System quality,technology acceptance model and theory of planned behaviour models: Agents for adopting blended learning tools

Abstract

There are declines in learning outcomes despite teacher’s efforts to attain the lesson goals and objectives. The application of technology in education paved way for constructive teaching and learning, which made the evaluative processes convenient. Hence, this study investigated system quality, technology acceptance model and theory of planned behaviour models as an agent to adopt blended learning tools. The sample used for the study was 1200 students from seven public higher institutions in Lagos State using a stratified sampling technique to select the students from their respective colleges. A seven-point Likert scale was used to collect data from the selected samples. SmartPLS3 was used to analyze the data using the bootstrapping method. It was found that system quality influences the perceived ease of use, perceived usefulness, attitude, subjective norms, perceived behavioural control towards intentions to use and culminated into actual usage of blended learning. Also, there is a strong mediating influence of system quality and the antecedents of technology acceptance model as against the theory of planned behaviour towards the actual usage of blended learning. Therefore, education institutions will have to create enabling classroom environment and quality blended learning tools which gives the facilitators the privilege to interact well with the learners and the subject matter without abrupt alteration of the lesson processes/procedures.

Keywords

System quality blended learning tools electrical installation and maintenance works technology acceptance model theory of planned behaviour

Introduction

There are declines in learning outcomes despite teacher’s efforts to attain the lesson goals and objectives. These declines are adduced to several factors ranging from teacher’s personality, classroom/school environment, learners’ personality and non – interactive instructional media (Falout et al., 2009; Hawlitschek and Joeckel, 2017). For effective learning outcome, Darling – Darling– Hammond et al. (2020) highlighted four major engaging areas that should be considered in the school system: supportive environmental conditions, productive instructional strategies, social and emotional learning strategies and system supports. Fortunately, some institutions have begun to implement the innovative practices which enhance teacher’s pedagogical skills so as to address most common challenges to ensure rigor and comparability (Lachan-Hache et al., 2012).

The adoption of technology and its application to education has paved way for constructive teaching and learning with considerable and convenient evaluative process. The choice of blended learning over the long known traditional teaching has shaped the learning outcome and could be seen as an essential element in education (Mozelius and Hettiarachchi, 2017). Blended learning plays a dual role by allowing the teachers to deliver their lesson and as well evaluate learners by using a creative and innovative method (Khan et al., 2012).

The successful use of blended learning tools in the classroom is reliant on the quality of the tools/system used. System quality is a measure of the information processed by the system; it entails the compatibility level of the hardware and the software (Al-Mamary et al., 2014). System quality also refers to the characteristics of the whole system, such as response time, completeness of functionalities, availability and reliability of the system, ability to handle large number of user requests in a timely manner, minimal interruptions or bottlenecks, and strong security measures in place to prevent security risks (Gan and Balakrishnan, 2017). Delone and McLean (2003) is of the opinion that higher system quality is expected to lead to higher user satisfaction and use, leading to positive impacts on higher outcome. Therefore, system quality affects the rate and consistency in which teachers and learners use blended learning tools to facilitate learning. The outcome of this research study will have both theoretical and practical implication which will stimulate the adoption of blended learning among electrical installation and maintenance work students in public higher institutions.

Electrical installation and maintenance work (EIMW) students are the ones under training in a skilled subject area that teaches industrial and domestic installation of electricity, rural electrification, electrical machines (motors and generators), inspection, testing and maintenance of electrical/electronic devices (Shodipe and Ohanu, 2021). Ohanu, Shodipe, Chukwu and Chukwuma (2020) stated that EIMW is an area of specialization in technical vocational education and training offered colleges of education (technical), polytechnics and universities in Nigeria, designed to impart knowledge and practical skills in electrical engineering trade area such as house wiring (conduit and surface), coil winding and re-winding, electrical gadgets repairs, installation and maintenance of electrical machines, battery charging, installation and maintenance of electric motors, rural electrification etc. The adoption of blended learning tools in EIMW in Nigeria is low (Kintu et al., 2017; Orji et al., 2021) as compared to the adoption of blended learning tools among vocational technical education and training in developed countries (Orji et al., 2021). This bridges the widening gap between teachers and learners and foster consistent interaction between them.

Despite the several rigorous researches on the application of blended learning to teaching and learning activities (Hongli et al., 2011; Qian 2011; Kintu et al., 2017; Ibrahim and Nat, 2019; Westerlaken et al., 2019), this study focused on the influence of system quality, technology acceptance model (TAM) and theory of planned behaviour (TPB) as an agent to adopt blended learning tools. Specifically, the research study tends to find out the influence of the constructs of TPB, TAM and C-TAM-TPB on the relationship between quality systems used and blended learning activities with verifiable evidences. A structural equation model (SEM) which is an advanced multivariate analysis method was adopted for the analysis in the study as it good and consistent approach to analyze a cause and effect relationship between factors (Hair et al., 2014). In this study, SEM allows several constructs of TAM and TPB to simultaneously predicted or expressed by the construct of system quality which will lead to cogent validities, conclusion, verifiable reliabilities of the results (Nachtigall et al., 2003).

Theoretical background and hypotheses

Blended learning

Blended learning is an integration of face – to – face and online component of education (Margolis, 2018). It is adopted to support interactions between students in the online mode and improve learning outcomes (Bojović, 2017; Islam et al., 2021). Blended learning did not only enhance teaching and learning process through online or mobile technologies but also bridge the gap between learning and working (Garrison and Kanuka, 2004; Vaughan, 2007; Bohle-Carbonell et al., 2013). Blended learning facilitates communication and collaboration among students and teachers through social networking, increases the ease of use of course materials (Wai and Seng, 2013), decrease physical class time, create a student-based learning environment, produce an encouraging learning environment, flexible learning time and location, promotes independent learning skills, and develops individually course solutions (Rahman et al., 2015; Siew-Eng and Muuk 2015). Littlejohn and Pegler (2007) and Zavyalova (2020) lay much emphasis that blended learning reduced the cost of student’s travel as compared to the traditional method of learning.

Blended learning is categorized into four major taxonomies which are: rotation model, flex model, self – blend model and enriched virtual model (Staker and Horn, 2012). Staker et al. (2011) explained blended learning by focusing on the learners rather than the instructors. This allows learners to learn at any time at a supervised brick and mortar location away from home and at least in part through online delivery with some element of student control over time, place and pace. Staker and Horn (2012) and Staker et al. (2011) explained rotational model as a program in which within a given course or subject, students rotate on a fixed schedule or at the teacher’s discretion between learning modalities, at least one of which is online learning. In flex model, content and instruction are delivered primarily by the Internet, students move on an individually customized, fluid schedule among learning modalities, and the teacher-of-record is on-site. The self – blend model describes a scenario in which students choose to take one or more courses entirely online to supplement their traditional courses and the teacher-of-record is the online teacher and the virtual model is a whole-school experience in which within each course, students divide their time between attending a brick-and-mortar campus and learning remotely using online delivery of content and instruction (Staker and Horn, 2012; Staker et al., 2011).

Blended learning tools are technology devices used as an effective mechanism for adequate lesson delivery (Castro, 2019; Basal, 2011). He explained three major importance of adopting technology into education as: increases the diversity of mechanism and modes in education, decreases barrier to education as a democratization mechanism and enhances individual control over one’s own education in terms of content, delivery mode and pace of learning.

Technology acceptance model

Technology acceptance model is a salient scientific model with several empirical evidences on the application of information technology which cut across many fields of study. Technology acceptance model found its root from TRA (Davis, 1989), with the prime objective to explain a clincher for the user to accept information technology, verifying the theory and explaining the users’ behaviour. (Chen and Chen, 2009). Technology acceptance model focuses on either information technology (IT) systems in use by individuals or systems with which users are familiar; however, TAM’s ability to predict the inexperienced user’s behaviour and the differences between experienced and inexperienced users in terms of use behaviour is unclear (Taylor and Todd, 1995a; Ho et al., 2013).

