Social Sciences Letters, 2024, 12(3): 461-480 475 © 2024 Conscientia Beam. All Rights Reserved. Figure 2. Composite reliability and outer loading after item deletion. Figures 3 and 4 display a comparison of the structural equation model for AVE and outer loading values for all constructs before and after item deletion. The outer loading values for each item were displayed by the arrow respectively. All indicators of the first-order construct BTC_TL, BTC_PBAS, BTC_PI, BTC_FTSI, BTC_ER, BTC_MBLE, TIIL_IECLL, and TIIL_ICTM have outer loadings higher than the threshold value of 0.70. Nonetheless, few constructs (i.e., UE_PE_4, UE_EE_3, SI_5, FC_4, CSE_2, BTC_PBA_2, BTC_FSSI_2, BI_4, and TIIL_ITEC_3) consisted of items with outer loading values less than .70. Nine items out of the 77 original items in the questionnaire were removed after examination. Thus, the total percentage of items deleted from the research instruments is reported as 11.7%. The item deletion has led to an increase in AVE values. Humanities and Social Sciences Letters, 2024, 12(3): 461-480 476 © 2024 Conscientia Beam. All Rights Reserved. Figure 3. AVE and outer loading before item deletion. Figure 4. AVE and outer loading after item deletion. Humanities and Social Sciences Letters, 2024, 12(3): 461-480 477 © 2024 Conscientia Beam. All Rights Reserved. 5. DISCUSSION The UTAUT model was used in the current study to determine the five components that influence TIIL, UE, SI, FC, CSE, BTC and BI. This pilot study used the PLS-SEM analytical approach to assess the validity and reliability of scales prior to obtaining the results of the interrelationship of the components in the expanded UTAUT model to explore contributing variables to TIIL. There is a scarcity of research adopting Smart PLS to validate multidimensional instruments in spite of questionnaires developed in previous literature. Additionally, when previous research used the first-generation technique to validate research instruments (i.e., performance expectancy scale , effort expectancy scale, social influence scale, facilitating conditions scale, behavioral intention scale and teachers’ informatization instructional leadership scale), they mainly focused on the Cronbach’s alpha value (other than composite reliability value) and CFA values instead of EFA values. The limitation of using the first generation statistical analysis approach is the lack of instrument validation in multidimensional data and the easy production of measurement errors. Thus, this research adopts the PLS-SEM statistical analysis approach to evaluate the validation of the instruments in terms of internal consistency reliability, convergent validity and discriminant validity for all items of instruments to reduce measurement error. When testing the internal consistency reliability of instruments using the first-generation statistical analysis technique, the PLS-SEM approach emphasises composite reliability while compensating for the lack of primary focus on the Cronbach's alpha value. Hence, re-validating instruments using more advanced second-generation approaches tends to improve the instrument's precision in measuring certain constructs since the several validation perspectives boost the accuracy of evaluating the instrument using many indications. 5.1. Reliability According to Buabeng-Andoh and Baah (2020) and Wang (2018) the reliability analysis for the developed PE scale, EE scale , SI scale , FC scale and BI scale only used one criteria which is Cronbach’s alpha value, whereas current research used two criteria which are composite reliability and Cronbach’s alpha value to analyze the reliability of scales. Research results showed that five modified scales (i.e., PE scale, EE scale, SI scale, FC scale and BI scale) established the internal consistency reliability extended UTAUT model that can be applied to the field of teachers’ informatization and instructional leadership. This research combined CSE and BTC instrument development from the literature review by Compeau et al. (1999), Graham et al. (2019) and Pulham and Graham (2018) with the Chinese university context. The results of composite reliability and Cronbach's alpha all fulfilled requirements after the CSE and BTC scales were revalidated and outside