Examining the Reliability and Validity of Measuring Scales related to Informatization Instructional Leadership Using PLS-SEM Approach

During the COVID-19, (TIIL) was adopted in many countries. This attracted widespread attention. This research derived six factors from the unified theory of acceptance and use of technology (UTAUT) including performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC) and behavioral intention (BI), added two new internal elements related to the individual teacher which are computer self-efficacy (CSE) and blended teaching competence (BTC). Before using the Partial Least Squares Structural Equation Modeling (PLS-SEM) to explore the contributing factors to TIIL by assessing interrelation between constructs within extended UTAUT model in this study. This pilot study aimed to examine the reliability and validity of modified scales incorporating Use Expectancy (UE) Scale including (PE Scale and EE Scale) used to measure use expectancy, SI Scale to measure social influence, FC Scale to measure facilitating conditions, CSE Scale to measure computer self-efficacy, BTC Scale to measure blended teaching competence, BI Scale to measure behavioral intention to adopt TIIL, and the TIIL informatization instructional leadership. A total of 60 teachers from the large multi-nce in China participated in this research. The data was collected in November-December 2022 during the middle stages of COVID-19 pandemic. The PLS-SEM approach was used to evaluate the reliability and validity of the adapted scales. The internal consistency reliability was determined by composite reliability (CR) extracted (AVE). Assessment of discriminant validity was measured by Fornell-Larcker criterion, Cross-loadings and Heterotrait-Monotrait Ratio (HTMT). Results showed after deleting nine items with lower than .40, . All item values fulfilled the criteria of AVE, Fornell-Larcker criterion, cross-loadings, and HTMT. Research results revealed all adapted scales were valid and reliable to be used in future research. This study explored the influencing factors of TIIL in Chinese context, enriched the theory of TIIL, and provided practical support for the future development of TIIL.


INTRODUCTION
The present teaching management environment is complex with blended teaching and learning environment.Blended teaching was defined by [14] as models face-to-.The COVID-19 has caused blended teaching as common-state teaching modality across worldwide universities [31], bringing the greater challenge to university teachers to learn computer technology to lead or manage blended teaching.Thus, the conventional face-to-face way of leading and managing university class was broken, presenting a mode of adoption of university blended teaching through computer technology/devices/teaching management platform.
Furthermore, Chinese Education Informatization 2.0 Action Plan pushed university teachers to integrate computer technology into leading and managing blended teaching.From the perspective of leadership process, it has been suggested that university teachers need continuously to adapt themselves and enhance their competence of informatization instructional leadership to respond to the changes of blended teaching and learning environment.What factors influence TIIL is concerned by many educational researchers during the COVID-19?informatization instructional leadership is a kind of comprehensive competence that teachers lead and manage blended teaching with the help of internet tools/devices.[44] proposed factors affecting TIIL in the perspective of extrinsic factors, intrinsic factors and individual ability factors i.e. blended teaching competence.UTAUT model by [39] is increasingly used in educational domain to explore influencing factors to behavioral intention to use a system or technology, and investigate individual use behaviors.
Previous researches on instructional leadership and the affecting factors to it has focused on the relationships between elements using the first-generation technique such as correlation analysis and regression analysis and the use of AMOS structural equation modeling.But the re-validation of research instruments adopting the second-generation such as PLS-SEM is not be sufficient.In addition, the discussion about the adoption of special period of COVID-19, therefore, this study used PLS-SEM approach to explore the factors to TIIL among university teachers during COVID-19, and examine the reliability and validity of adapted scales to measure the factors affecting TIIL.

