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Behav. Sci. 2017, 7(3), 47; https://doi.org/10.3390/bs7030047

Article
Interactivity and Trust as Antecedents of E-Training Use Intention in Nigeria: A Structural Equation Modelling Approach
1
Faculty of Management, Universiti Teknologi Malaysia, Johor 81310, Malaysia
2
School of Management and Information Technology, Modibbo Adama University of Technology, Yola 640221, Nigeria
*
Author to whom correspondence should be addressed.
Received: 2 June 2017 / Accepted: 11 July 2017 / Published: 18 July 2017

Abstract

:
Background: The last few decades saw an intense development in information technology (IT) and it has affected the ways organisations achieve their goals. Training, in every organisation is an ongoing process that aims to update employees’ knowledge and skills towards goals attainment. Through adequate deployment of IT, organisations can effectively meet their training needs. However, for successful IT integration in training, the employees who will use the system should be positively disposed towards it. This study predicts employees’ intention to use the e-training system by extending the technology acceptance model (TAM) using interactivity and trust. Methods: Two hundred and fourteen employees participated in the study and structural equation modelling was used in the analysis. Results: The findings of the structural equation modelling reveal that interactivity, trust, perceived usefulness and perceived ease of use have direct and positive effects on employees’ intention to use e-training. It was also shown that perceived ease of use had no effects on perceived usefulness, while trust has the strongest indirect effects on employees’ intention. In addition, the results of Importance-Performance Map Analysis (IPMA), which compares the contributions of each construct to the importance and performance of the model, indicate that to predict intention to use e-training, priorities should be accorded to trust and perceived usefulness.
Keywords:
e-training; interactivity; trust; perceived ease of use; perceived usefulness

1. Introduction

Providing a competent workforce is the desire of every modern organisation as its success largely hinges on the performance of its human resource [1]. To have competitive advantage and comply with the demands of the emerging global labour market infrastructure, organisations must create a mechanism that ensures the availability of a workforce with the prerequisite knowledge, skills, and ability to effectively deliver within the existing constraints of global competition. This can be achieved through the provision of extensive training that provides employees with updated knowledge of modern changes in technologies and socio-economic set up in the world of competition [2]. Sustaining the traditional method of training is becoming less attractive to organisations due its costly nature [3,4] and the existence of alternative and more sustainable options provided by technology [5,6]. According to Kamal, Aghbari [7], the demand for alternative methods for learning is increasing exponentially, and that the demand for corporate electronic learning is on the increase, as many corporations have adopted e-learning for employee training and learning. However, organisations should be cautious in their drive towards technology integration in training. To ensure success of an e-training system, the employees, who are major stakeholders, should be able and willing to use such systems, even though options available to them in a mandatory use environment are very limited. The importance of assessing intention to use information technology (IT) prior to its implementation has been recommended [8]. Although previous studies on intention to use technology exist in the extant literature, most of these studies have investigated intentions in the fields of e-commerce, e-banking, mobile banking, consumer behaviour, websites and cloud services [9,10]. The roles played by interactivity and trust as determinants of intention have been established in the aforementioned areas [9,11,12]. However, research in the field of e-training is generally scarce in the extant literature. Specifically, no study has empirically investigated the factors influencing intention to use e-training in public organisations [13]. In addition, no study has investigated the roles of interactivity and trust in e-training intentions. This paper presents empirical findings on the influence of interactivity and trust on intention to use e-training using TAM, in the context of Nigerian public universities, in order to fill the gaps in the literature.

1.1. Theoretical Foundation

This study uses the technology acceptance model (TAM) [14] as its underpinning theory. According to the TAM, user perceptions of usefulness (PU) and ease of use (PEOU) determine the attitudes toward using the system, and attitudes toward using the system determine behavioural intentions, which in turn, lead to actual system use. Perceived usefulness is the degree to which a person believes that using a particular system would enhance his/her job performance, while perceived ease of use is the degree to which a person believes that using a particular system would be free of physical and mental effort [14]. According to Davis (1989) [14], the sequential relationship of belief–attitude–intention–behaviour in TAM, enables us to predict the use of new technologies by users (see Figure 1).
Since its inception in 1989, the technology acceptance model has been widely used by researchers in investigating behavioural intentions and acceptance of technology across different fields of study and contexts. The reason advanced for its acceptance among researchers is the degree of its flexibility, which can be modified, based on the purposes of the study, enabling it to be extended [15]. Many studies have used the TAM to investigate intention to use technology and found it to be effective in predicting intention [9,10,12,16,17,18]. Therefore, this study aims at extending the TAM, by examining the influence of interactivity and trust on the intention to use e-training.

1.2. Conceptual Framework and Hypotheses

Based on the TAM, perceived usefulness and perceived ease of use are used to explain intention to use e-training. In the context of this study, perceived usefulness and perceived ease of use refer to employees’ perceptions of e-training in the universities. Interactivity, is a formative second-order construct, which means that it is formed by other constructs or dimensions. In this study, interactivity is formed by three other dimensions namely, active control, two-way communication, and synchronicity, based on the modified Liu’s interactivity dimensions [19]. The trust construct is adapted from Gefen, Karahanna [11]. Both constructs denote the external variables influencing intention to use e-training through perceived usefulness and perceived ease of use.

1.2.1. Perceived Ease of Use

Perceived ease of use is one of the main constructs of the TAM. In this study, perceived ease of use refers to the degree to which an employee believes that using e-training system will be easy to operate, understandable, interactive, and flexible. Previous studies have reported factors such as self-efficacy, experience, understandability, interactiveness, flexibility and facilitating conditions, to have influenced perceived ease of use [20,21,22]. Thus, the ability of employee to use the e-training system will be affected by his/her level of self-efficacy, ease of understanding, and flexibility of the system. On the other hand, employees PEOU will improve his/her perceptions of the usefulness of the e-training system in terms of task performance, skills, and reward emphasised. Equally, PEOU use is likely to remove the fear that usually comes with the introduction of a new system and reinforce employees’ trust towards e-training. Prior studies have reported that most research conducted in the field of IT have confirmed the existence of significant and positive relationships between perceived ease of use, attitude and intention to use e-learning [23]. Likewise, PEOU was reported to have had significant and direct effects on PU [24,25,26]. In addition, the indirect influence of PEOU on intention through PU has also been established [24,27,28,29]. On the contrary, PEOU was found to have no effects on PU [30] and on intention [31]. Considering the evidences above, the researchers propose the following hypotheses:
Hypothesis 1.
Perceived ease of use positively and significantly affects employees’ intention to use e-training in Nigerian public universities.
Hypothesis 2.
Perceived ease of use positively and significantly affects employees’ perceived usefulness of e-training in Nigerian public universities.
Hypothesis 3.
Perceived ease of use influences the effects of interactivity on intention to use e-training in Nigerian public universities.
Hypothesis 4.
Perceived ease of use influences the effects of trust on intention to use e-training in Nigerian public universities.