loadings with a coefficient of less than.40 were eliminated. Thus, distinct internal consistency reliability has already been established in the field of TIIL. As for the TIIL scale, it was developed according to a Chinese literature review by Zhao and Zhang (2019) but after re-validating the adapted the TIIL scale , one item’s outer loading was found to fail loading so it was deleted. This indicated that it is essential for researchers to re-validate an adapted modified TIIL scale although the TIIL scale and the adapted TIIL scale are both used in the same research field of teachers’ informatization and instructional leadership. The rationale behind it is that this research adopted the PLS-SEM technique and Zhao and Zhang (2019) used the AMOS-SEM technique. In other words, PLS-SEM and AMOS-SEM are both second- generation approaches but they require different data assumptions. PLS-SEM has no assumptions about the data distribution. In contrast, AMOS-SEM assumes the data to be normally distributed. This is similar to Wong (2013). 5.2. Validity Outer loading and Average Variance Extracted (AVE) are two important criteria to assess the convergent validity of seven adapted scales. The results of this study clearly showed that convergent validity assessment is Humanities and Social Sciences Letters, 2024, 12(3): 461-480 478 © 2024 Conscientia Beam. All Rights Reserved. crucial for analyzing the extent to which the constructs explain the items or indicators and for assessing the correlation between an item and its corresponding latent variable for outer loading and AVE before and after item deletion. Furthermore, it demonstrated that every construct had a unique quality that set it apart from the other constructs in the structural model by increasing cross loading, the Fornell-Larcker criterion and HTMT in response to the deletion of unloaded items. This is consistent with the findings of Hair et al. (2017) who stated in their study that there was no contradiction issue that emerged for the reliability and validity assessments because all of the items deleted in the HTMT were the same as the items deleted in the cross-loading assessment, Fornell- Larcker criterion, Cronbach's alpha, composite reliability and AVE analysis. The aforementioned discussion demonstrated that it was crucial for this study to evaluate the validity of the seven modified scales in the PLS-SEM model. When the constructed UE, SI, FC, CSE, BI, and TIIL scales were revalidated, the results added up to show that these instruments are valid and reliable for use in the subsequent examination of the relationships between the constructs to determine the components that contribute to TIIL. 6. CONCLUSION This study added two new variables to the UTAUT model from the perspective of teachers' informatization instructional leadership process and examined the status of Chinese university teachers participating in informatization instructional leadership during COVID-19 from both theoretical and empirical perspectives. In terms of practice, this study suggests that CSE and BTC are also two important factors affecting to TIIL so they should be focused on in the process of adopting TIIL. The PLS-SEM technique was most crucially employed in this study to re-validate seven updated scales that evaluate different influencing elements. This enriched methodological theory will help to increase Chinese teachers’ computer self-efficacy to use technology in their future instructional leadership, i.e., to gradually shift from passive obedience to conduct TIIL to an intrinsic confidence to integrate computer technology into instructional leadership. In addition, this empirical research expressed the concern that Chinese private university teachers need to improve their blended teaching competence in order to design and use technology well to achieve TIIL goals. Funding: This study received no specific financial support. Institutional Review Board Statement: The Ethical Committee of the Faculty of Psychology and Education, University Malaysia Sabah, Malaysia has granted approval for this study on 19 August 2020 (Ref. No. DP11921112A). Transparency: The authors state that the manuscript is honest, truthful, and transparent, that no key aspects of the investigation