Definition of
The term "informatization" originated in Japan.Wu (2008) defines it as "the process of penetration of information and communication technology into all levels and fields of human production, exchange and social interaction".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 leadership in the context of the information age [37].From the perspective of leadership process, the connotation of teacher informatization instructional leadership includes Informatization Teaching Environment Construction (ITEC), Informatization Extracurricular Learning Leading (IELL) and Informatization Classroom Teaching Management (ICTM) [44].
In the context of current research, teacher informatization instructional leadership refers to a process of information technology integrated with instructional management and leadership.Additionally, it also refers to the comprehensive competence that teachers utilize information technology to manage and lead blended teaching process.TIIL is not only limited to the face-to-face 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 [44] to measure TIIL from three dimensions:ITEC, IELL and ICTM.
The research perspective of the instructional leadership is multidimensional.[44] discussed TIIL in terms of connotation, influencing factors and improving path, using the first-generation data analysis methods (i.e., the 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 of rationally controlling network autonomous learning time and informatization teaching evaluation ability.leadership and management process is a behavior which can be explained by Unified Theory of Acceptance and Use of Technology (UTAUT) by [39] in current research.Originally UTAUT was created to understand the factors that affected employee information technology acceptance and use.Nevertheless, with the trending of technology integration into education area, increasing studies have applied it to an educational context [5] [1] [4][27] [29].Researchers often use the UTAUT model because it examines more factors in the technology use decision.Increasing researches have suggested that UTAUT model by [39] technology and use behavior [6] [22] [24] [29] [42], stipulating the effect of performance expectancy (PE), effort expectancy (EE), social influence (SC) and facilitating conditions (FC) on the behavioral intention (BI) to adopt innovative technology in the classroom.
In current research, performance expectancy is adapted to suggest that university teachers will find computer technology useful in instructional leadership.Adapting effort expectancy to this study indicates if university teachers find computer technology easy to apply while leading and administering instructional process, they will have stronger intention to conduct instructional leadership.Social influence adapted to current study to indicate due to using technology.The construct of facilitating conditions concerns the view that support the use of a technology.
Beyond that, in proposed structural model based on UTAUT model in current research, computer self-efficacy (CSE) and blended teaching competence (BTC) were attempted to become two additional direct determinants of behavioral intention and informatization instructional leadership behavior.This is grounded in Theory of Planned Behavior (TPB) by [3] that blended teaching competence is one of technology skills, and computer self-efficacy is one of self-efficacy beliefs.Moreover, the research by [11] from information system research area has found that individual computer-related behaviors and attitudes are rooted in all or part of social cognitive theory (SCT).[23] found that computer self-efficacy positively affects individual cognition and behaviors.Other than this, it can be inferred from theory of planned behavior that blended teaching competence which is a kind of control belief and perceived facilitation believed behavior.This argument was identified by [44] that teachers' blended teaching competency is one of the important influencing factors in predicting teachers' informatization instructional leadership (TIIL).
[41] and [33] considered Smart PLS as one of the second-generation prominent software applications for Partial Least Squares Structural Equation Modeling (PLS-SEM), still treated by many as an emerging multivariate data analysis method.However, data analysis in related research of informatization instructional leadership still mainly adopts the first-generation techniques such as correlation analysis and regression analysis [44].[43] used AMOS-SEM to explore the impact of teacher information technology leadership on teaching efficacy in Chinese education context.However, there have been relatively few attempts to validate the instruments using PLS-SEM.
Based on the above review, prior to examining the interrelation between performance expectancy (PE), effort expectancy (EE), social influence (SC), facilitating conditions (FC), behavioral intention (BI), computer self-efficacy (CSE), blended teaching competence (BTC) and teachers' informatization instructional leadership (TIIL) among university teachers to explore which construct-al intention and employing informatization instructional leadership, this pilot study mainly attempted to examine the reliability and validity of the adapted, modified and translated scales by using PLS-SEM approach.SmartPLS was performed to examine the reliability and validity of scales in terms of three criteria: internal consistency reliability depending loading and average variance extracted (AVE), and discriminant validity including Fornell-Larcker criterion, cross-loadings and Heterotrait-Monotrait Ratio (HTMT).

Participants
Sample participants in current research were randomly selected based on cluster sampling technique from a population of nine private undergraduate universities in Shaanxi Province of China.According to the comprehensive ranking of private undergraduate universities from Chinese Ministry of Education, Chinese private universities are divided into four clusters.In this research, four private undergraduate universities were elected from four clusters because they have carried on blended teaching that is a necessary conditio leadership.The purposive sampling technique was used to exclude those private undergraduate universities which did not employ blended teaching.And then a random cluster sampling technique was used to select in-service teachers from different clusters of universities, which is to say, different cluster universities had the same probability of being chosen during the sampling process.A total of 60 in-service teachers were finally selected randomly for this study with 15 in-service teachers representing each of the four cluster universities.They are A representing Chinese top private universities, B representing Chinese first-class private university, C representing regional first-class private university and D representing regional well-known private university.