1.2.2. Perceived Usefulness

Perceived usefulness is another important construct predicting intention in the TAM. According to Li and Huang [32], PU in the TAM is the main belief factor determining behavioural intention to use an information technology. It refers to the degree to which an employee believes that using e-training would enhance his/her skills, task accomplishment, productivity, and make work easy and useful. This implies that the more an employee views e-training system as capable of positively affecting his/her personal skills and work performance, then the employee is likely to view a training as very useful worth using. Previous studies have reported PU as an important determinant of attitude and behavioural intention [33]. The influence of the construct on intention to use technology was also established [34]. Similarly, PU was reported to have had a direct and positive effect on employees’ intention to use web-based training [10]. Likewise, PU was found to have predicted behavioural intention to use e-portfolio [20,35] and a strong determinant of adoption [36]. Therefore, building on the above empirical evidence, and in accordance with TAM, the study postulates that PU plays a significant role of influencing employees’ intention to use e-training systems in Nigerian public universities. Hence, the following hypotheses are proposed.
Hypothesis 5.
Perceived usefulness positively and significantly affects employees’ intention to use e-training in Nigerian public universities.
Hypothesis 6.
Perceived usefulness influences the effects of interactivity on intention to use e-training in Nigerian public universities.
Hypothesis 7.
Perceived usefulness influences the effects of trust on intention to use e-training in Nigerian public universities.
Hypothesis 8.
Perceived usefulness influences the effects of perceived ease of use on intention to use e-training in Nigerian public universities.

1.2.3. Interactivity

One of the established weaknesses of e-training is the absence of face–to-face interaction [37]. Emphasising on the importance of interaction, Moore and Kearsley [38] have opined that interaction is relevant to all forms of learning, whether such learning involves technology or not. There are many types of interaction in a technology based learning environment in the extant literature. These include, among others: learner–tool interaction [39], learner–task interaction [40], the learner self, learner–learner, learner–instructor, learner–content, and learner–interface types [41]. In this study, interactivity refers to the extent to which two or more parties communicating in an e-training environment can act on each other, on the communication medium, and on the messages, and the degree to which such influences are synchronised. Interactivity in this study is a formative construct with indicators that are fundamental parts of it namely, active control, two-way communication, and synchronicity. Active control refers to the employee’s ability to voluntarily participate in and instrumentally influence a communication; two-way communication refers to the mutual communication: (1) between users and users or (2) between users and messages on the internet; and synchronicity refers to the degree to which employees’ contribution to a communication and the responses they receive from the communication are simultaneous [19]. Previous studies have confirmed that interactivity has a positive influence on the attitude of users and their use intentions [42] and increases intention to use e-learning [43]. Similarly, past studies have reported that interactivity as an exogenous variable has positively influenced perceived usefulness and ease of use [44,45]. Furthermore, Macy and Skvoretz [46] have demonstrated in a simulation study that high levels of interaction leads to trust. Based on the evidence above, the researchers opine that interactivity is important to employees’ use of e-training system in universities. Accordingly, the following hypotheses are formulated:
Hypothesis 9.
Interactivity positively and significantly affects employees’ intention to use e-training in Nigerian public universities.
Hypothesis 10.
Interactivity positively and significantly affects employees perceived usefulness in Nigerian public universities.
Hypothesis 11.
Interactivity positively and significantly affects employees perceived ease of use.

1.2.4. Trust

To reduce uncertainty and get anticipated commitment from employees, trust is argued to be the key [47]. It has been argued that organisations first build trust in their employees before they become affectively committed [48]. For instance, in using an e-training system, issues relating to security of personal information, feedback on e-training performance, safety of e-training platform, terms and conditions for using the university’s e-training system, integrity of people to interact with in the course of e-training process, absence of face to face interaction, and fear of not achieving much during e-training etc., could raise some concerns among employees and might influence their trust in the e-training system. In this study, trust is defined as the extent to which employees consider the e-training system and the training participants as secure, trustworthy, considerate, and in their best interest. Building trust may help in improving perceived usefulness and employees’ experience of using a technology. For instance, when employees believe that e-training system has the necessary ability, integrity and benevolence to deliver a positive training and experience to them, they are likely to use it. According to Gefen, Karahanna [11], trust provides the assurance to the users that using the system will result in positive outcomes in the future. In addition, Luhmann [47] opines that trust is key for an organisation to reduce uncertainty and get anticipated the commitment, and further, that trust can reduce risk and uncertainty in trust related behaviours [49]. Yoon [50] has also confirmed the effects of trust on perceived usefulness. The relationship between trust and intention has also been established in the extant literature. For example, trust has been found as an initial prerequisite for users to participate in knowledge transferring and exchanging [51] and plays a significant role in the promotion of knowledge sharing among users [52]. In another study, Yusoff, Ramayah [53] have established that trust had a significant effect on the attitude towards using electronic human resource management (E-HRM). Also, Wang, Ngamsiriudom [54] have established that the relationship between trust and behavioural intention are positively significant. Based on the above assertions and evidence, this study opines that trust will play an important role in influencing employees’ intention to use e-training in Nigerian universities. Accordingly, the following hypotheses are proposed:
Hypothesis 12.
Trust positively and significantly affects employees’ intention to use e-training in Nigerian public universities.
Hypothesis 13.
Trust positively and significantly affects employees’ perceived usefulness in Nigerian public universities.
Hypothesis 14.
Trust positively and significantly affects employees’ perceived ease of use in Nigerian public universities.

2. Methods

2.1. Respondents

The respondents of this study were drawn from employees of five federal universities of technology in Nigeria. A multi-stage cluster sampling was used and the respondents were selected through systematic random sampling based on strata. After data cleaning and treatment of outliers, a total of 214 usable responses were retained and used for the analysis. Demographically, the sample has more men than women and the majority of the respondents were aged 30 years and above. Likewise, the analysis indicates that the population is an educated one as the majority of the respondents have Masters Degrees and above. In terms of computer/internet experience, the analysis indicates that 13% had less than 1 year experience, 35.2% had 1–3 years, 31.6% had 4–7 years, 13.6% had 8–11 years and 6.6% of the respondents had more 12 years of experience.
In order to conform to ethics requirements, a cover letter was attached to each questionnaire in which the purpose of the study was clearly stated. Also included in the letter were the names, addresses (including email addresses), and institution of the researchers with the hope of increasing the confidence of the respondents and for them to be familiar with whom they were dealing with, as opined by Cooper and Schindler [55]. Information provided by the respondents was treated as confidential, used only for academic purposes, and were not in any way described to allow their identity be revealed. In addition, the researchers used only the combined results in reporting the findings of the study.

