Dissertation

Constructs Cronbach’s

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Emmanuel FINAL SUBMISSION-2023

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• Constructs Cronbach’s alpha Composite reliability (rho_c)
• Paths β SE t -statistics

Constructs	Cronbach’s alpha	Composite reliability (rho_c)
Average variance extracted (AVE)
CDT	0.927	0.938	0.606
DET	0.946	0.952	0.561
MoT	0.884	0.904	0.516
TUDI	0.969	0.974	0.824
TUIP	0.894	0.900	0.533
TUSD	0.933	0.945	0.683
WET	0.918	0.932	0.634
ADIE	0.941	0.954	0.807
ATT	0.939	0.947	0.603
CTA	0.896	0.920	0.659
LET	0.968	0.973	0.837
TCT	0.942	0.952	0.691
WDE	0.974	0.977	0.779
DTA	0.954	0.963	0.814
PEU	0.934	0.947	0.696
QAP	0.924	0.936	0.677
UA	0.956	0.979	0.958

Source: Field Data (2023)

Table 4.23 presents the results of construct reliability and convergent validity for 17 constructs. The Cronbach’s alpha values for all constructs range from 0.884 to 0.974, indicating high internal consistency among the items in each construct. The composite reliability (CR; rho_c) values range from 0.900 to 0.979, showing that each construct has strong reliability, as all the values exceed the threshold of 0.7. The average variance extracted (AVE) values range from 0.516 to 0.958, and all are above the recommended threshold of 0.5. This demonstrates that the constructs have acceptable convergent validity, as they capture more than 50% of the variance in their respective items.

In summary, the results in Table 4.20 show that the constructs in the study have high reliability and convergent validity. This indicates that the measurement models are well-specified, and the constructs are suitable for use in further analysis. The high values of reliability and validity provide confidence in the robustness of the constructs, allowing for meaningful interpretation of results derived from these constructs in subsequent analyses (Hair & Alamer, 2022).
Table 4.24 Construct reliability and convergent validity

Constructs	Cronbach’s alpha	Composite reliability (rho_c)	Average variance extracted (AVE)
TUDI	0.969	0.974	0.824
MoT	0.884	0.904	0.516

The summary of Table 4.24 shows construct reliability and convergent validity. Results on the availability of digital infrastructure in education (TUDI) have the highest Cronbach’s alpha, composite reliability, and average variance extracted of 0.969, 0.974, and 0.824, respectively. However, mode of teaching (MoT) has the lowest Cronbach’s alpha, composite reliability, and average variance extracted of 0.884, 0.904, and 0.516, respectively
Table 4.25 Discriminant validity - HTMT

Path	HTMT	Path	HTMT
DET <-> CDT	0.781	ATT <-> ADIE	0.141
MoT <-> CDT	0.631	CTA <-> ADIE	0.643
MoT <-> DET	0.616	CTA <-> ATT	0.298
TUDI <-> CDT	0.620	LET <-> ADIE	0.594

Path	HTMT	Path	HTMT
TUDI <-> DET	0.663	LET <-> ATT	0.334
TUDI <-> MoT	0.698	LET <-> CTA	0.569
TUIP <-> CDT	0.484	TCT <-> ADIE	0.846
TUIP <-> DET	0.290	TCT <-> ATT	0.253
TUIP <-> MoT	0.348	TCT <-> CTA	0.732
TUIP <-> TUDI	0.295	TCT <-> LET	0.728
TUSD <-> CDT	0.811	WDE <-> ADIE	0.690
TUSD <-> DET	0.812	WDE <-> ATT	0.361
TUSD <-> MoT	0.665	WDE <-> CTA	0.762
TUSD <-> TUDI	0.813	WDE <-> LET	0.785
TUSD <-> TUIP	0.304	WDE <-> TCT	0.813
WET <-> CDT	0.830	PEU <-> DTA	0.573
WET <-> DET	0.793	QAP <-> DTA	0.480
WET <-> MoT	0.559	QAP <-> PEU	0.204
WET <-> TUDI	0.678	UA <-> DTA	0.584
WET <-> TUIP	0.561	UA <-> PEU	0.750
WET <-> TUSD	0.723	UA <-> QAP	0.160