Technology acceptance model has two main constructs (perceived ease of use and perceived usefulness) (Moon and Kim, 2001). Perceived ease of use is the extent to which a perspective user expects that using a system is free from effort while perceived usefulness is the subjective probability that using a particular system enhances job performance (Davis et al., 1989; Hsiao and Tang, 2013). Technology acceptance model posit that perceived ease of use and perceived usefulness will influence the formation of favourable attitude associated with the use of a technology system which in conjunction with perceived usefulness will generate individuals’ behavioural intention to use the technology system. Also, perceived ease of use is expected to have a positive influence on individuals’ perceptions regarding the usefulness of the technology system (Ma et al., 2017; Davies et al. 1989)

Technology acceptance model constructs are actual usage, behavioural intentions to use, attitude, perceived ease of use and perceived usefulness (Venkatesh and Davis, 2000). Taylor and Todd (1995b) emphatically describe the model constructs in a way that clarify their functions. Perceived ease of use have a direct linkage to perceived usefulness, perceived ease of use and perceived usefulness have a direct linkage with attitude towards usage but having an indirect linkage with behavioural intentions to use, users attitude directly linked with an ideal to influence behavioural intentions to use, behavioural intentions to use direct linked but plays a mediating role between actual usage and other constructs of TAM and actual usage which is motivated by the conditions of the other construct. Davis et al. (1989) proposed the effect of exogenous variables to enhance the robustness of TAM and found a strong correlation between user intentions, self – reported usage with perceived usefulness owing to a great influence on the users’ intentions as it was proposed in our model in Figure 1. As a result of divergent opinions and users’ perception of IT and based on empirical review of previous studies of Rahiet al. (2017); Lu et al. (2003); Hu et al. (1999); Szajan, 1996) on TAM, we hypothesized that:

1. Student’s behavioural intention influences their actual usage of blended learning tools.

2. Student’s perceived ease of use influences their actual usage of blended learning tools.

3. Student’s perceived ease of use mediates the relationship between behavioural intentions and actual usage blended learning tools.

4. Student’s perceived ease of use mediates the relationship between their attitude and behavioural intentions towards use of blended learning tools.

5. Student’s perceived ease of use enhances their perceived usefulness of blended learning tools.

6. Student’s perceived usefulness enhances their attitude towards the adoption of blended learning tools.

7. Student’s perceived usefulness mediates the relationship between attitude and behavioural intentions to use blended learning tools.

8. Student’s perceived usefulness influences their behavioural intentions to adopt blended learning tools.

9. Student’s perceived ease of use influences their behavioural intentions to adopt blended learning tools.

10. Student’s perceived ease of use influences their attitude towards the adoption of blended learning tools.

11. The relationship between student’s perceived usefulness and behavioural intentions is mediated by perceived ease of use of blended learning tools.

12. The relationship between student’s perceived usefulness and attitude is mediated by perceived ease of use of blended learning tools.

Figure 1.

Theoretical framework and hypotheses.

Theory of planned behaviour

The theory of planned behaviour (Ajzen, 1991), is an extension of theory of reasoned action (TRA) (Ajzen and Fishbein, 1975). It parsimoniously gives an explanation about the motivational influences on behaviour (Conner and Armitage, 1998). It also predicts non-volitional behaviour by incorporating perceptions of control over performance of behaviour as an additional predictor (Ajzen, 1991). TPB holds a tentative statement of fact that intentions to perform a behaviour is founded on: attitude, subjective norms and perceived behavioural control (Ajzen, 1991). Attitude is the “degree to which a person has a favourable or unfavourable evaluation or appraisal of certain behaviour (Ajzen, 1991). It is an individual’s disposition to react with a certain degree of favourableness or unfavourableness to an object, behaviour, persons or an event (Ajzen, 1993). Subjective norm is the social component in the TPB model which speaks to a person’s perception of how significant others view his/her behaviour in question and whether or not they would endorse the practice. Significant others can include persons such as family members and friends whose support or lack thereof of a particular behaviour can influence intentions to act or not to act (Fishbein and Ajzen, 1985; Carpenter and Reimers, 2005). Perceived behavioural control can be viewed as an individual’s perception of ease or challenging it may be to perform the behaviour (Ajzen, 1991). Ajzen (2002) defined perceived behavioural control as the “perceived ease of use of performing the behaviours based on past experiences and anticipated impediments.”

Krueger and Carsrud (1993) postulated that exogenous factors/variables could serve as antecedents of behaviour or influence the intentions – behaviour relationship. In Figure 1, system quality serves as an exogenous variable that influences behavioural antecedents and stimulates the intention – behaviour relationship towards IT usage. Empirically, compatibility, peer influence, superior’s influence, self – efficacy, resource facilitating condition, technology facilitating condition, trust has been used to test the strength of TPB model (Taylor and Todd, 1995a; Wu and Chen, 2005) but this study adopted system quality as an exogenous variable that will enhance TPB model. Hence, we hypothesized that:

13. Student’s attitude influences their behavioural intentions to adopt blended learning tools.

14. Student’s subjective norms influence their behavioural intention to adopt blended learning tools.

15. Student’s perceived behavioural control enhances their behavioural intentions to adopt blended learning tools.

16. Student’s subjective norms mediate the relationship between behavioural intentions and actual usage of blended learning tools.

17. Student’s perceived behavioural control mediates the relationship between behavioural intentions and actual usage of blended learning tools.

18. Student’s subjective norms influence their actual usage of blended learning tools.

19. Student’s perceived behavioural control influence their actual usage of blended learning tools.

System quality and C-tam-tpb

The two models have been used to explain the factors that influence behavioural intentions in many studies of IT adoption and usability (Ramayah et al., 2009). Theory of planned behaviour looked into three antecedents that enhance intentions while TAM was also channeled in the same course but contained different antecedents fused to enhance behavioural intentions (Nadlifatin et al., 2020). Taylor and Todd (1995a) held that TAM failed to include factors of society and control that have been proven to affect actual behaviours. The two factors are also key factors in TPB. As a result, Taylor and Todd (1995b) integrated TAM and TPB to include subjective norm and perceived behavioural control into technology acceptance models and proposed C – TAM – TPB.

As earlier explained, this research study is on the influence of exogenous variables on TAM and TPB models. System quality was adopted as an independent variable with strong exogenous influence on the adoption, flexibility and usability of blended learning tools. A system displaying high data quality and system quality can lead to net benefits for individuals, group of individuals and institutions (Seddon, 1997). It can give user’s an understanding of the decision context, increase decision making productivity and change how certain tasks is performed (Wixom and Watson, 2001). System quality is a desirable characteristic of an information system which involves ease of use, flexibility, reliability, ease of learning, intuitiveness, sophistication and response time (Petter et al., 2008; Al-Mamary et al., 2014). System quality is the quality of the information system processing itself which includes software and data components and it is a measure of the extent to which the system is technically sound (Gorla et al., 2010). It is an important factor to be considered in the selection of IT tools to be used in the classroom, as good and quality IT system may foster users’ behaviour to use and poor quality may hinder lasting and adequate usability. From the personnel perspective, system quality is thought to be an important motivating factor for people to use their systems and derive any benefits essential for organizations to gain a return on their investment (Rai et al., 2002; Guimaraes et al., 2017).

The relationship between system quality and C-TAM-TPB cannot be neglected as long as IT application is concerned, although previous studies have discussed the relationship between TAM, TPB, C–TAM–TPB and other variables (Taylor and Todd, 1995b; Wu and Chen, 2005; Chen and Chen, 2009), C-VBN-TPB (Chen, 2020), C-TPB-EEM (Sharahiley, 2020) but few has discussed the relationship between system quality and C – TAM – TPB. This ideal formed the basic objective of this research study. Hence, we hypothesized that:

20. System quality mediates the relationship between student’s perceived ease of use and student’s perceived usefulness of blended learning tools.

21. System quality influences student’s perceived ease of use of blended learning tools.

22. System quality mediates the relationship between student’s perceived usefulness and attitude towards the usage of blended learning tools.

23. System quality influences student’s subjective norms towards the usage of blended learning tools.

24. System quality enhances student’s perceived behavioural control towards the usage of blended learning tools.

25. System quality influences student’s attitude towards the usage of blended learning tools.

26. System quality influences student’s perceived usefulness of blended learning tools.

Methodology

This study was conducted among students who study electrical installation and maintenance work in colleges of education (technical), polytechnics and universities in Lagos State in the south west region of Nigeria. The area of focus is suitable for the study because this category of students has had the privilege to use blended learning tools at some stages in their learning institutions.

Sample

The sample selected for the study was 1200 Electrical Installation and Maintenance Work students; this was as a result of the accurately filled instrument for data collected from the 1500 copies initially produced and administered. This gave 80% return rate of the administered instrument. A stratified sampling technique was used to select sample from the technical vocational education and training institutions which gave the opportunity for the selection of students from seven TVET institutions from the zone. Among these respondents 980 (81.67%) were male, 220 (18.33%) were female, 780 (65%) were in their final year, 300 (25%) were in their second year and 120 (10%) were in their first year. The average age of the respondents falls between 18 – 22 years.