2.2. Instrument and Data Collection

A 5-Likert scaled questionnaire was designed to measure the elements of the proposed model. The constructs of the model were selected based on the extant literature in order to assess employees’ intention to use e-training. The items for intention construct (INT) were adapted from Venkatesh and Davis [56] and Dix, Ferguson [57]. Items used in measuring trust were adapted from Gefen, Karahanna [11] and from Zhou [58]. The items for perceived usefulness and perceived ease of use were adapted from Davis [14]. Lastly, the interactivity construct, beings a second order formative construct has its items adapted from Liu [19]. The questionnaires were pre-tested by academic experts and professionals with good experience in training and technology management. Copy of the final questionnaire used in the study is provided in Appendix A. A sample of 214 drawn from the five universities of technology that formed the population of the study, and was used in the final data analysis of this study. Stratified random sampling was used and respondents were selected based on systematic random sampling from each strata (university). The questionnaire was finally administered to the employees of the 5 universities and were asked to indicate their agreement or disagreement with the above items as contained in the questionnaire. The data collection was done between January and March 2017.

2.3. Data Analysis Technique

SPSS 21.0 and SmartPLS 3.0 were used for statistical analysis of the data collected. The assessment of the Partial Least Square–Structural Equation Modeling (PLS-SEM) and reporting the output, recommendations of Hair Jr, Hult [59] and Ramayah, Cheah [60] were followed.

3. Results

3.1. Measurement Model

The measurement model was assessed using item loadings, convergent validity, reliability of measure, and discriminate validity. For convergent validity, the researchers first examined the outer loadings of the indicators, which as recommended by Hair Jr, Hult [59] should be 0.708. Secondly, the researchers examined the average variance extracted (AVE) values of all the constructs in the research model and the results as presented in Table 1, show that all the constructs which must meet the recommended minimum requirement of AVE > 0.50 [61]. An item is reliable if its loading was greater than 0.7. The convergent validity was determined using average variance extracted (AVE), which according to Fornell and Larcker [61] must exceed 0.5. Composite reliability and Cronbach’s Alpha were used to assess the reliability of the measures. Normally, the minimum values of composite reliability should be 0.7 [62], and that of Cronbach’s alpha should also be 0.7 [63]. To check discriminate validity, square root of average variance extracted, latent variable correlations, and Heterotrait–Monotrait Ratio of Correlation (HTMT) were used. As rule of thumb, the square root of average variance extracted of each construct should exceed the correlation shared between one construct and other constructs in the model [61]. For cross loadings, the requirement is that the loadings of indicators on the assigned latent variable should be higher than the loadings on all other latent variables [64]. HTMT discriminant validity between two constructs is deemed to be established if the HTMT0.85 value is less than 0.85 [65] or HTMT0.90 value of 0.90 [66]. The results in Table 2 shows that all the items outer loadings are above the recommended threshold of 0.708 and the AVEs for constructs are above 0.5. In addition, the composite reliability for all constructs have met the threshold of 0.7. Also, the Cronbach’s alpha for all construct were above the recommended minimum of 0.7.
In addition, based on the results in Table 3, the Fornell–Lacker Criterion shows that the square root of average variance extracted of each construct have exceeded the correlation shared between one construct and other constructs. Lastly, the HTMT Criterion also shows that none of the values is greater than 0.90 as recommended by Gold and Arvind Malhotra [66]. Therefore, based on the results in Table 1 and Table 2, the reflective measurement can be said to have met convergent and discriminant validity.

Second-Order Formative Construct’s Measurement Model

Having a second-order formative construct requires that its measurement model be assessed separately, as different indices are required to assess the significance and relevance of the formative indicators. To do that, the researchers first assessed the convergent validity, and the redundancy results show a path coefficient of 0.686, which is approximately 0.70 (see Figure 2), which is acceptable to provide support for convergent validity of the formative construct [59].
Likewise, the researchers checked the outer Variance Inflation Factor (VIF) values using PLS algorithm and all the VIF values were below the recommended 3.3 [67]. Lastly, to assess the significance and relevance of the formative indicators, the researcher run basic bootstrapping using 5000 subsamples. The results in Table 4 indicates that the weights of IRAC (p-value = 0.01), IRCM (p-value = 0.01), and IRSN (p-value = 0.013) were significant. Thus, based on the results in Table 3, the formative construct measurement model has achieved validity. Having ascertained the validity and reliability of the reflective and formative construct measurement models, the researchers then tested the structural model.

3.2. Structural Model

To test the structural model, collinearity assessment was carried out and the results indicate that both tolerance and VIF are below the threshold of 10 and 5 respectively [68]. This confirms that multicollinearity is not a concern. Partial least squares-structural equation modeling (PLS-SEM) algorithm was then run to obtain path coefficients (the structural model relationships) which represent the hypothesised relationships among the constructs of the study (see Figure 3).
Path coefficients have standardised values between −1 and +1 which values of +1 represent strong positive relationships, while −1 represent strong negative relationships [59]. This value should be significant, and at least at the 0.05 level [69].

3.2.1. Significance of the Relationships among Constructs

The results of the structural model are presented in Table 5. To determine the significance of each of the path coefficient, a basic bootstrapping using 5000 sub-samples was run as recommended by Chin [64]. The results of the bootstrapping also show the significant structural relationships among the research variables and path coefficients.
The results indicate that perceived ease of use (t = 2.365, β = 0.132, p < 0.018) has positive influence on intention to use e-training which supports the hypothesis that perceived ease of use positively and significantly affects employees’ intention. However, the influence of perceived ease of use on perceived usefulness (t = 0.055, β = −0.004, p > 0.1) was found to be statistically insignificant to support the hypothesis that perceived ease of use positively and significantly affects employees’ perceived usefulness. Also, the results show that perceived usefulness (t = 7.222, β = 0.453, p < 0.01) had a strong influence on intention which supports the hypothesis that perceived usefulness positively and significantly affects employees’ intention. Likewise, the results indicate that interactivity has significant influence on intention (t = 2.160, β = 0.138, p < 0.031) which supports the hypothesis that Interactivity positively and significantly affects employees’ intention. Interactivity also had significant effects on perceived ease of use (t = 6.929, β = 0.490, p < 0.01) and thus supports the hypothesis that interactivity positively and significantly affects employees perceived ease of use. Its influence on perceived usefulness (t = 4.530, β = 0.399, p < 0.01) has also been established thereby supporting the hypothesis that interactivity positively and significantly affects employees perceived usefulness. Furthermore, the results demonstrate that trust has significant influence on intention (t = 3.467, β = 0.251, p < 0.01) which supports the hypothesis that trust positively and significantly affects employees’ intention. The results also indicate that trust has significantly influenced perceived usefulness (t = 5.464, β = 0.446, p > 0.1) which supports the hypothesis that trust positively and significantly affects employees’ perceived usefulness. Likewise, trust has positively and significantly influenced on perceived ease of use (t = 4.178, β = 0.275, p< 0.01), thereby supporting the hypothesis that trust positively and significantly affects employees’ perceived ease of use. Therefore, the results have supported all the direct hypothesised relationships between the constructs except that which exist between perceived ease of use and perceived usefulness.