have been omitted, and that any differences from the study as planned have been clarified. This study followed all writing ethics. Competing Interests: The authors declare that they have no competing interests. Authors’ Contributions: Conceptualization, methodology, investigation, R.A and A.A.; software, data curation, writing—original draft preparation, R.A.; validation, formal analysis, supervision and project administration, E.M.A.; resources, R.A., A.A. J.A and A.O.; writing—review and editing, R.A., A.A., E.M.A., J.A and A.O.; visualization, E.M.A. and A.A.; funding acquisition, A.A., E.M.A and A.O. Both authors have read and agreed to the published version of the manuscript. REFERENCES Agudo-Peregrina, Á. F., Hernández-García, Á., & Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301-314. http://doi.org/10.1016/j.chb.2013.10.035 Attuquayefio, S. N., & Addo, H. (2014). Using the UTAUT model to analyze students' ICT adoption. International Journal of Education and Development using Information and Communication Technology, 10(3), 75. Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman. Bervell, B., & Arkorful, V. (2020). LMS-enabled blended learning utilization in distance tertiary education: Establishing the relationships among facilitating conditions, voluntariness of use and use behaviour. International Journal of Educational Technology in Higher Education, 17(1), 1-16. https://doi.org/10.1186/s41239-020-0183-9 http://doi.org/10.1016/j.chb.2013.10.035 https://doi.org/10.1186/s41239-020-0183-9 Humanities and Social Sciences Letters, 2024, 12(3): 461-480 479 © 2024 Conscientia Beam. All Rights Reserved. Bian, Q., Tian, Z. Q., & Meng, G. H. (2016). Research on the influencing factors of network learning of normal school students taught in mongolian in perspective of technology acceptance model. Research on Ethnic Education, 27(6), 60-64. https://doi.org/10.15946/j.cnki.1001-7178.2016.06.009 Buabeng-Andoh, C., & Baah, C. (2020). Pre-service teachers’ intention to use learning management system: An integration of UTAUT and TAM. Interactive Technology and Smart Education, 17(4), 455-474. http://doi.org/10.1108/ITSE-02-2020- 0028 Chuttur, M. (2009). Overview of the technology acceptance model: Origins, developments and future directions. All Sprouts Content. Retrieved from https://aisel.aisnet.org/sprouts_all/290 Compeau, D., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Quarterly, 23, 145-158. https://doi.org/10.2307/249749 Duan, C. (2020). Research on the influence of principal's informatization leadership on teachers' information technology application behavior: A meta-analysis based on 42 empirical research literatures. China Distance Education, 2020(10), 13. Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21, 719-734. https://doi.org/10.1007/s10796-017-9774-y Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research United States of America: Addison-Wesley Publishing Company. Graham, C. R. (2006). Blended learning systems: Definition, current trends, and future directions in C. J. Bonk & C. R. Graham (Eds.), Handbook of blended learning Global perspectives local designs. In (pp. 3–21). San Francisco: John Wiley and Sons. Graham, C. R., Borup, J., Pulham, E., & Larsen, R. (2019). K-12 blended teaching readiness: Model and instrument development. Journal of Research on Technology in Education, 51(3), 239-258. http://doi.org/10.1080/15391523.2019.1586601 Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modelling (PLS- SEM) (2nd ed.). United States of America: Sage Publication. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. http://doi.org/10.1007/s11747-014- 0403-8 Lei, C. Z. (2018). Educational informatization: From 1.0 to 2.0-- The trend and thinking of the development of educational informatization in China in the new era. Journal oF East China Normal University, 1, 98-103. https://doi.org/10.16382/j.cnki.1000-5560.2018.01.012 Li, Y., & Zhao, M. (2021). A study on the influencing factors of continued intention to use MOOCs: UTAUT model and CCC moderating effect. Frontiers in Psychology, 12, 528259. https://doi.org/10.3389/fpsyg.2021.528259 Li, Y. F. (2020). Research on influencing factors and forming path of informatization leadership behavior of primary school teachers. Education Research, 41(4), 109-115. https://doi.org/10.13811/j.cnki.eer.2020.04.015 Liaw, S.