Instrument
Examining the Reliability and Validity of Measuring Scales related to Informatization Instructional Leadership Using PLS-SEM Approach Page 16 of 32 Table 1 shows the code and all items used in this research instruments.Use Expectancy (UE) instrument referred to the measurement scale from [8], which was initially formulated to measure pre-Beyond that, it also referred to a five-point Likert Scale (Wang, 2018).It consists of 5 items measuring sion and 5 items adapted to measure SI Scale to measure social influence and FC Scale to measure facilitating conditions were referred to and modified from a six point Likert scale [8] and a five-point Likert scale [40].
The CSE scale was adapted from [11].[11] 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.Based on the above scale, this study adapted the computer selfefficacy scale under the background of informatization teaching leadership in the blended teaching mode, including 5 questions, which is used to measure the level of university -efficacy.Blended Teaching Competence (BTC) Scale was developed by [15], referring to [28] which was originally to measure pre-service and incompetence.BTC Scale by [15] 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 environment.In this research, the Blended Teaching Competence Scale was modified and consisted of eight dimensions with a total of 32 items in terms of the pedagogy, management, assessment, and technology.The --Behavioral Intention (BI) is the mediating variable in this research model.BI Scale also referred to the scale to measure presystem [8] and a 5-point Likert scale [40].
The TIIL Scale was adapted from [44] that involved three dimensions with four items for each dimension and in current research modified it 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.The above seven scales all adapted, modified, and translated original scales into 11-point semantic differential scales starting from 0 (strongly disagree) to 10 (strongly agree) to fulfil the requirement of employing PLS-SEM approach to conduct data analysis in this research context.

Procedures
development center from 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 administrated during teacher routine meeting weekly in Wednesday afternoon.The survey questionnaires made via 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 Shaanxi Province of China by survey questionnaire via social media (i.e., QQ, We-chat) with the help of peer teachers.None of the respondents was forced to answer the questionnaire but voluntarily and anonymously Page 18 of 32 responded to questions.The respondents were also given adequate time 20 min to answer the questionnaire.

Data Analysis
Prior to using PLS-SEM to analyze data, it is crucial to screen the collected data to delete errors from missing value, suspicious response patterns, and outliers.It is essential to review and evaluate the statistical analysis in terms of the relation among items in the measurement model.In current research, the assessment of reliability and validity of the survey questionnaire is based on three important criteria (Table 2): internal consistency reliability, convergent validity, and discriminant validity [33].Internal consistency and composite reliability.The convergent validity of the instrument depends on Outer Loading (OL) and Average Variance Extracted (AVE) to be evaluated.Forenell Larcker Criterion, Cross-loading and Heterotrait-Monotrait ratio (HTMT) were assessed to evaluate the discriminant validity for each construct in seven scales.

RESULTS AND DISCUSSION
This study examined the reliability and validity of seven adapted scales based on the survey questionnaires and the results of findings are as follows.

Data Distribution
For the Kolmogorov-Smirnov normality test shown in Table 3, the significant level is reported to be .200(p > .05)for total UE, .008(p< .05)for total SI, .032(p < .05)for total FC, .024(p < .05)for total CSE, .028(p < .05)for total BTC, .034(p < .05)for total BI, and .200(p > .05)for total TIIL.Results show that the data is normally distributed for latent .whereas it is non-normal for the latent constructs social influence, facilitating conditions, computer self-efficacy, blended teaching competence, and behavioral intention.Nevertheless, nonnormal distribution is still suitable for using PLS-SEM to analyze data because PLS-SEM is a soft second-generation data analysis technique and modeling approach with less stringent criterion as compared to CB-SEM.It has no assumption towards the data distribution.However, CB-SEM requires the data to be normally distributed [33];.[41].[33] posited that three crucial criteria were used to assess reliability and validity of the survey questionnaire: internal consistency reliability, convergent validity, and discriminant validity.

Internal Consistency Reliability
Internal consistency reliability is the first criterion for evaluation of how all factors on the test relate to all other factors.In this research, the reflective measurement model for the instruments was being evaluated to measure the internal consistency reliability.
is the most conventional method used to show degree of internal consistency reliability measure in the first-generation statistical techniques.Alpha follows a principle that all factors intend to measure the same variable, then they are highly related and the value of alpha must be high.On the contrary, they are not related and the value of alpha must be low.
tends to underestimate the internal consistency reliability.It assumes all items have equal outer loading on the constructs.While composite reliability makes up the overestimate the internal consistency reliability.In addition, composite reliability takes into account the different outer loading of all items.
The matrix tab in Table 4 shows the composite reliability value showed as .Convergent Validity Convergent validity is used to measure the extent to which a measure correlates positively with alternative measure of the same construct.Outer loading is also referred as item/indicator reliability.Item loadings reflect the correlation between an item and its corresponding latent variable.Based on [33], Average Variance Extracted (AVE) indicates the degree in which the constructs explain its items/indicators.