Instrument for data collection

The instrument used for data collection was adapted from various previous research works to suit the purpose of the study. The instrument contained four segments that addressed the demography, technology acceptance model constructs, theory of planned behaviour and system quality. The constructs contained are perceived usefulness (PU) – 4 items (Venkatesh and Davis, 1996, 2000; Pavlou, 2003), perceived ease of use (PEOU)– 4 items (Venkatesh and Davis, 1996, 2000; Wu and Chen, 2005), attitude (AT) – 4 items (Bhattacherjee, 2000; Ajzen, 1991), subjective norm (SN) – 3 items (Taylor and Todd, 1995a; Bhattacherjee, 2000; Ajzen, 1991), perceived behavioural control (PBC) – 3 items (Bhattacherjee, 2000; Ajzen, 1991), behavioural intention to use (BI)– 3 items (Venkatesh and Davis, 1996, 2000; Taylor and Todd, 1995a), actual usage (AU) – 4 items (Doll and Torkzadeh, 1998) and system quality (SQ)– 7 items (Alsabawy et al., 2016). The instrument was measured on a seven point Likert scale of 7(Strongly agree) – 1(Strongly disagree).

Data analysis

The instrument for data collection was distributed to two experts from the field of Computer Education and Robotics from the Faculty of Vocational Technical Education, University of Nigeria, Nsukka and Education Technology Department, Federal College of Education (Technical) Akoka who checked the content structure of the instrument. Their corrections were used to produce the final copies of the questionnaire distributed for data collection. The data collected for the study was analyzed using Partial Least Square analysis technique with SmartPLS 3.0 software (Ringle et al., 2015). The analysis of the data was done with measurement model testing (validity and reliability of the construct), structural assessment model and hypotheses testing based on Hair et al. (2014) recommendations.

Measurement model testing

Measurement of the various items of the constructs was tested to ascertain its validities and reliabilities. This is the stage of measurement modeling where Cronbach alpha reliability, convergent validity and composite reliability were ascertained as recommended (Lee et al., 2007). The process shows the extent to which the measured items fit appropriately into the model (Henseler et al., 2009).

The cross - loading factors of the observed variable as shown in Table 1 indicates the validity and reliability of the study. The convergent validity of the study was assessed with three cogent criteria. The factor loadings sufficiently meet the recommendation of 0.5 and above (Hair et al., 2009) while loadings below the recommended value were eliminated from further analysis. Also, the composite reliability for all the constructs should exceed 0.70 and the average variance extracted (AVE) for the constructs should be or surpass the recommended cut-off 0.50 (Fornell and Larker, 1981).

Table 1.

Cross loading of the observed variables.

	At	AU	BI	PBC	PEOU	PU	SN	SQ
AT1	0.512	0.225	0.262	0.285	0.236	0.311	0.193	0.192
AT2	0.762	−0.074	0.303	0.195	0.450	0.367	0.331	0.279
AT3	0.671	0.133	0.334	0.399	0.270	0.281	0.447	0.385
AT4	0.610	0.011	0.162	0.211	0.308	0.284	0.279	0.218
AU1	−0.017	0.770	−0.024	0.082	−0.091	0.053	−0.073	0.039
AU2	0.073	0.829	−0.094	0.026	−0.012	0.124	−0.040	−0.061
AU3	0.151	0.822	0.059	0.180	0.034	0.203	0.150	0.086
BI1	0.352	−0.017	0.859	0.471	0.330	0.313	0.273	0.419
BI2	0.385	−0.003	0.906	0.475	0.457	0.442	0.432	0.468
PBC1	0.348	0.105	0.428	0.761	0.379	0.482	0.402	0.459
PBC2	0.339	0.073	0.319	0.659	0.404	0.489	0.319	0.385
PBC3	0.231	0.103	0.703	0.728	0.200	0.165	0.261	0.448
PEOU1	0.422	0.014	0.369	0.277	0.762	0.430	0.435	0.434
PEOU2	0.436	−0.116	0.317	0.381	0.785	0.388	0.296	0.364
PEOU3	0.399	0.048	0.338	0.334	0.724	0.281	0.384	0.329
PU1	0.306	0.138	0.218	0.252	0.351	0.635	0.240	0.187
PU2	0.461	0.152	0.396	0.481	0.467	0.873	0.341	0.427
PU4	0.356	0.102	0.378	0.388	0.448	0.825	0.290	0.435
SN1	0.364	−0.097	0.306	0.277	0.355	0.241	0.650	0.386
SN2	0.276	−0.030	0.189	0.322	0.331	0.218	0.676	0.387
SN3	0.405	0.146	0.360	0.366	0.382	0.331	0.820	0.446
SQ1	0.235	−0.068	0.459	0.368	0.228	0.076	0.141	0.502
SQ2	0.319	0.065	0.448	0.458	0.380	0.317	0.388	0.672
SQ3	0.178	−0.127	0.446	0.392	0.335	0.261	0.322	0.670
SQ4	0.324	−0.104	0.460	0.453	0.401	0.318	0.376	0.704
SQ5	0.250	0.098	0.215	0.285	0.136	0.265	0.468	0.648
SQ6	0.318	0.205	0.478	0.497	0.324	0.380	0.429	0.742
SQ7	0.280	0.048	0.419	0.398	0.430	0.408	0.442	0.702

Figure 2 shows the Path coefficient, factor loading and R² values explaining the relationship among attitude, behavioural intentions, perceived behavioural control, perceived ease of use, perceived usefulness, and subjective norms.

Figure 2.

Model path coefficient.

The convergent validity was further ascertained with an extract of AVE. The AVE of the constructs are above the cut – off of 0.50 with a composite reliability of the constructs above the cut – off of 0.70, with the exception of attitude (AVE = 0.416) and system quality (AVE = 0.427) as shown in Table 2. Despite the low average variance of the two constructs, their convergent validities were found to be adequate with composite reliabilities greater than 0.7 (Fornell and Larcker, 1981). Also, the items load sufficiently (factor loadings above 0.5) on their underlying constructs to confirm convergent validity (Ahire and Devaraj, 2001; Tabachnick and Fidell, 2007; Du et al., 2012). The Cronbach alpha coefficient obtained for the construct varied from 0.5 to 0.7 which is an indication of an appreciative move from a poor to an acceptable level of internal coefficient of the constructs (Namdeo and Rout, 2016).

Table 2.

Average variance, convergent validity and cronbach alpha.

Latent variables	Cronbach’s alpha	Composite reliability	Average variance extracted
AT	0.529	0.736	0.416
AU	0.738	0.849	0.652
BI	0.718	0.876	0.779
PBC	0.544	0.760	0.515
PEOU	0.632	0.801	0.574
PU	0.684	0.825	0.615
SN	0.538	0.761	0.518
SQ	0.774	0.837	0.427

The discriminant validity is the measure of the cross loading of the constructs correlation matrix and the square root of the average variance explained (Ngah et al., 2015; Lowry and Gaskin, 2014). Table 3 indicated that the square root of the AVE in the study is higher than the cross loadings on the other constructs in the model. This is an indication of good discriminant validity.

Table 3.

Inter construct correlation and discriminant validity.

Constructs	1	2	3	4	5	6	7	8
Attitude	0.645
Actual usage	0.092	0.808
Behavioral intentions	0.419	−0.010	0.883
Perceived behavioral control	0.413	0.133	0.710	0.717
Perceived ease of use	0.553	−0.025	0.452	0.432	0.758
Perceived usefulness	0.483	0.164	0.434	0.491	0.542	0.784
Subjective norms	0.490	0.032	0.407	0.449	0.494	0.374	0.720
System quality	0.421	0.040	0.636	0.651	0.502	0.465	0.622	0.654

Structural assessment model

In this segment, the proposed model, measures and the tentative statement of hypothesis are further verified. The lateral collinearity test (VIF), coefficient of determination (R²), cross – validated redundancy (Q²), path coefficient and the effect size (f²) were considered as recommended by Hair Jr et al. (2016) to assess the structural equation model.

The standard validity and reliability test do not properly reveal lateral collinearity (Kock and Lynn, 2012). The lateral collinearity test (VIF) in a research study shows whether the independent variables in a model are highly and significantly correlated (Draper and Smith, 2003). The VIF threshold recommended in this study was 3.3 (Cenfetelli and Bassellier, 2009) i.e. a value greater than the threshold is considered indicative of collinearity and otherwise if lesser than the threshold. Hence the lateral collinearity of the study ranged from 1.00 – 2.17. The result presented in Table 4 showed that the latent and independent variables are less than the recommended threshold and implies that result is free from lateral multicollinearity (Hair et al., 2014).

Table 4.

Effect size (f2) and lateral collinearity test (VIF).