-S., Chang, W.-C., Hung, W.-H., & Huang, H.-M. (2006). Attitudes toward search engines as a learning assisted tool: Approach of Liaw and Huang’s research model. Computers in Human Behavior, 22(2), 177-190. https://doi.org/10.1016/j.chb.2004.09.003 Malczyk, B. R. (2018). Multimodal instruction, the new hybrid: A student-centered approach to blended learning. Journal of Nonprofit Education and Leadership, 8(1), 16-31. http://doi.org/10.18666/JNEL-2018-V8-I1-8347 Mukred, M., Yusof, Z. M., Alotaibi, F. M., Mokhtar, U. A., & Fauzi, F. (2019). The key factors in adopting an electronic records management system (ERMS) in the educational sector: A UTAUT-based framework. IEEE Access, 7, 35963-35980. http://doi.org/10.1109/ACCESS.2019.2904617 Pulham, E., & Graham, C. R. (2018). Comparing K-12 online and blended teaching competencies: A literature review. Distance Education, 39(3), 411-432. http://doi.org/10.1080/01587919.2018.1476840 https://doi.org/10.15946/j.cnki.1001-7178.2016.06.009 http://doi.org/10.1108/ITSE-02-2020-0028 http://doi.org/10.1108/ITSE-02-2020-0028 https://aisel.aisnet.org/sprouts_all/290 https://doi.org/10.2307/249749 https://doi.org/10.1007/s10796-017-9774-y http://doi.org/10.1080/15391523.2019.1586601 http://doi.org/10.1007/s11747-014-0403-8 http://doi.org/10.1007/s11747-014-0403-8 https://doi.org/10.16382/j.cnki.1000-5560.2018.01.012 https://doi.org/10.3389/fpsyg.2021.528259 https://doi.org/10.13811/j.cnki.eer.2020.04.015 https://doi.org/10.1016/j.chb.2004.09.003 http://doi.org/10.18666/JNEL-2018-V8-I1-8347 http://doi.org/10.1109/ACCESS.2019.2904617 http://doi.org/10.1080/01587919.2018.1476840 Humanities and Social Sciences Letters, 2024, 12(3): 461-480 480 © 2024 Conscientia Beam. All Rights Reserved. Raman, A., & Don, Y. (2013). Preservice teachers' acceptance of learning management software: An application of the UTAUT2 model. International Education Studies, 6(7), 157-164. https://doi.org/10.21203/rs.3.rs-27351/v1 Raman, A., & Thannimalai, R. (2021). Factors impacting the behavioural intention to use E- learning at higher education amid the Covid-19 Pandemic: UTAUT2 model. Psihologičeskaâ Nauka i Obrazovanie, 26(3), 82-93. http://doi.org/10.17759/pse.2021260305 Ritimoni, B., Prasenjit, D., & Kandarpa, D. (2021). Perception towards online/blended learning at the time of Covid-19 pandemic: An academic analytics in the Indian context. Asian Association of Open Universities Journal, 16(1), 41-60. Robert, I. V., & Lavina, T. A. (2012). Explanatory dictionary of words and phrases of the conceptual apparatus of education informatization. In (pp. 108). Moscow: BINOM Laboratoriya Znanii. Sun, Z. X., & Liu, X. X. (2015). Teacher information-based teaching leadership: Concept connotation and survey analysis. Modern Distance Education, 4, 28-36. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. Mis Quarterly, 27(3), 425-478. http://doi.org/10.2307/30036540 Wang, X. p. (2018). Research on model construction and empirical study of college teacher’s blended learning acceptance, 108-110 (Master dissertation) Yunnan normal university. Retrieved from https://kns.cnki.net/kcms/detail/detail.aspx?FileName=1018796769.nh&DbName=CMFD2019 Wong, K. K. (2013). Partial least squares structural equation modelling (PLS-SEM) techniques using SmartPLS. Marketing Bulletin, 24(1), 1-32. Yeop, M. A., Yaakob, M. F. M., Wong, K. T., Don, Y., & Zain, F. M. (2019). Universiti Utara Malaysia, M., Universiti Pendidikan Sultan Idris, M., & Universiti Utara Malaysia, M. M. F. U. Implementation of ICT policy (blended learning approach): investigating factors of behavioural intention and use behaviour. International Journal of Instruction, 12(1), 767-782. http://doi.org/10.29333/iji.2019.12149a Yu, T. Z., & Zhang, X. F. (2020). Impact of teachers' information technology leadership on teaching efficacy: An empirical analysis using structural equation model. Teacher Education Research, 32(2), 48-56. https://doi.org/10.13445/j.cnki.t.e.r.2020.02.008 Zhao, L. L., & Zhang, R. F. (2019). Teachers’ informationization teaching leadership: Connotation, influencing factors and improving path. 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