Discriminant Validity
Discriminant validity measures the uniqueness of each construct to ensure it is distinct from other constructs in the structural model [33].Cross Loading indicates that the associated construct should be greater than any of its crossloadings on other constructs.Fornell-Larcker Criterion compares the square root of AVE value with the latent variable correlations.The construct is considered valid and distinct from other construct when t is greater than its highest correlation with any other construct [33].According to [34], HTMT approach is the mean value of all correlations 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-heteromethod correlations).The threshold value for HTMT is .90.Any HTMT value that is higher than .90 is considered as lack of discriminant validity.
Table 6 shows the results of the Fornell-Larcker criterion assessment with the reflective construct BI has a value of .743for the square root of its AVE.This value is higher than 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).As for the other reflective construct, they also have the highest values for the square root of their AVE values which are respectively greater than values in the same row and column.Thus, it can be concluded that based on research findings shown in Table 6 the discriminant validity has been established for all seven constructs.Similarly, items CSE_1, CSE_3, CSE_4, and CSE_5 also appeared to load high on its corresponding construct CSE but much higher on other constructs 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 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 each item of BI, BTC, FC, CSE, UE and TIIL.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.Mean-while, the number shown in the circular shape is the composite reliability for each construct.Nine 40, hence must be removed to meet the criterion of outer loading.Although composite reliability values for all items reach the acceptance minimum threshold of .60 in both figures, after item deletion, all composite reliability values reach a satisfying level.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.After assessment, there were a total of nine items eliminated from the original 77 items in the questionnaire.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.

Discussion
The current research obtained the five influencing factors to TIIL which are UE, SI, FC, CSE, BTC and BI based on the UTAUT model.Before achieving the result of the interrelation of the constructs in extended UTAUT model to explore contributing factors to TIIL, this pilot study examined above all the reliability and validity of research instruments by using the PLS-SEM approach.There is a scarcity of research adopting Smart PLS to validate instruments from multidimensional in spite of questionnaires developed in previous literature.Additionally, when the previous researches 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 , they and CFA values instead of EFA values.The limitation of using the first generation statistical analysis approach is lack of the instrument validation in multidimensional and easy to produce measurement error.Thus, this research adopts PLS-SEM statistical analysis approach to evaluate the validation of the instruments in terms of internal consistency reliability, convergent validity, and discriminant validity for each individual item of the instruments to reduce measurement error.Using PLS-SEM approach focuses on composite reliability with making up the deficiency of main emphasis on the when measuring internal consistency reliability of instruments by employing the first-generation statistical analysis approach.Hence, using more sophisticated second-generation approach to re-validate instruments tends to enhance the instrument precision in measuring specific constructs as the various perspectives of validation contributes to increase the accuracy of evaluating the instrument by using several indicators.

Reliability
In the studies by [8] and [40], the reliability analysis for developed PE Scale, EE Scale, SI Scale, FC Scale, and BI Scale only used one criteria which is , whereas current research used two criteria which are 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, which is to say, extended UTAUT model can be applied into the tion instructional leadership.This research combined CSE and BTC instruments development from the literature review by [11], by [15] with the Chinese university context.Through re-validating the CSE and BTC scales and deleting outer loadings with lower than .40, the composite reliability value and value all met criteria.Thus, this proved distinct internal consistency reliability has already been established in the field of TIIL.
As for TIIL scale, the TIIL Scale was developed according to Chinese literature review by [44] , but after reto fail loading so was deleted.This indicated it is essential for researchers to re-validate an adapted, modified TIIL scale although TIIL scale by [44] and adapted TIIL scale both used rationale behind it is because this research adopted PLS-SEM technique and [44] used AMOS-SEM technique.In other words, PLS-SEM and AMOS-SEM are both the secondgeneration approach, but they require different data assumption that PLS-SEM has no assumption towards the data distribution.In contrast, AMOS-SEM assumes the data to be normally distributed.This is a similarity to [41].