Hyp	Model path	Effect size (f²)	Lateral collinearity test (VIF)	Decision
1	BI -> AU	0.01	1.00	Small effect
2	PEOU -> AU	0.11	1.19	Small effect
5	PEOU -> PU	0.00	1.01	Small effect
6	PU -> AT	0.02	1.18	Small effect
8	PU -> BI	0.02	1.98	Small effect
9	PEOU -> BI	0.01	2.10	Small effect
10	PEOU -> AT	0.01	1.58	Small effect
13	AT -> BI	0.02	1.20	Small effect
14	SN -> BI	0.01	1.00	Small effect
15	PBC -> BI	0.13	1.00	Medium effect
18	SN -> AU	0.00	1.00	Small effect
19	PBC -> AU	0.36	1.15	Large effect
21	SQ -> PEOU	0.39	1.22	Large effect
23	SQ -> SN	0.37	1.00	Large effect
24	SQ -> PBC	0.21	1.00	Medium effect
25	SQ -> AT	0.02	1.20	Small effect
26	SQ -> PU	0.09	1.37	Small effect

Hypothesis testing

A bootstrapping method was used to calculate the various estimates to enable the acceptance or rejection of the stated hypothesis. The result yielded various parameters of t – statistics, p – value, original sample and sample mean. A limit bound threshold t – value of 1.96 and its equivalent p - value less than 0.05 which indicate that the hypotheses are accepted at 95% confidence interval (Kock, 2016). Figure 3 and Table 5 showed the result of the path coefficients and decisions on the various constructs. Therefore, H5, H6, H9, H10, H13, H15, H19, H21, H23, H24, H25 and H26 are significant and accepted as having a direct effect on their dependent variables.

Figure 3.

Structural model of the construct.

Table 5.

Result of the direct effect of the constructs.

Hyp	Path	Coefficient	Std. error	T values	p values	Decision
1	BI -> AU	−0.19	0.06	3.16	0.01	Accepted
2	PEOU -> AU	−0.07	0.06	1.54	0.12	Rejected
5	PEOU -> PU	0.41	0.04	10.06	0.00	Accepted
6	PU -> AT	0.22	0.03	6.73	0.00	Accepted
8	PU -> BI	0.03	0.03	0.85	0.18	Rejected
9	PEOU -> BI	0.12	0.02	4.85	0.01	Accepted
10	PEOU -> AT	0.37	0.03	11.95	0.00	Accepted
13	AT -> BI	0.07	0.03	2.29	0.02	Accepted
14	SN -> BI	0.03	0.03	1.40	0.16	Rejected
15	PBC -> BI	0.60	0.03	18.26	0.00	Accepted
18	SN -> AU	0.01	0.01	1.15	0.20	Rejected
19	PBC -> AU	0.29	0.13	2.14	0.03	Accepted
21	SQ -> PEOU	0.50	0.02	21.51	0.00	Accepted
23	SQ -> SN	0.62	0.02	28.27	0.00	Accepted
24	SQ -> PBC	0.65	0.02	29.06	0.00	Accepted
25	SQ -> AT	0.13	0.03	4.98	0.00	Accepted
26	SQ -> PU	0.26	0.05	5.76	0.00	Accepted

To examine the mediation effect, Preacher and Hayes (2008) procedure was applied with an examination of the indirect effect of the various constructs (Hair et al., 2014). The threshold limit of t – value and p – value as recommended was applied (Kock, 2016). Table 6 showed the mediating effect of the constructs. Therefore, H4, H7, H12, H20 and H22 are significant and are accepted as mediations.

Table 6.

Mediation effect of the constructs.

Hyp	Path	Coefficient	Std. error	T values	p values	Decision
3	BI -> PEOU -> AU	−0.01	0.01	1.48	0.14	Rejected
4	AT -> PEOU -> BI	0.03	0.01	2.37	0.02	Accepted
7	AT -> PU -> BI	0.02	0.01	2.10	0.04	Accepted
11	PU -> PEOU -> BI	0.01	0.01	1.25	0.18	Rejected
12	PU -> PEOU -> AT	0.16	0.08	1.98	0.03	Accepted
16	BI -> SN -> AU	−0.00	0.00	1.07	0.29	Rejected
17	BI -> PBC -> AU	−0.06	0.04	1.60	0.11	Rejected
20	PEOU -> SQ-> PU	0.21	0.02	9.78	0.00	Accepted
22	PU -> SQ-> AT	0.06	0.01	4.09	0.00	Accepted

Figure 3 shows the results of the path coefficients on the various constructs, which are defined by H2, H6, H7, H10, H14, H16, and H17.Table 7

Table 7.

Coefficient of determination (R²) and cross – validated redundancy (Q²).

Endogenous variable	Coefficient of determination (R²)	Cross – validated redundancy (Q²)	Decision
Attitude	0.36	0.31	Moderate/predicted
Actual usage	0.04	0.41	Weak/predicted
Behavioral intentions	0.54	0.36	Substantial/predicted
Perceived behavioral control	0.42	0.25	Substantial/predicted
Perceived ease of use	0.25	0.12	Moderate/predicted
Perceived usefulness	0.34	0.08	Moderate/predicted
Subjective norms	0.38	0.02	Moderate/predicted
System quality	—	—

The average variance explained (R²) for endogenous variables should be greater than or equal to 0.10 to be adequate (Falk and Nancy, 1992) but Chin (1998) was emphatic that R² should have a threshold of 0.67 (substantial), 0.33 (moderate) and 0.19 (weak). Hence the R² value explained in Figure 2 for attitude, behaviour intentions, perceived behavioural control, perceived ease of use, perceived usefulness and subjective norms are moderate and substantial but actual usage is weak with the R² = 0.043. Also, the effect size (f²) for each path in the model was determined with a threshold of 0.02(small), 0.15 (medium) and 0.35 (large) (Cohen, 1988; Hair Jr, et al., 2014). The cross – validated redundancy (Q²) measures the inner model’s predictive relevance. Q² greater than 0 for particular endogenous variables is an indicator that the path model’s predictive are relevant (Rigdon, 2014; Sarstedt et al., 2014). The Q² for the endogenous variables in this study are greater than 0 which indicates that endogenous variable predicted in the model are relevant.

Discussion of the findings

The purpose of the study was to examine system quality, TAM and TPB as an agent to adopt blended learning tools. System quality as an exogenous variable played a major role that significantly and substantially predicts electrical installation and maintenance work student’s perceived ease of use and perceived behavioural control, likewise, moderately predicts their subjective norms, attitude and perceived usefulness to use blended learning tools. It motivates the electrical installation and maintenance work students with the intentions to either use blended learning tools or not, to enhance teaching and learning activities in the classroom.

The result of the study showed that there is a significant influence of behavioural intentions on actual usage of blended learning tools. Hence, H1 is accepted. The result of the study is consistent with previous literature on intention – actual usage relationship (Shih and Huang, 2009; Arlt et al., 2011; Alleyne and Lavine, 2013). This is an indication that the result of the study supports that intentions is a strong predictor of actual usage (Ajzen, 1991). Also there is an insignificant influence of perceived ease of use on actual usage of blended learning tools. Therefore, H2 is rejected. The result of the study is inconsistent with previous studies that found significant relationship (Ngai et al., 2007; Lu et al., 2010; Letchumanan and Tarmizi, 2011). The result of the study showed an insignificant mediation effect of perceived ease of use on the relationship between behavioural intentions and actual usage of blended learning tools. This makes H3 to be rejected. The result of the finding showed that intentions influence actual usage of blended learning tools but mediating the mediation of PEOU on their relationship reduced the predictive ability of intentions on actual usage. Likewise, there is a significant mediating influence of perceived ease of use on the relationship between attitude and behavioural intentions to use blended learning tools. Therefore, H4 is accepted. The result of the finding indicated a reasonable relationship between attitude and intentions to use blended learning tools. PEOU increases the predictive effective of attitude on intention with significant mediation. Hence, PEOU will not only influence their attitude but also their intentions. Also, there is a significant influence of perceived ease of use on perceived usefulness of blended learning tools, which allowed H5 to be accepted. This result is consistent with previous studies that found a significant relationship between PEOU and PU (Abdullah et al., 2016; Chen and Tung, 2007; Van de Heijden, 2003). There is a significant influence of perceived usefulness on student’s attitude to use blended learning tools. This allowed H6 to be accepted. The result of the study found credence in the result of some previous studies (Van de Heijden, 2003; Ngai et al., 2007; Davis, 1993). There is a significant mediating influence of perceived usefulness on the relationship between attitude and student’s behavioural intentions. Hence, H7 is accepted. The result of the finding showed that there is a strong mediation influence of perceived usefulness on attitude and intention. This is an indication that attitude predicts intentions through perceived usefulness. Also, perceived usefulness has an insignificant influence on behavioural intentions to use blended learning tools which allowed H8 to be rejected. Hence, the result of the study does not align with previous studies that found a significant relationship between the constructs (Chang and Tung, 2007; Venkatesh and Davis, 2000). Morealso, there is a significant influence of student’s perceived ease of use on their attitude towards the use of blended learning tools. This allowed H9 to be accepted. The result of the findings is concordance with previous literature of the constructs of TAM (Van de Heijden, 2003; Venkatesh and Davis, 2000). There is a significant influence of perceived ease of use on student’s attitude to use blended learning tools. Therefore, H10 is accepted. The result of the findings is consistent with some previous literatures (Renny et al., 2013; Van de Heijden, 2003; Ngai et al., 2007). There is an insignificant mediating influence of perceived ease of use on the relationship between perceived usefulness and student’s behavioural intention to use blended learning tools. Hence, H11 is rejected. The result of the findings showed that intention is not influenced by perceived usefulness of blended learning tools. Likewise, the link between perceived usefulness and intention is not predicted by perceived perceived ase of use. This is an indication that the way users’ perceived the usefulness or ease of use of blended learning tools do not influence their intentions. Also, there is a significant mediating influence of perceived ease of use on perceived usefulness and student’s attitude towards the use of blended learning tools. Therefore, H12 is accepted. The result of the findings showed that perceived usefulness influenced the attitude towards the intentions to use blended learning tools. The attitude and intention relationship is strongly mediated by perceived ease of use. This implies that attitude through ease of use will increase intentions to use blended learning tools.