3.2.2. Mediation

To test the mediation effects of perceived usefulness and trust, PLS-SEM bootstrapping was run as recommended by Preacher and Hayes [70]. Mediation is said to be established when results of indirect effect from the confidence interval bias corrected is all positive or all negative. If the results show that zero is not between the lower and upper bound, it means that the indirect effect is not zero [71]. The results of the indirect effects and the confidence interval bias corrected values as shown from Table 6 indicate that perceived ease of use has mediated the influences of interactivity and trust on intention (all p-values < 0.01) which support the hypotheses that perceived ease of use influences the effects of interactivity on intention to use e-training and that perceived ease of use influences the effects of trust on intention to use e-training. Likewise, perceived usefulness has mediated the influences of interactivity on intention and that of trust on intention (all p-values < 0.01) thus supporting the hypotheses that perceived usefulness influences the effects of interactivity on intention and that perceived usefulness influences the effects of trust on intention. However, perceived usefulness has failed to mediate the influence of perceived ease of use on intention (p-value > 0.1). This means that the hypothesis stating that perceived usefulness influences the effects of perceived ease of use on intention to use e-training was not supported.

3.2.3. Importance-Performance Map Analysis (IMPA)

In order to provide better understanding of the most important constructs influencing intention to use e-training, the researcher conducted an Importance-Performance Matrix Analysis using PLS-SEM. According to Hair Jr, Hult [59], the importance is determined by the total effects of the structural model while the performance is determined by the average values of the latent variable. The use of IPMA to identify which construct(s) in the structural model are relatively important and/or have relatively higher performance has been recommended [59]. This analysis is important as it extends findings from PLS-SEM analysis which offers direct, indirect and total relationships and extract the analysis to include another dimension showing the actual performance of each construct. The importance of carrying out IPMA analysis using SmartPLS has been previously recommended [72,73]. The results in Table 7 show the indicators’ importance-performance map which indicate that trust has the highest performance (76.62) and highest total effect (0.588). This is followed by perceived usefulness with 0.505 and 73.17 importance and performance respectively. Perceived ease of use had the least importance (0.152) though had a relatively high performance (73.77).

4. Discussion and Implications

4.1. Discussion

As mentioned earlier, successful e-training in organisations could be affected by the employee’s ability and willingness to use such system even though options available to the employee in a mandatory use environment are very limited. The present study proposes that interactivity and trust have direct influence on intention to use e-training as well as indirect influence on intention through perceived ease of use and perceived usefulness. Based on this, nine direct hypotheses and five indirect hypotheses were formulated and tested. From the findings, employees are most probably going to use e-training if such usage is easy and causes less fatigue. On the other hand, the insignificant influence of perceived ease of use on perceived usefulness is an important finding of the study as it goes contrary to one of the fundamental relationships of TAM and findings of prior studies that established a direct and significant influence of perceived ease of use on perceived usefulness [74,75]. The reason could be related to the employees’ competence and experience in computer and internet use based on their educational background and experiences in computer and internet use experiences. This most probably be the reason why the employees perceived e-training system to be inherently easy use, thereby rendering the effects of ease of on usefulness as insignificant. This suggests that perceived ease of use is not a predictor of perceived usefulness in an e-training environment. Furthermore, the findings have confirmed the influence of perceived usefulness on intention, suggesting that employees will most probably use e-training in future when they feel it is a useful tool for enhancing their ability, skills, and performance. In addition, the findings of the study have verified that interactivity can have positive influence upon employees’ e-training use intentions which confirms previous findings that demonstrated the influence of interactivity on intention [43,76,77,78,79]. This means that, the extent of interactiveness of e-training system is most likely to create a positive disposition towards e-training among employees in the universities. The employees see the interactiveness of e-training system as providing cushioning effect on absence of face-to-face training as found in the traditional training methods they are used to. Also, the results have confirmed the influence of interactivity on perceived ease of use and perceived usefulness which is consistent with previous findings [80,81]. This implies that interactiveness of the e-training system is very likely to facilitate knowledge sharing among the trainees which can influence how they navigate and make use of the system in the easiest ways. Likewise, the findings show that trust is the strongest predictor of intention to use e-training. This underscores the importance employees attached to trust while intending to use e-training in the future. The positive and significant effects of trust on perceived usefulness and perceived ease of use suggests that the level of trust employees have on e-training could create positive disposition towards it, as the risk of exposing personal information and not achieving much under e-training will no longer be issues to contend with. This also indicates that trust could increase perceptions of employees on the easiness of the e-training system. The findings have also confirmed the mediating powers of perceived usefulness and perceived ease of use in predicting intention to e-training.

4.2. Theoretical/Managerial Implications

Looking at the contributions of this study from the theory-testing perspective, the study takes an initial step toward extending and validating the research results from existing studies. The study has empirically investigated the intention to use to use e-training among university employees using the TAM with interactivity and trust factors influencing employees’ intention to use e-training through perceived usefulness and perceived ease of use. This study contributes to the literature by generating empirical evidence that supported the critical role of interactivity and trust as significant determinants of intention to use e-training. The findings reiterated the ability of TAM to explain intention; thus, supporting the extant literature [15,82]. The extension of TAM in this study reveals that, when TAM is applied in the context of e-training use intention, perceived usefulness and perceived ease of use remain strong determinants of use intention as previously established in the general IS context [15]. Contrary to the two of the main relationships of the TAM, analysis of the findings suggests that perceived ease of use has insignificant effects on perceived usefulness while perceived usefulness could not mediate the influence of perceived ease use on intention which indicates the changing perceptions of users on the relevance of perceived ease of use on perceived usefulness. The contemporary users of IS now compared to IS users three decades ago, are likely to have better self-efficacy in technology due to the technological developments and increase in technology.
Based on the findings of this study, employees’ positive perceptions on the benefits of e-training and its ease of use are most likely to be of importance for successful implementation of e-training in the universities. This requires that priority should be accorded to the aspects of e-training that justifies the benefits of its usage. For instance, employees must be convinced that e-training can be easily used and offers them the opportunity to enhance their job performance and facilitate career development. Hence, management and supervisors should be involved in improving employees’ perceptions about using e-training in the universities. In addition, management of the universities should ensure that employees overcome the fear of potentially wasting time and disclosing sensitive information among employees. In this regard, the universities need to provide trust building supports, such as giving adequate pre-implementation training to employees, enhancing their computer and internet use skills, and ease at which they can use e-training system. Essentially, supported roles of supervisors can greatly enhance trust and employee support for e-training in the universities. Likewise, the influence of interactivity on e-training use intention suggests that employees will prefer interactive features of e-training system that enhances maximisation of the benefits of e-training and that which create positive disposition towards e-training use. Thus, employees are most likely to appreciate and use interactivity tools that they can have certain control on and that which provides prompt response to their requests and other queries. Managerially, importance should be accorded to e-training systems that provides most interactivity tools and functions. Adequate training should also be given to the users of e-training. Providers of e-training services should also focus on the aspects of e-training system that enhance interactivity. The design should emphasis user control, easy communication, and prompt responses to queries. Generally, trust and perceived usefulness should be given more priority prior to the implementation of e-training for having high performance and highest total effects on intention to use e-training respectively as indicated in the IPMA findings suggested.