Validity
Outer loading and Average Variance Extracted (AVE) are two important criteria to assess the convergent validity of seven adapted scales.Compared before items deletion with after items deletion for outer loading and AVE in this research, It was evident from the findings that the assessment of convergent validity is very necessary for examining the correlation between an item and its corresponding latent variable, and for examining the degree in which .the constructs explain its items/indicators.Beyond that, based on results from Cross Loading, Fornell-Larcker Criterion, and HTMT, it proved that each of seven constructs had its uniqueness and it is distinct from other constructs in the structural model by deleting unloaded items to lead to an increase in Cross Loading, Fornell-Larcker Criterion, and HTMT.This is in line with [33] who mentioned in his study that all items deleted in HTMT were the same as the items deleted in cross-loading assessment, Fornellwas no contradiction issue emerged for the reliability and validity assessments.This from the above proved that validity assessment of seven adapted scales in PLS-SEM model was essential for this research.
Taken together, it also revealed after re-validating the developed UE Scale, SI Scale, FC Scale, and CSE Scale, BI Scale and TIIL Scale, they are reliable and validate to be used in the following investigation of interrelation between constructs to explore the contributing factors to TIIL.

CONCLUSION
This research explored status of Chinese university teachers carrying on informatization instructional leadership during COVID-19 from both theoretical and empirical perspectives, and extended UTAUT model by adding two new variables from the In terms of practice, this study suggests that CSE and BTC are also two important affecting factors to TIIL so they should be focused in the process of adopting TIIL.Most importantly, this research adopted PLS-SEM approach to re-validate seven modified scales which are used to respectively measure seven influencing factors.This enriched methodological the -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 conduct TIIL goals.
Due to the influence of CONVID-19, the research respondents are limited to in-service teachers from private undergraduate universities of the same province of China.The discussions in the article are limited to a study of representative states of China.Thus, the future study should extend the research population to the state-funded universities from the different provinces of China, and should discuss the representative states worldwide.On the other hand, whether the six influential factors i.e., UE, SI, FC, CSE, BTC and BI derived from UTAUT model are positively or significantly related to TIIL has not been verified, whether five influential factors (i.e., UE, SI, FC, CSE, BTC) have positive and significant effect on BI has not been confirmed, and whether mediating variable i.e., BI can mediate the relationship between five influential factors (i.e., UE, SI, FC, CSE, BTC) and TIIL also need to be verified, for this reason, the researcher attempts to use PLS-SEM to Examining the Reliability and Validity of Measuring Scales related to Informatization Instructional Leadership Using PLS-SEM Approach Page 22 of 32

Figure 1 .
Figure 1.Composite Reliability and Outer Loading before item deletion

Figures 3
Figures 3 and 4 display a comparison of the structural equation model for AVE and OuterLoading 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.After assessment, there were a total of nine items eliminated from the original 77 items in the questionnaire.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.

Figure 3 .
Figure 3. AVE and Outer Loading before item deletion

Figure 4 .
Figure 4. AVE and Outer Loading after item deletion

Table 1 :
Number of items in survey questionnaire

Table 2 :
Criteria for reliability and validity in PLS-SEM

Table 4 .
The internal consistency reliability of the instruments based on construct UE, SI, FC, CSE, BTC, BI and TIIL after item deletion.

Table 5 .
Research items of outer loading assessment

Table 7
Similarly, items UE_PE and UE_EE also load higher than other constructs each item of BI, BTC, SI, CSE, FC and TIIL.

Table 7 .
Cross loadings for the construct BI, UE, CSE, FC and SI

Table 8
shows the cross-loadings for each item reflected on latent construct BTC. Items BTC_ER, BTC_FSSI, BTC_FTSI, BTC_MBLE, BTC_PBA, BTC_PBAS, BTC_PI, BTC_TL load high on its corresponding construct BTC and also much higher on other constructs each item of BI, CSE, FC, SI, TIIL and UE.

Table 8 .
Cross loadings for the construct BTC Examining the Reliability and Validity of Measuring Scales related to Informatization Instructional Leadership Using PLS-SEM Approach

Table 9
displays the cross-loadings for each item reflected on latent construct TIIL.The each item of TIIL also load higher than other constructs each item of BI, BTC, SI, CSE, UE and FC.

Table 9 .
Cross loadings for the construct TIILAccording to Fornell-Larcker criterion and cross-loadings criterion, it can be concluded from the research findings shown from Table6to Table9that 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 exceeded the cross-loadings.Thus, this indicates the discriminant validity has been established for all seven constructs.The Heterotrait-Monotrait Ratio (HTMT) shown in Table 10 was the last criterion used to measure the discriminant validity.All the constructs fall under the maximum threshold value of .85.It illustrates 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 indicated the discriminant validity has been established.

Table 10 .
Heterotrait-Monotrait Ratio (HTMT)Comparison of Structural Equation ModelingFigure 1 and 2 display a comparison of the structural equation model for Composite Examining the Reliability and Validity of Measuring Scales related to Informatization Instructional Leadership Using PLS-SEM Approach Page 26 of 32