There is a significant influence of attitude on student’s behavioural intentions to use blended learning tools. This allowed H13 to be accepted. The result of the findings found credence in previous literature (Lin, 2010; Letchumanan and Tarmizi, 2011; Stoel and Lee, 2003). Also, subjective norms have an insignificant influence on student’s behavioural intentions to use blended learning tools, therefore, H14 is rejected. This result of the findings is consonance with some previous literature (Asadi and Abdekhoda, 2019; Trafimow, 2000; Suki and Suki, 2017). This is an indication that respondents believed that encouragement from people who are important to them only minimally influence their intention to use blended learning tools. Likewise, they were less likely to appreciate the sentiment of those whose opinion they value regarding the usefulness of the tools (Kim et al., 2009; Suki and Suki, 2017). Student’s perceived behavioural control significantly influenced their behavioural intentions to use blended learning tools. Hence, H15 is accepted. The result of the findings aligns with the result from previous studies on the relationship between perceived behavioural control and behavioural intentions (Lin, 2010; Richard and Meuli, 2013). There is no significant mediation influence of subjective norms on the relationship between student’s behavioural intention and actual usage of blended learning tools. Therefore, H16 is rejected. The result of the findings showed that there is a strong relationship between intentions to use and actual usage of blended learning toolls but this relationship is not mediated by their subjective norms. There is no mediating influence of perceived behavioural control on the relationship between student’s behavioural intention and actual usage of blended learning tools. Hence, H17 is rejected. The result of the findings showed that there is a strong relationship between intentions to use and actual usage of blended learning tools but the relationship is not mediated by their perceived behavioural control. There is no significant influence of student’s subjective norms on actual usage of blended learning tools. Therefore, H18 is rejected. The result of the findings is inconsistence with some previous studies that found otherwise (Alzahrani et al., 2016; Salleh and Laxman, 2014; Hsu and Lu, 2007; Lee and Tsai, 2010). But, there is a significant influence of student’s perceived behavioural control on their actual usage of blended learning tools. This allowed H19 to be accepted. The result of the findings is consistent with the findings in previous literature where perceived behavioural control has strong positive influence on actual usage (Gopi and Ramayah, 2007; Yulihasri, 2004; Taylor and Todd, 1995a)

Also, there is a significant mediating influence of system quality on the relationship between student’s perceived ease of use and perceived usefulness of blended learning tools. Therefore, H20 is accepted. The result of findings indicated a strong relationship between perceived ease of use and perceived usefulness. Hence, the relationship is strongly mediated by system quality. System quality influence student’s perceived ease of use of blended learning tools. Hence, H21 is accepted. The result of the finding is coherent with previous studies that found significance between TAM constructs and their exogenous variables (Abdullah et al., 2016; Al-Mamary et al., 2014; Al – Gahtani, 2016). There is a significant mediation influence of system quality on the relationship between student’s perceived usefulness and attitude towards the adoption of blended learning tools. This allowed H22 to be accepted. The result of the findings indicated a strong relationship between perceived usefulness and attitude. The relationship is mediated by system quality which implied that users’ attitude could be predicted when quality blended learning tools is perceived. There is a significant influence of student’s system quality on their subjective norms to use blended learning tools. Hence, H23 is accepted. The result of the findings is in consonance with existing literatures where system quality had significant relationship with subjective norms (Grimes and Marquardson, 2019; Al – Nawafleh, et al., 2019). There is significant influence of system quality on student’s perceived behavioural control to use blended learning tools. Therefore, H24 is accepted. The result of the finding is line with previous literature where system quality and perceived behavioural control had significant relationship (Grimes and Marquardson, 2019; Al – Nawafleh et al., 2019). There is a significant influence of system quality on student’s attitude to use blended learning tool which allowed H25 to be accepted. The result of the finding is in consonance with previous literatures that found system quality as a strong predictor of users’ attitude (Kleijnen et al., 2004; Yun, 2013). Also, there is a significant influence of system quality on student’s perceived usefulness of blended learning tools. Therefore, H26 is accepted. The result of the finding is consistent with previous literature that found significant relationship between perceived usefulness and some exogenous variables (Abdullah et al., 2016; Pituch and Lee, 2006; Lau and Woods, 2008; Lee et al., 2011; Abbad et al., 2009).

Implication

The findings in this study have implication on the theoretical framework used in the adoption of blended learning tools in both technology acceptance and behavioural theory. Since, attitude significantly predicts behavioural intentions and intentions culminated into actual usage of blended learning tools. Then the combination of TAM and TPB model in predicting the adoption of blended learning tools is a good channel for system quality construct. System quality having a significant relationship with the constructs of TAM model and TPB model will motivate the learners or users to adopt blended learning tools which will culminate into actual usage of such tools in the teaching and learning process.

As a result of the changing technologies and quality of blended learning tools which has greater influence on students intentions to use and actual usage of blended learning tools. School management should endeavour to provide blended learning tools that will not only enhance learning instructions but that will provide quality graphics for visual media, sound and pictures for audio – visuals and that is sustainable to withstand the test of time (Fresen, 2018). Since, system quality is significantly influence to perceived ease of use and perceived usefulness, the educational institution should endeavour to provide a conducive environment or facilitating conditions for the use of blended learning tools in the case of usage of these tools in a face – to – face classroom and quality internet services for online classes (Davies, 1986; Marriot et al., 2004)

The result obtained from the study has several implications as stated earlier. It will enhance educational institutions, educational partners, facilitators, education agency of government and policy makers. The education institutions will have to create enabling classroom environment and quality blended learning tools which gives the facilitators the privilege to interact well with the learners and the subject matter without abrupt alteration of the lesson processes/procedures. The institutions will have to engage the teachers in professional development to enable them to have confidence in using and carefully handling provided blended learning tools during teaching. In most cases, the application of blended learning tools will enhance deep learning by the students rather than surface learning through traditional teaching method because learners are the major focus in the education system. It will motivate education partners and donating institutions to liase with the institutions to provide quality blended learning tools needed within their system. Some institutions can engage in private – public partnership. This partnership allows the private organizations to salvage higher institutions to meeting up with the technological need in the classroom.

Limitations

This study is limited in terms of the number of sample/participants from the few selected technical vocational education and training institutions in Nigeria. First, the result could not be generalized because of the nature of the programme as it involves the inclusion of practical skill, values and morales into general education (Malamud and Pop-Eleches, 2010). Secondly, the outcome of the study could not be generalized among TVET institutions since the study was carried out in a developing country. The factor (policy, environment, availability of blended learning tools and exposure of students) that inhibits or enhance the adoption and usage of blended learning tools in developing is different from developed countries. Also, the level of technology advancement in developed countries could not allow the result to be generalized. Most developed countries have free access to internet connectivity and quality blended learning tools unlike underdeveloped and developing countries that battles unavailable and/or poor internet facilities, deficient experts to use blended learning tools, inadequate support services, inconsistent power supply, poor management system (Busulwa and Bbuye, 2017; Orji et al., 2021).