4.3. Limitations

While the contributions of this study to both theory and practice have been established, the study is not without some limitations that must be taken into consideration. First, this scholarly work was constrained by the fact that data collected and analysed came from employees of five federal universities of technology, where the majority of the respondents have at least a Master’s degree which could have their perceptions. A population that is well educated which might well have influenced the outcome of this study. Caution is thus warranted in generalising the findings of this study. Second, the study used an instrument adapted from the IS field and based on their application in private organisation whose validity and reliability were previously established. Second, considering the constraints of survey time, it was not possible for the researcher to include all possible factors influencing intention to use technology available in the extant literature. Third, although the instrument was further tested and validated in a pilot study and in the main survey, there is still the need for other factors associated with intention to be explored and potentially included in a more complete theoretical model. Fourth, the present study used a cross-sectional data for its analysis which inherently has limitations such providing just a snapshot perception, inability to measure some variables and uncover the meaning behind the data or causes and effects of variables.

4.4. Future Research

The limitations of this study provide a basis for conducting future research. Future research should consider conducting similar studies in a different environment and explore other factors influencing intention such as cultural lineage, group influence, and motivation in an e-training environment. Using qualitative and longitudinal studies could overcome the limitation of using cross-sectional data and to capture temporal aspects of e-training implementation.

5. Conclusions

This study deals with an important aspect of the IS literature through the prediction of employees’ intention to use e-training system by extending the TAM with interactivity and trust constructs. Previous findings have demonstrated the strong influences of interactivity and trust on intention [11,42]. Based on the analysis of the findings of this study, interactivity and trust are good predictors of intention in an e-training environment. The study has also established the relevance of the TAM’s belief factors of perceived ease of use and perceived usefulness in predicting intention to use e-training. The study has further demonstrated the applicability of TAM in predicting intention to use e-training in a pre-implementation and mandatory-use environment, and in the context of Nigeria.

Acknowledgments

The researchers wish to acknowledge that this paper is part of the ongoing PhD thesis which has been supported by Tertiary Education Trust Fund (TETFUND) through financial interventions.

Author Contributions

A. U. Alkali and Nur Naha Abu Mansor conceived and designed the study. A. U. Alkali performed data analysis and the report writing while Nur Naha Abu Mansor provided useful contributions and guidance upon which the work is conducted.

Conflicts of Interest

The authors hereby declare that the supporters of this research had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

Appendix A

Research Questionnaire
Part 1: Respondent Demographic Information
No.ItemOptions
1Gender(a) Male
(b) Female

2Age(a) Below 29 years
(b) 30–39
(c) 40–49
(d) 50 and above



3Educational level(a) Secondary
(b) Diploma
(c) Bachelor’s Degree
(d) Master’s Degree
(e) Doctoral Degree




4Staff category(a) Senior staff
(b) Junior Staff

5Computer/Internet Self-Efficacy(a) <1 year
(b) 1–3 years
(c) 4–7 years
(d) 8–11 years
(e) >12 years




Part 2: E-Training Factors
In this part of the questionnaire (Table A1, Table A2, Table A3, Table A4 and Table A5), you are please required to indicate how much you agree or disagree with each of the following statements by circling the appropriate number that best reflects your answer. The scale is between 1 to 5, where: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, and 5 = Strongly agree.
Table A1. Perceived Ease of Use.
Table A1. Perceived Ease of Use.
Please Circle the Option that Best Describes the Level at which You Agree or Disagree with the Following Statements.Strongly DisagreeDisagreeNeutralAgreeStrongly Agree
PEOU1Learning how to use e-training system would be easy for me.12345
PEOU2It would be easy to perform e-training tasks12345
PEOU3My interaction with e-training system would be clear and understandable.12345
PEOU4It would be easy for me to become skilful at using e-training system.12345
PEOU5I would find the system to be flexible to interact with12345
PEOU6On the overall, I would find e-training very easy to use12345
Table A2. Perceived Usefulness.
Table A2. Perceived Usefulness.
Please Circle the Option that Best Describes the Level at which You Agree or Disagree with the Following Statements Strongly DisagreeDisagreeNeutralAgreeStrongly Agree
PU1Using e-training would enable me to accomplish my job tasks quickly.12345
PU2Using e-training system would improve my job performance.12345
PU3Using e-training system would enhance my effectiveness.12345
PU4Using e-training would make it easier for me to manage my job.12345
PU5I believe e-training contents will be informative12345
Table A3. Interactivity.
Table A3. Interactivity.
Please Circle the Option that Best Describes the Level at which You Agree or Disagree with the Following Statements Strongly DisagreeDisagreeNeutralAgreeStrongly Agree
A. Active Control
IR1I feel I should have a lot of control over my use of e-training Web site12345
IR2While using the e-training Web site, I should choose freely what I want to see12345
IR3When using the e-training Web site, my actions should decide the kind of experience I get12345
B. Two-Way Communication
IR4The e-training Web site should be effective in gathering my feedback12345
IR5This Web site should facilitate two-way communication between the trainees and the Web site12345
IR6The Web site should make me feel it wants to listen to trainees12345
IR7The Web site should give trainees the opportunity to talk back12345
C. Synchronicity
IR8The e-training Web site should process my input very quickly12345
IR9Getting training information from the Web site should be very fast12345
IR10I should be able to obtain the information I want without any delay12345
IR11When I click on the training links, I should get instantaneous information12345
Please describe the extent of importance of the following statement based on this scale (only for this subsection): 1. Not important; 2. Low importance; 3. Neutral; 4. Very important; and 5. Extremely important.Not important Low importanceNeutralVery importantExtremely important
IR GlobalOn the overall, what level of importance do you attached to e-training system allowing you to have active control and get timely feedback?12345
Table A4. Trust.
Table A4. Trust.
Please Circle the Option that Best Describes the Level at which You Agree or Disagree with the Following Statements.Strongly DisagreeDisagreeNeutralAgreeStrongly Agree
TRS1I trust e-training systems to be reliable.12345
TRS2I trust e-training system to be secure.12345
TRS3I believe e-training systems will be trustworthy.12345
TRS4I believe my personal information will be secured under an e-training12345
TRS5I believe in e-training system’s ability to perform its functions correctly12345
TRS6E-training system will be in trainees’ best interests12345
Table A5. Intention.
Table A5. Intention.
Please Circle the Option that Best Describes the Level at which You Agree or Disagree with the Following Statements Strongly DisagreeDisagreeNeutralAgreeStrongly Agree
INT1I intent to use e-training when it will be implemented12345
INT2I intent to use e-training in order to improve my performance.12345
INT3I intent to use e-training on a regular basis.12345
INT4Given the circumstances, I would use e-training12345
INT5I would strongly recommend my colleagues to use e-training12345