Source: Field Data (2023)
Table 4.25 presents the discriminant validity results of the constructs using the Heterotrait-Monotrait (HTMT) ratio. The HTMT values range from 0.141 to 0.846. Generally, the values should be below 0.85 or 0.90, depending on the threshold considered appropriate, to establish discriminant validity. In this case, all the HTMT values are below 0.85, indicating acceptable discriminant validity between the constructs (Flake et al., 2022; Purwanto, 2021)
In summary, the results in Table 4.21 show that the constructs have adequate discriminant validity. This means that each construct measures a distinct concept and that they are sufficiently different from one another. The good discriminant validity supports the overall measurement models and provides confidence in the constructs’ ability to capture unique aspects of the research phenomena, enabling meaningful interpretation of relationships among them in further analyses.

1. How disruptive technologies, affect student learning environment and teaching strategies for skill training (students and teachers’ perspectives)

Table 4.26 Predicting attitude of students towards technology

Paths	β	SE	t-statistics	p-values	f2
ADIE => ATT	-0.221	0.092	2.410	0.016**	0.020
CTA => ATT	0.112	0.129	0.867	0.386	0.007
LET => ATT	0.172	0.068	2.529	0.011**	0.014
TCT => ATT	0.047	0.117	0.400	0.689	0.001
WDE => ATT	0.249	0.123	2.027	0.043**	0.018

Note: *p<0.10; **p<0.05; ***p<0.001
Table 4.26 and Figure 4.1 present the results of the effects of the statuses or standings of availability of digital infrastructure in education (ADIE), computer technology application (CTA), laboratory equipment for teaching (LET), technology for classroom training (TCT), and workshop equipment for training (WDE) on students’ attitudes towards technology based on the accounts of the students concerning these factors.

1. Status of digital infrastructure in education (ADIE) and students’ attitudes towards technology

The findings show a significant negative relationship between ADIE and ATT (β = -0.221; SE = 0.092; t = 2.410; p = 0.016; f² = 0.016), indicating that the poor status of ADIE in these institutions has a negative impact on students' attitudes towards technology. This finding supports the idea that students' ATT is negatively impacted by insufficient digital infrastructure in classrooms.
Several studies have shown in the literature how crucial digital infrastructure is in influencing how students view technology (e.g., (Ludvigsen & Steier, 2019; Marín et al., 2019). The results of this study support the conclusions of those other studies, highlighting the critical part that digital infrastructure plays in affecting students' perceptions. The theory of technology acceptance (Al-Rahmi et al., 2019), which contends that perceived ease of use and perceived utility are important determinants of users' attitudes towards technology, can be used to

explain the study's negative correlation between ADIE and ATT. In the context of this study, inadequate digital infrastructure may result in decreased perceived usability and convenience of use, which could result in students having negative views towards technology.

The research exhibits some limitations. First off, the results may not be generalizable to other fields or nations because the sample size and scope are restricted to mechanical engineering students in Ghanaian technical universities. Second, this study depends on student self-reported data, which might be biased towards social desirability. To address this problem, future study might include impartial evaluations of the calibre of the digital infrastructure and the attitudes of the students. Despite these weaknesses, this study sheds important light on how ADIE and ATT interact at Ghanaian technical universities. The finings support the hypothesis that the current state of ADIE has a significant negative impact on students' ATT, which can be drawn from the findings. This demonstrates the necessity for governments and educators to invest in enhancing digital infrastructure in order to encourage students at Ghanaian technical institutes to have more positive attitudes towards technology. Disruptive technologies can help these schools close the digital skills gap while preparing students for success in the dynamic engineering field.

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