Conclusion

This study contributed immensely to the robustness of combined – TAM – TPB model to adopt blended learning tools. In theoretical perspective, the study showed that the consideration of quality blended learning tools with C – TAM – TPB model, will motivate the learners to accept, use and willing to continue using any form of blended tools integrated into the education institution. In this light, students will be desirous to use more complex learning tools to facilitate learning. The result showed a strong relationship between the quality of blended learning tools, technology acceptance constructs and the constructs of theory of planned behaviour. There is also a strong mediation effect between the TAM but not the TPB constructs. Likewise, there is a mediation effect between system quality and the relationship between perceived ease of use and perceived usefulness, perceived usefulness and attitude which implies that quality blended learning tools will propel student perception of the ease of use, perceived usability that will boost their attitude towards actual usage of such tools in educational institutions. Therefore, educational institutions should strive to provide quality blended learning tools to learners and instructors to enhance positive change in behaviour and academic excellence as a result of actual and continuous usage of such tools.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Ifeanyi B Ohanu

Taiwo O Shodipe

Author Biographies

Ifeanyi Benedict Ohanu is a Senior Lecturer in the Department of Industrial Technical Education, Faculty of Vocational and Technical Education, University of Nigeria, Nsukka. He holds a PhD, a Master's Degree, and a BSc First Class Honours in Industrial Technical Education obtained from the University of Nigeria, Nsukka. His research interest focuses on Innovative Pedagogies in Vocational and Technical Education, Career and Technical Education, Industrial Technical Education, Career and Workplace Education, practical skills training and acquisition, Entrepreneurship, career/job/work-related intentions, curriculum innovation, technical teacher preparation, workforce training, as well as career guidance; particularly in Electrical and Electronic Technology Education. His e-mail address is ifeanyi.ohanu@unn.edu.ng

Taiwo Olabanji Shodipe is a PhD student in Industrial Technical Education at the University of Nigeria, Nsukka, Nigeria. He holds a Master's degree in Industrial Technical Education obtained from the University of Nigeria, Nsukka. He also holds a BSc Second Class Honours Upper Division in Industrial Technical Education obtained from University of Nigeria, Nsukka, Nigeria. His email address is olabanji.shodipe.pg00974@unn.edu.ng

Chinenye Maria-Goretti Ohanu is a Lecturer in the Department of Zoology and Environmental Biology, Faculty of Biology Sciences, University of Nigeria, Nsukka. Presently, she is a PhD student in Entomology and Forensic Science at the University of Nigeria, Nsukka, Nigeria. She holds a Master's degree in Animal Ecology and Environmental Biology from the University of Nigeria, Nsukka, Nigeria. She holds a BSc. Second Class Honours Upper Division in Parasitology and Entomology obtained from Nnamdi Azikiwe University Awka, Anambra State, Nigeria. Chinenye Maria-Goretti. Ohanu focuses her research interest in entomology and forensic sciences, innovations in teaching and learning. Her e-mail address is chinenye.ohanu@unn.edu.ng

Josephine E. Anene-Okeakwa is a Chief Lecturer in the Department of Home Economics Education, Federal College of Education (Technical), Asaba, Delta State, Nigeria. She holds a Ph.D. in Home Economics Education obtained from the University of Nigeria, Nsukka. She is presently the Provost of the Federal College of Education (Technical), Asaba, Delta State, Nigeria. Her email address is jossyaneson5429@gmail.com

Appendix

References

Abbad

Morris

De Nahlik

(2009) Looking under the bonnet: factors affecting student adoption of E-learning systems in Jordan. International Review of Research in Open and Distance Learning 10(2): 1–25. DOI: 10.19173/irrodl.v10i2.596

Abdullah

Ward

Ahmed

(2016) Investigating the influence of the most commonly used external variables of TAM on students’ perceived ease of use (PEOU) and perceived usefulness (PU) of e-portfolios. Computers in Human Behavior 63: 75–90. DOI: 10.1016/j.chb.2016.05.014

Ahire

Devaraj

(2001) An empirical comparison of statistical construct validation approaches. IEEE Transactions on Engineering Management 48(3): 319–329. DOI: 10.1109/17.946530

Ajzen

(1985) From intentions to actions: a theory of planned behavior. In: Kuhland

JAJB

(ed) Actioncontrol: From Cognitions to Behavior. Heidelberg: Springer. DOI: 10.1007/978-3-642-69746-3_2

Ajzen

(1991) The theory of planned behavior. Organizational Behavior and Human Decision Processes 50(2): 179–211. DOI: 10.1016/0749-5978(91)90020-t

Ajzen

(1993) Attitude theory and the attitude – behaviour relation. In: Krebs

Schmidt

(eds) New Directions in Attitude Measurement. Berlin, New York: Walter de Gruyter.

Ajzen

(2002) Attitudes, Personality and Behaviour. Buckingham: Open University Press.

Ajzen, I. and Fishben, M.A (1975). Belief, attitude, intention and behavior: An introduction to Theory and research. Reading, M.A: Addison-Wesley.

Al-Gahtani

(2016) Empirical investigation of e-learning acceptance and assimilation: a structural equation model. Applied Computing and Informatics 12(1): 27–50. DOI: 10.1016/j.aci.2014.09.001

(2014) The role of different types of information systems in business organization: a review. International Journal of Research 1(7): 333–339. DOI: 10.26524/jms.2014.16

, et al. (2019) Review of the impact of service quality and subjective norms in TAM among telecommunication customers in Jordan. International Journal of Ethics and Systems 35(1): 148–158. DOI: 10.1108/IJOES-07-2018-0101

12.

Alleyne

Lavine

(2013) Factors influencing accountants’ behavioural intentions to use and actual usage of enterprise resource planning systems in a global development agency. Journal of Financial Reporting & Accounting 11(2): 179–200. DOI: 10.1108/jfra-11-2011-0017

(2016) Determinants of perceived usefulness of e-learning systems. Computers in Human Behavior 64: 843–858. DOI: 10.1016/j.chb.2016.07.065

, et al. (2016) Extending the theory of planned behavior (TPB) to explain online game playing among Malaysian undergraduate students. Telematics & Infomatics 34(4): 239–251. DOI: 10.1016/j.tele.2016.07.001

(2011) Climate change and media usage: effects on problem awareness and behavioural intentions. The International Communication Gazette 73(1–2): 45–61. DOI: 10.1177/1748048510386741

(2019) Understanding and predicting teachers’ intention to use cloud computing in smart education. Interactive Technology and Smart Education 17(1): 14–27. DOI: 10.1108/itse-05-2019-0019

17.

Basal

(2011) Learning objects in web foreign language teaching: achievement, permanence and its effect on attitude. Unpublished Doctorate Thesis, Firat University, Institute of Education Sciences. Elazig.

18.

Bhattacherjee

(2000) Acceptance of e-commerce services: the case of electronic brokerages. IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans 30(4): 411–420. DOI: 10.1109/3468.852435

19.

Bohle-Carbonell, K., Dailey-Hebert, A., Gerken, M., & Grohnert, T. (2013). Supporting learner engagement through Problem – based learning: Institutional and instructional implications. In P. Blessinger & C. Wankel (Eds.), Increasing student engagement and retention in e-learning environments: Web 2.0 and blended learning technologies (7). New York, Emerald.

20.

Bojović

(2017) Blended learning as a foreign language learning environment. Teme-Časopis Za Društvene Nauke 41(4): 1017–1036.

21.

Busulw

Bbuye

(2017) Attitudes and coping practices of using mobile phones for teaching and learning in a Uganda secondary school. Open Learning: The Journal of Open, Distance and E-Learning 33(1): 34–45. DOI: 10.1080/02680513.2017.1414588

22.

Carpenter

Reimers

(2005) Unethical and fraudulent financial reporting: applying the theory of planned behavior. Journal of Business Ethics 60(2): 115–129. DOI: 10.1007/s10551-004-7370-9

23.

Castro

(2019) Blended learning in higher education: trends and capabilities. Education and Information Technologies 24(4): 2523–2546. DOI: 10.1007/s10639-019-09886-3

24.

Cenfetelli

Bassellier

(2009) Interpretation of formative measurement in information systems research. The Milbank Quarterly 33(4): 689–708. DOI: 10.2307/20650323

(2007) An empirical investigation of students’ behavioural intentions to use the online learning course websites. British Journal of Educational Technology 39(1): 71–83. DOI: 10.1111/j.1467-8535.2007.00742.x

26.

Chen

(2020) Selecting environmental psychology theories to predict people’s consumption intention of locally produced organic foods. International Journal of Consumer Studies 44(5): 455–468. DOI: 10.1111/ijcs.12578

27.

Chen

(2009) The empirical study of automotive telematics acceptance in Taiwan: comparing three technology acceptance models. International Journal of Mobile Communications 7(1): 50–65. DOI: 10.1504/ijmc.2009.021672

28.

Chin

(1998) The partial least squares approach for structural equation modeling. In: Macoulides

(ed) Modern Methods for Business Research. Mahwah, NJ: Lawrence Erlbaum Associates, pp. 295–336.