References

  1. Masum, A.K.M.; Kabir, M.J.; Chowdhury, M.M. Determinants that Influencing the Adoption of E-HRM: An Empirical Study on Bangladesh. Asian Soc. Sci. 2015, 11, 117. [Google Scholar] [CrossRef]
  2. Koontz, H.; Weihrich, H. Essentials of Management, 6th ed.; Tata McGraw-Hill Publication Co: New Delhi, India, 2006. [Google Scholar]
  3. Khademi, M.; Kabir, H.; Haghshenas, M. E-learning as a Powerful Tool for Knowledge Management. In Proceedings of the International Conference on Distance Learning and Education (ICDLE 2011), Singapore, 16–18 Septenber 2011; pp. 16–18. [Google Scholar]
  4. Ellis, P.F.; Kuznia, K.D. Corporate eLearning Impact on Employees. Glob. J. Bus. Res. 2014, 8, 1. [Google Scholar]
  5. Bhuasiri, W.; Xaymoungkhoun, O.; Zo, H.; Rho, J.J.; Ciganek, A.P. Critical Success factors for e-learning in developing countries: A comparative analysis between ICT experts and faculty. Comput. Edu. 2012, 58, 843–855. [Google Scholar] [CrossRef]
  6. Batalla-Busquets, J.-M.; Martínez-Argüelles, M.-J. Determining factors in online training in companies. Int. J. Manag. Educ. 2014, 12, 68–79. [Google Scholar] [CrossRef]
  7. Kamal, K.B.; Aghbari, M.A.; Atteia, M. E-Training & Employees’ Performance a Practical Study on the Ministry of Education in the Kingdom of Bahrain. J. Resour. Dev. Manag. 2016, 18, 46–58. [Google Scholar]
  8. Jhurree, V. Technology integration in education in developing countries: Guidelines to policy makers. Int. Educ. J. 2005, 6, 467–483. [Google Scholar]
  9. Md Nor, K.; Pearson, J.M. The Influence of Trust on Internet Banking Acceptance. J. Internet Bank. Commer. 2007, 12, 1–10. [Google Scholar]
  10. Chatzoglou, P.D.; Sarigiannidis, L.; Vraimaki, E.; Diamantidis, A. Investigating Greek employees’ intention to use web-based training. Comput. Educ. 2009, 53, 877–889. [Google Scholar] [CrossRef]
  11. Gefen, D.; Karahanna, E.; Straub, D.W. Trust and TAM in online shopping: An integrated model. MIS Q. 2003, 27, 51–90. [Google Scholar]
  12. Koç, T.; Turan, A.H.; Okursoy, A. Acceptance and usage of a mobile information system in higher education: An empirical study with structural equation modeling. Int. J. Manag. Educ. 2016, 14, 286–300. [Google Scholar] [CrossRef]
  13. Zainab, B.; Bhatti, M.A.; Pangil, F.B.; Battour, M.M. E-training adoption in the Nigerian civil service. Eur. J. Train. Dev. 2015, 39, 538–564. [Google Scholar] [CrossRef]
  14. Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef]
  15. Šumak, B.; HeričKo, M.; Pušnik, M. A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Comput. Hum. Behav. 2011, 27, 2067–2077. [Google Scholar] [CrossRef]
  16. Marangunić, N.; Granić, A. Technology acceptance model: A literature review from 1986 to 2013. Univers. Access Inf. Soc. 2015, 14, 81–95. [Google Scholar] [CrossRef]
  17. Park, N.; Rhoads, M.; Hou, J.; Lee, K.M. Understanding the acceptance of teleconferencing systems among employees: An extension of the technology acceptance model. Comput. Hum. Behav. 2014, 39, 118–127. [Google Scholar] [CrossRef]
  18. Hamid, A.A.; Razak, F.Z.A.; Bakar, A.A.; Abdullah, W.S.W. The Effects of Perceived Usefulness and Perceived Ease of Use on Continuance Intention to Use E-Government. Procedia Econ. Financ. 2016, 35, 644–649. [Google Scholar] [CrossRef]
  19. Liu, Y. Developing a scale to measure the interactivity of websites. J. Advert. Res. 2003, 43, 207–216. [Google Scholar] [CrossRef]
  20. Abdullah, F.; Ward, R.; Ahmed, E. 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. Comput. Hum. Behav. 2016, 63, 75–90. [Google Scholar] [CrossRef]
  21. Venkatesh, V.; Bala, H. Technology Acceptance Model 3 and a Research Agenda on Interventions. Decis. Sci. 2008, 39, 273–315. [Google Scholar] [CrossRef]
  22. Mbarek, R.; Zaddem, F. The examination of factors affecting e-learning effectiveness. Int. J. Innov. Appl. Stud. 2013, 2, 423–435. [Google Scholar]
  23. Cheng, Y.-M. Antecedents and consequences of e-learning acceptance. Inf. Syst. J. 2011, 21, 269–299. [Google Scholar] [CrossRef]
  24. Shen, J.; Eder, L.B. Intentions to use virtual worlds for education. J. Inf. Syst. Educ. 2009, 20, 225. [Google Scholar]
  25. Yu, U.-J.; Damhorst, M.L. Body Satisfaction as Antecedent to Virtual Product Experience in an Online Apparel Shopping Context. Cloth. Text. Res. J. 2015, 33, 3–18. [Google Scholar] [CrossRef]
  26. Joo, Y.J.; Kim, N.; Kim, N.H. Factors predicting online university students’ use of a mobile learning management system (m-LMS). Educ. Technol. Res. Dev. 2016, 64, 1–20. [Google Scholar] [CrossRef]
  27. Hu, P.J.-H.; Clark, T.H.; Ma, W.W. Examining technology acceptance by school teachers: A longitudinal study. Inf. Manag. 2003, 41, 227–241. [Google Scholar] [CrossRef]
  28. Purnomo, S.H.; Lee, Y.-H. E-learning adoption in the banking workplace in Indonesia an empirical study. Inf. Dev. 2013, 29, 138–153. [Google Scholar] [CrossRef]
  29. Huang, Y.-M. The factors that predispose students to continuously use cloud services: Social and technological perspectives. Comput. Educ. 2016, 97, 86–96. [Google Scholar] [CrossRef]
  30. Rouibah, K.; Hamdy, H.I.; Al-Enezi, M.Z. Effect of management support, training, and user involvement on system usage and satisfaction in Kuwait. Ind. Manag. Data Syst. 2009, 109, 338–356. [Google Scholar] [CrossRef]
  31. Tsai, T.-H.; Chang, H.-T.; Ho, Y.-L. Perceptions of a Specific Family Communication Application among Grandparents and Grandchildren: An Extension of the Technology Acceptance Model. PLoS ONE 2016, 11, e0156680. [Google Scholar] [CrossRef] [PubMed]
  32. Li, Y.-H.; Huang, J.-W. Applying theory of perceived risk and technology acceptance model in the online shopping channel. World Acad. Sci. Eng. Technol. 2009, 53, 919–925. [Google Scholar]
  33. Bhattacherjee, A.; Hikmet, N. Reconceptualizing organizational support and its effect on information technology usage: Evidence from the health care sector. J. Comput. Inf. Syst. 2008, 48, 69–76. [Google Scholar]
  34. Tarhini, A.; Hone, K.; Liu, X. The effects of individual differences on e-learning users’ behaviour in developing countries: A structural equation model. Comput. Hum. Behav. 2014, 41, 153–163. [Google Scholar] [CrossRef]
  35. Kuo, Y.-F.; Yen, S.-N. Towards an understanding of the behavioral intention to use 3G mobile value-added services. Comput. Hum. Behav. 2009, 25, 103–110. [Google Scholar] [CrossRef]
  36. Lee, J.; Choi, M.; Lee, H. Factors affecting smart learning adoption in workplaces: Comparing large enterprises and SMEs. Inf. Technol. Manag. 2015, 16, 291–302. [Google Scholar] [CrossRef]
  37. Singh, H.; Singh, B.P. E-Training: An assessment tool to measure business effectiveness in a business organization. Proceedings of 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, 11–13 March 2015; pp. 1229–1231. [Google Scholar]
  38. Moore, M.G.; Kearsley, G. Distance Education: A Systems View of Online Learning; Cengage Learning: Boston, MA, USA, 2011. [Google Scholar]
  39. Hirumi, A. The design and sequencing of online and blended learning interactions: A framework for grounded design. Can. Learn. J. 2011, 16, 21–25. [Google Scholar]
  40. Herrington, J.; Reeves, T.C.; Oliver, R. Authentic tasks online: A synergy among learner, task, and technology. Distance Educ. 2006, 27, 233–247. [Google Scholar] [CrossRef]
  41. Chou, C.; Peng, H.; Chang, C.-Y. The technical framework of interactive functions for course-management systems: Students’ perceptions, uses, and evaluations. Comput. Educ. 2010, 55, 1004–1017. [Google Scholar] [CrossRef]
  42. Wang, H. To Investigate Relative Effectiveness of the Dimensions Of Interactivity; University of Portsmouth: Portsmouth, UK, 2011. [Google Scholar]
  43. Liu, I.-F.; Chen, M.C.; Sun, Y.S.; Wible, D.; Kuo, C.-H. Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community. Comput. Education 2010, 54, 600–610. [Google Scholar] [CrossRef]
  44. Wu, G.; Wu, G. Conceptualizing and measuring the perceived interactivity of websites. J. Curr. Issues Res. Advert. 2006, 28, 87–104. [Google Scholar] [CrossRef]
  45. Lee, Y.-H.; Hsieh, Y.-C.; Chen, Y.-H. An investigation of employees' use of e-learning systems: Applying the technology acceptance model. Behav. Inf. Technol. 2013, 32, 173–189. [Google Scholar] [CrossRef]
  46. Macy, M.W.; Skvoretz, J. The evolution of trust and cooperation between strangers: A computational model. Am. Sociol. Rev. 1998, 63, 638–660. [Google Scholar] [CrossRef]
  47. Luhmann, N. Familiarity, confidence, trust: Problems and alternatives. Trust Mak. Break. Coop. Relat. 2000, 6, 94–107. [Google Scholar]
  48. Lewicka, D.; Krot, K. The model of HRM-trust-commitment relationships. Ind. Manag. Data Syst. 2015, 115, 1457–1480. [Google Scholar] [CrossRef]
  49. McKnight, D.H.; Choudhury, V.; Kacmar, C. Developing and validating trust measures for e-commerce: An integrative typology. Inf. Syst. Res. 2002, 13, 334–359. [Google Scholar] [CrossRef]
  50. Yoon, C. The effects of national culture values on consumer acceptance of e-commerce: Online shoppers in China. Inf. Manag. 2009, 46, 294–301. [Google Scholar] [CrossRef]
  51. Chai, S.; Kim, M. What makes bloggers share knowledge? An investigation on the role of trust. Int. J. Inf. Manag. 2010, 30, 408–415. [Google Scholar] [CrossRef]
  52. Chiu, C.-M.; Hsu, M.-H.; Wang, E.T. Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories. Decis. Support Syst. 2006, 42, 1872–1888. [Google Scholar] [CrossRef]
  53. Yusoff, Y.M.; Ramayah, T.; Othman, N.-Z. Why Examining Adoption Factors, HR Role and Attitude towards Using E-HRM is the Start-Off in Determining the Successfulness of Green HRM? J. Adv. Manag. Sci. 2015, 3. [Google Scholar] [CrossRef]
  54. Wang, S.W.; Ngamsiriudom, W.; Hsieh, C.-H. Trust disposition, trust antecedents, trust, and behavioral intention. Serv. Ind. J. 2015, 35, 555–572. [Google Scholar] [CrossRef]
  55. Cooper, D.R.; Schindler, P.S. Business Research Methods, 7th ed.; McGraw-Hill Irwin: Singapore, 2001. [Google Scholar]
  56. Venkatesh, V.; Davis, F.D. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manag. Sci. 2000, 46, 186–204. [Google Scholar] [CrossRef]
  57. Dix, S.; Ferguson, G.; Son, J.; Sadachar, A.; Manchiraju, S.; Fiore, A.M.; Niehm, L.S. Consumer adoption of online collaborative customer co-design. J. Res. Interact. Market. 2012, 6, 180–197. [Google Scholar]
  58. Zhou, T. Examining the critical success factors of mobile website adoption. Online Inf. Rev. 2011, 35, 636–652. [Google Scholar] [CrossRef]
  59. Hair, J.F., Jr.; Hult, G.T.M.; Ringle, C.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM); Sage Publications, Inc.: Thousand Oaks, CA, USA, 2014. [Google Scholar]
  60. Ramayah, T.; Cheah, J.; Chuah, F.; Ting, H.; Meman, M.A. Partial Least Squares Structural Equation Modeling (PLS-SEM) Using SmartPLS 3.0; Pearson Malaysia Sdn Bhd: Kuala Lumpur, Malaysia, 2016. [Google Scholar]
  61. Fornell, C.; Larcker, D.F. Structural equation models with unobservable variables and measurement error: Algebra and statistics. J. Market. Res. 1981, 18, 382–388. [Google Scholar] [CrossRef]
  62. Nunnally, J. psychometric methods; McGraw-Hill: New York, NY, USA, 1978. [Google Scholar]
  63. Cronbach, L.J. Coefficient alpha and the internal structure of tests. Psychometrika 1951, 16, 297–334. [Google Scholar] [CrossRef]