29.

Cohen

(1988) Statistical Power Analysis for the Behavioral Sciences. Mahwah, NJ: Lawrence Erlbaum.

30.

Conner, M. and Armitage, C.J. (1998). Extending the theory of planned behavior: A review and avenues for further research. Journal of Applied Social Psychology. 28(1), 1429–1464. https://doi.org/10.1111/j.1559-1816.1998.tb01685.x

31.

Davies, F.D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Massachussets, United States: Sloan School of Management, Massachussets Institute of Technology.

32.

Darling–Hammond

Flook

Cook–Harvey

, et al. (2020) Implications for educational practice of the science of learning and development. Applied Developmental Science 24(2): 97–140. DOI: 10.1080/10888691.2018.153779

33.

Davis

(1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. The Milbank Quarterly 13(3): 319–340. DOI: 10.2307/249008

34.

Davis

(1993) User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies 38(3): 475–487. DOI: 10.1006/imms.1993.1022

(1989) User acceptance of computer technology: a comparison of two theoretical models. Management Science 35(8): 982–1003. DOI: 10.1287/mnsc.35.8.982

36.

Delone

McLean

(2003) The delone and mclean model of information systems success: a ten-year update. Journal of Management Information Systems 19(4): 9–30. DOI: 10.1080/07421222.2003.11045748

37.

Doll

Torkzadeh

(1998) Developing a multidimensional measure of system-use in an organizational context. Information & Management 33(4): 171–185. DOI: 10.1016/s0378-7206(98)00028-7

38.

Draper, N. R., & Smith, H. (2003). Applied regression analysis, 3rd edition, United State. Wiley.

39.

Lai

Cheung

, et al. (2012) Willingness to share information in a supply chain: a partnership-data-process perspective. Information & Management 49(2): 89–98. DOI: 10.1016/j.im.2011.10.003

40.

Falk

Nancy

(1992) A Primer for Soft Modeling. University of Akron Press. Includes an introduction to PLS.

(2009) Demotivation: affective states and learning outcomes. System 37(3): 403–417. DOI: 10.1016/j.system.2009.03.004

42.

Fresen

(2018) Embracing distance education in a blended learning model: challenges and prospects. Distance Education 39(2): 224–240. DOI: 10.1080/01587919.2018.1457949

43.

Fornell

Larcker

(1981) Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18(1): 39–50. DOI: 10.1177/002224378101800104

44.

Gan

Balakrishnan

(2017) Predicting acceptance of mobile technology for aid students – lecturer interactions: an empirical study. Australasian Journal of Educational Technology 33(2): 143–158.

45.

Garrison

Kanuka

(2004) Blended learning: uncovering its transformative potential in higher education. The Internet and Higher Education 7(2): 95–105. DOI: 10.1016/j.iheduc.2004.02.001

46.

Grimes

Marquardson

(2019) Quality matters: evoking subjective norms and coping appraisals by system design to increase security intentions. Decision Support Systems 119: 23–34. DOI: 10.1016/j.dss.2019.02.010

47.

Gopi

Ramayah

(2007) Applicability of theory of planned behavior in predicting intention to trade online. International Journal of Emerging Markets 2(4): 348–360. DOI: 10.1108/17468800710824509

48.

Gorla, N., Somers, T.M. and Wong, B. (2010). Organizational impact of system quality, information quality, and service quality. Journal of Strategic Information Systems.19(3), 207–228. https://doi.org/10.1016/j.jsis.2010.05.001

49.

Guimaraes

Armstrong

Jones

(2017) A new approach to measuring information system quality. Quality Management Journal 16(1): 42–51. DOI: 10.1080/10686967.2009.11918217

, et al. (2009) Multivariate Data Analysis. Upper Saddle River, NJ: Prentice-Hall.

, et al. (2014) Partial least squares structural equation modeling (PLS - SEM) an emerging tool in business research. European Business Review 26(2): 106–121. DOI: 10.1108/EBR-10-2013-0128

52.

Hawlitschek

Joeckel

(2017) Increasing the effectiveness of digital educational games: The effects of a learning instruction on students’ learning, motivation and cognitive load. Computers in Human Behavior 72: 79–86. DOI: 10.1016/j.chb.2017.01.040

(2009) The use of partial least squares path modeling in international marketing. Advances in International Marketing 20: 277–319. DOI: 10.1108/s1474-7979(2009)0000020014

(2013) Using theoretical models to examine the acceptance behavior of mobile phone messaging to enhance parent-teacher interactions. Computers & Education 61: 105–114. DOI: 10.1016/j.compedu.2012.09.009

, et al. (2011) The application of blended learning in the teaching design of computer aided design. In International conference on control, automation and systems engineering (CASE), pp. 1–3. DOI: 10.1109/ICCASE.2011.5997818

(2013) Explaining undergraduates’ behavior intention of e – textbook adoption: empirical assessment of five theoretical models. Library Hi Tech 32(1): 139–163. DOI: 10.1108/LHT-09-2013-0126

(2007) Consumer behavior in online game communities: a motivational factor perspective. Computers in Human Behavior 23(3): 1642–1659. DOI: 10.1016/j.chb.2005.09.001

, et al. (1999) Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems 16(2): 91–112. DOI: 10.1080/07421222.1999.11518247

59.

Ibrahim

Nat

(2019) Blended learning motivation model for instructors in higher education. International Journal of Educational Technology in Higher Education 16(12): 1–21. DOI: 10.1186/s41239-019-0145-2

(2021) Promoting student – centred blended learning in higher education: A model. E – Learning and Digital Media 0(0): 1–19. DOI: 10.1177/20427530211017721

, et al. (2012) Study of blended learning process in education context. International Journal of Modern Education and Computer Science 4(9): 23–29. DOI: 10.5815/ijmecs.2012.09.03

(2009) Modeling roles of subjective norms and etrust in customers’ acceptance of airline B2C ecommerce websites. Tourism Management 30(2): 266–277. DOI: 10.1016/j.tourman.2008.07.001

(2017) Blended learning effectiveness: the relationship between students characteristics, design features and outcomes. International Journal of Educational Technology in Higher Education 14(7): 1–20. DOI: 10.1186/s41239-017-0043-4

(2004) Consumer acceptance of wireless finance. J Financ Serv Mark 8(3): 206–217. DOI: 10.1057/palgrave.fsm.4770120

65.

Kock

(2016) Hypothesis testing with confidence intervals and P values in PLS-SEM. International Journal of E–Collaboration 12(3): 1–6. DOI: 10.4018/ijec.2016070101

66.

Kock

Lynn

(2012) Lateral collinearity and misleading results in variance-based SEM: an illustration and recommendations. Journal of the Association for Information Systems 13(7): 546–580. DOI: 10.17705/1jais.00302

67.

Krueger

Carsrud

(1993) Entrepreneurial intentions: applying the theory of planned behaviour. Entrepreneurship & Regional Development 5(4): 315–330. DOI: 10.1080/08985629300000020

68.

Lachan – Hache, L., Cushing, E., & Bivona, L. (2012). Students learning objectives: Benefits, challenges and solutions. In Performance Management Advantage: Evaluation and Profession Growth. American Institute for Research. Washington, D.C

69.

Lau

Woods

(2008) An empirical study of learning object acceptance in multimedia learning environment. Communications of the IBIMA 5(1): 1–6.

70.

Lee

Hsieh

(2011) A model of organizational employees’ e-learning systems acceptance. Knowledge-Based Systems 24(3): 355–366. DOI: 10.1016/j.knosys.2010.09.005

(2010) What drives people to continue to play online games? An extension of technology model and theory of planned behavior. International Journal of Human-Computer Interaction 26(6): 601–620. DOI: 10.1080/10447311003781318

(2007) Understanding user acceptance of multimedia messaging services: an empirical study. Journal of the American Society for Information Science & Technology 58(13): 2066–2077. DOI: 10.1002/asi.20670

73.

Letchumanan

Tarmizi

(2011) Assessing the intention to use e‐book among engineering undergraduates in Universiti Putra Malaysia, Malaysia. Library Hi Tech 29(3): 512–528. DOI: 10.1108/07378831111174459

74.

Lin

H-F

(2010) Applicability of the extended theory of planned behavior in predicting job seeker intentions to use job-search websites. International Journal of Selection and Assessment 18(1): 64–74. DOI: 10.1111/j.1468-2389.2010.00489.x

75.

Littlejohn

Pegler

(2007) Preparing for Blended E-Learning. London: Routledge.

76.

Lowry

Gaskin

(2014) Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: when to choose it and how to use it. IEEE Transactions on Professional Communication 57(2): 123–146. DOI: 10.1109/tpc.2014.2312452

77.

Liu

, et al. (2003) Technology acceptance model for wireless internet. Journal of Internet Research 13(2): 206–222. DOI: 10.1108/10662240310478222

78.

Deng

Wang

(2010) Exploring factors affecting Chinese consumers’ usage of short message service for personal communication. Information Systems Journal 20(2): 183–208. DOI: 10.1111/j.1365-2575.2008.00312.x

79.

Gam

Banning

(2017) Perceived ease of use and usefulness of sustainability labels on apparel products: application of the technology acceptance model. Fashion and Textile 4(3): 1–20. DOI: 10.1186/s40691-017-0093-1

80.

Malamud

Pop-Eleches

(2010) General education versus vocational training: evidence from an economy in transition. Review of Economics and Statistics 92(1): 43–60. DOI: 10.1162/rest.2009.11339

81.

Margolis, A.A. (2018). What kind of blending makes blended learning? Psychological Science and Education. 23(3), 5–19. https://doi.org/10.17759/pse.2018230301

82.

Marriot

Selwyn

(2004) Accounting undergraduates’ changing use of ICT and their views on using the internet in higher education-A research note. Accounting Education 13(4): 117–130. DOI: 10.1080/0963928042000310823

(2001) Extending the TAM for a world-wide-web context. Information & Management 38(4): 217–230. DOI: 10.1016/s0378-7206(00)00061-6

84.

Mozelius

Hettiarachchi

(2017) Critical factors for implementing blended learning in higher education. International Journal of Information and Communication Technologies in Education 6(1): 4–18. DOI: 10.1515/ijicte-2017-0010

, et al. (2003) Why should we use SEM? The pros and cons of structural equation modeling. Methods of Psychological Research Online 8(2): 1–22.

, et al. (2020) The measurement of university students’ intention to use blended learning system through technology acceptance model (TAM) and theory of planned behavior (TPB) at developed and developing regions: lesson learned from Taiwan and Indonesia. International Journal of Emerging Technologies in Learning 15(9): 219–230. DOI: 10.3991/ijet.v15i09.11517

87.

Namdeo

Rout

(2016) Calculating and interpreting cronbach’s alpha using rosenberg assessment scale on paediatrician’s attitude and perception on self esteem. Int J Community Med Public Health 3(6): 1371–1374. DOI: 10.18203/2394-6040.ijcmph20161448

(2015) Barriers and enablers in adopting of halal warehousing. Journal of Islamic Marketing 6(3): 354–376. DOI: 10.1108/jima-03-2014-0027

(2007) Empirical examination of the adoption of WebCT using TAM. Computers & Education 48(2): 250–267. DOI: 10.1016/j.compedu.2004.11.007

90.

Ohanu, I.B., Shodipe, T.O., Chukwu, D.U. and Chukwuma, J.N. (2020), “Impact of behavioural factors as related to available resources on entrepreneurial intentions of electrical installation and maintenance works students”, International Journal of Engineering Education, 36(1A), 142–154.

91.

Ohanu, I.B., et al., (2022). Quality blended learning systems for improving undergraduate students' skills. Quality Assurance in Education. 30(2), 169–183. https://doi.org/10.1108/QAE-08-2021-0132

92.

Orji

Anaele

EAO

Olelewe

, et al. (2021) A critical view on blended learning improvement strategies in post – covid 19. IETE Journal of Education 62(2), 80–88. DOI: 10.1080/09747338.2021.1967206

93.

Pavlou

(2003) Consumer acceptance of electronic commerce: integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce 7(3): 101–134. DOI: 10.1080/10864415.2003.11044275

(2008) Measuring information systems success: models, dimensions, measures, and interrelationships. European Journal of Information Systems 17(3): 236–263. DOI: 10.1057/ejis.2008.15

(2006) The influence of system characteristics on e-learning use. Computers & Education 47(2): 222–244. DOI: 10.1016/j.compedu.2004.10.007

96.

Preacher

Hayes

(2008) Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods 40(3): 879–891. DOI: 10.3758/brm.40.3.879

97.

Qian, M. (2011). The application of blended learning modes in college English teaching based on multimedia technology. In Lin, S., Huang, X. (eds). Advances in computer science, environment, ecoinformatics and education. Communications in Computer and Information Science, vol. 217. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23339-5_21

98.

Rai

Lang

Welker

(2002) Assessing the validity of IS success models: an empirical test and theoretical analysis. Information Systems Research 13(1): 50–69. DOI: 10.1287/isre.13.1.50.96

(2017) Predicting customer’s intentions to use internet banking: the role of technology acceptance model (TAM) in e – banking. Management Science Letters 7: 513–524. DOI: 10.5267/j.msl.2017.8.004

(2015) Satisfaction on blended learning in a public higher education institution: what factors matter? Procedia - Social and Behavioral Sciences 211: 768–775. DOI: 10.1016/j.sbspro.2015.11.107

(2009) Applying the theory of planned behavior (TPB) to predict internet tax filing intention. International Journal of Management 26(2): 272–284.

102.

Renny

Siringoringo

(2013) Perceived usefulness, ease of use, and attitude towards online shopping usefulness towards online airlines ticket purchase. Procedia - Social and Behavioral Sciences 81: 212–216. DOI: 10.1016/j.sbspro.2013.06.415

103.

Richard

Meuli

(2013) Exploring and modeling digital natives’ intention to use permission – based location – aware mobile advertising. Journal of Marketing Management 29(5–6): 698–719. DOI: 10.1080/0267257x.2013.770051

104.

Rigdon, E.E. (2014). Re: Defending PLS over covariance – based SEM. Communication to the SEMNET discussion list, semnet@listserv.ua.edu. In Garson, G.D. Partial Least Squares: Regression and Structural Equation Models. Statistical Associates Publishing. North Carolina, USA.

(2015) Boenningstedt: SmartPLS GmbH. http://www.smartpls.com

106.

Salleh

Laxman

(2014) Investigating the factors influencing teachers’ use of ICT in teaching in Bruneian secondary schools. Education and Information Technologies 19(4): 747–762. DOI: 10.1007/s10639-013-9251-2

, et al. (2014) Partial least squares structural equation modeling (PLS-SEM): a useful tool for family business researchers. Journal of Family Business Strategy 5(1): 105–115. DOI: 10.1016/j.jfbs.2014.01.002

108.

Seddon

(1997) A respecification and extension of the delone and mclean model of IS success. Information Systems Research 8(3): 240–253. DOI: 10.1287/isre.8.3.240

109.

Sharahiley

(2020) Examining entrepreneurial intention of the Saudi Arabia’s university students: analyzing alternative integrated research model of TPB and EEM. Glob J Flex Syst Manag 21(1): 67–84. DOI: 10.1007/s40171-019-00231-8

110.

Shih

Huang

(2009) The actual usage of ERP systems: an extended technology acceptance perspective. Journal of Research and Practice in Information Technology 41(3): 263–276.

111.

Shodipe, O.T and Ohanu, I.B (2021), “Electrical/electronics technology education teachers’ attitude, engagement and disposition towards actual usage of mobile learning in higher institutions”, Education and Information Technologies. 26(2), 1023–1042. https://doi.org/10.1007/s10639-020-10297-y

112.

Siew-Eng

Muuk

(2015) Blended learning in teaching secondary schools’ english: a preparation for tertiary science education in Malaysia. Procedia - Social and Behavioral Sciences 167: 293–300. DOI: 10.1016/j.sbspro.2014.12.677

113.

Staker, H., and Horn, M. B. (2012). Classifying K–12 blended learning. Innosight Institute. United Kingdom. http://www.christenseninstitute.org/wp-content/uploads/2013/04/Classifying-K-12-blendedlearning.pdf

114.

Staker

Chan

Clayton

, et al. (2011) The Rise ofK-12 Blended Learning: Profiles of Emerging Models. Innosight Institute. https://www.christenseninstitute.org/wp-content/uploads/2013/04/The-rise-of-K-12-blended-learning.emerging-models.pdf

115.

Stoel

Hye Lee

(2003) Modeling the effect of experience on student acceptance of web‐based courseware. Internet Research 13(5): 364–374. DOI: 10.1108/10662240310501649

116.

Mohd Suki

(2017) Flight ticket booking app on mobile devices: examining the determinants of individual intention to use. Journal of Air Transport Management 62: 146–154. DOI: 10.1016/j.jairtraman.2017.04.003

117.

Szajan

(1996) Empirical evaluation of the revised technology acceptance model. Management Science 42(1): 85–92. DOI: 10.1287/mnsc.42.1.85

118.

Tabachnick

Fidell

(2007) Using Multivariate Statistics. Pearson Education Inc.

119.

Taylor