  64. Chin, W.W. The partial least squares approach to structural equation modeling. Modern Methods Bus. Res. 1998, 295, 295–336. [Google Scholar]
  65. Kline, R. Principles And Practice of Structural Equation Modeling; Guilford Press Google Scholar: New York, NY, USA, 2011. [Google Scholar]
  66. Gold, A.H.; Arvind Malhotra, A.H.S. Knowledge management: An organizational capabilities perspective. J. Manag. Inf. Syst. 2001, 18, 185–214. [Google Scholar]
  67. Diamantopoulos, A.; Siguaw, J.A. Formative versus reflective indicators in organizational measure development: A comparison and empirical illustration. Br. J. Manag. 2006, 17, 263–282. [Google Scholar] [CrossRef]
  68. Hair, J.F.; Ringle, C.M.; Sarstedt, M. PLS-SEM: Indeed a silver bullet. J. Market. Theory Pract. 2011, 19, 139–152. [Google Scholar] [CrossRef]
  69. Urbach, N.; Ahlemann, F. Structural equation modeling in information systems research using partial least squares. JITTA 2010, 11, 5–40. [Google Scholar]
  70. Preacher, K.J.; Hayes, A.F. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav. Res. Methods 2008, 40, 879–891. [Google Scholar] [CrossRef] [PubMed]
  71. Hayes, A.F. Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Commun. Monogr. 2009, 76, 408–420. [Google Scholar] [CrossRef]
  72. Ringle, C.M.; Sarstedt, M. Gain more insight from your PLS-SEM results: The importance-performance map analysis. Ind. Manag. Data Syst. 2016, 116, 1865–1886. [Google Scholar] [CrossRef]
  73. Ramayah, T.; Chiun, L.M.; Rouibah, K.; May, O.S. Identifying priority using an importance-performance matrix analysis (ipma): The case of internet banking in Malaysia. Int. J. E-Adopt. (IJEA) 2014, 6, 1–15. [Google Scholar] [CrossRef]
  74. Sun, P.-C.; Tsai, R.J.; Finger, G.; Chen, Y.-Y.; Yeh, D. What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Comput. Educ. 2008, 50, 1183–1202. [Google Scholar] [CrossRef]
  75. Cheung, R.; Vogel, D. Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Comput. Educ. 2013, 63, 160–175. [Google Scholar] [CrossRef]
  76. Wang, M. Integrating organizational, social, and individual perspectives in Web 2.0-based workplace e-learning. Inf. Syst. Front. 2011, 13, 191–205. [Google Scholar] [CrossRef][Green Version]
  77. Qutaishat, F.T. Users’ perceptions towards website quality and its effect on intention to use e-government services in Jordan. Int. Bus. Res. 2012, 6, 97. [Google Scholar] [CrossRef]
  78. Popescu, D.; Avramescu, E.T.; Popescu, L.C.; Pascual, J.L.; Pratikakis, I.; Perantonis, S.; Lupescu, O. A Web-based E-training Platform for biomedical engineering education. In Proceedings of the 2014 International Conference and Exposition on Electrical and Power Engineering (EPE), Iasi, Romania, 16–18 October 2014; pp. 162–167. [Google Scholar]
  79. Hew, T.-S.; Syed Abdul Kadir, S.L. Behavioural intention in cloud-based VLE: An extension to Channel Expansion Theory. Comput. Hum. Behav. 2016, 64, 9–20. [Google Scholar] [CrossRef]
  80. Pai, F.-Y.; Yeh, T.-M. The effects of information sharing and interactivity on the intention to use social networking websites. Qual. Quant. 2014, 48, 2191–2207. [Google Scholar] [CrossRef]
  81. Kang, S.-U.; Park, S.; Lee, S. Factors influencing new media subscription based on multigroup analysis of IPTV and DCTV. ETRI J. 2014, 36, 1041–1050. [Google Scholar] [CrossRef]
  82. Bhattacherjee, A. Understanding information systems continuance: An expectation-confirmation model. MIS Q. 2001, 25, 351–370. [Google Scholar] [CrossRef]
Figure 1. Technology Acceptance Model (TAM).
Figure 1. Technology Acceptance Model (TAM).
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Figure 2. Formative Construct’s Convergent Validity.
Figure 2. Formative Construct’s Convergent Validity.
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Figure 3. Structural Model.
Figure 3. Structural Model.
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Table 1. Respondent Demographics.
Table 1. Respondent Demographics.
DemographicsFeaturesFrequencyPercentage (%)
GenderMale11754.9
Female9745.1
Total214100
AgeBelow 294219.6
30–397032.9
40–496630.9
50 and above3616.6
Total214100
EducationSecondary94.3
Diploma2110
Bachelors’ Degree4118.9
Master’s Degree8338.9
Doctoral Degree6027.9
Total214100
Staff CategoryAcademic9544.5
Non-Academic11955.5
Total214100.0
Computer/Internet Self-Efficacy Experience<1 year2813
1–3 years7535.2
4–7 years6831.6
8–11 years2913.6
>12 years146.6
Total214100.0
Table 2. Reflective Measurement Model Results.
Table 2. Reflective Measurement Model Results.
ConstructsItemsLoadingsAVECRCronbach Alpha
INTINT10.7600.750.9370.915
INT20.849
INT30.898
INT40.914
INT50.899
IRACIRAC10.8250.7040.8770.792
IRAC20.826
IRAC30.866
IRCMIRCM10.7670.6660.8890.832
IRCM20.817
IRCM30.870
IRCM40.808
IRSNIRSN10.7690.6080.8610.785
IRSN20.776
IRSN30.779
IRSN40.794
PEOUPEOU10.7390.5720.8420.752
PEOU20.780
PEOU30.741
PEOU40.763
PUPU10.7000.6770.9260.903
PU20.874
PU30.889
PU40.817
PU50.839
PU60.805
TRTRS10.7690.6220.9080.879
TRS20.806
TRS30.804
TRS40.806
TRS50.766
TRS60.782
Table 3. Discriminant Validity.
Table 3. Discriminant Validity.
Fornell–Larcker CriterionHeterotrait–Monotrait Ratio (HTMT)
INTIRACIRCMIRSNPEOUPUTR INTIRACIRCMIRSNPEOUPUTR
INT0.866 INT
IRAC0.5780.839 IRAC0.676
IRCM0.6710.5650.816 IRCM0.7720.675
IRSN0.4850.5140.5740.780 IRSN0.5740.6420.705
PEOU0.6210.6030.5880.5250.756 PEOU0.7420.7580.7440.665
PU0.8000.4810.6930.5020.5370.823 PU0.8760.5610.7980.5870.649
TR0.7480.5030.6240.5800.6060.7130.789TR0.8330.5910.7230.6990.7320.793
Table 4. Formative Construct’s Properties.
Table 4. Formative Construct’s Properties.
ConstructItemsConvergent ValidityWeightsVIFt-Value WeightsSig
InteractivityIRAC0.700.3411.5944.8870.000
IRCM0.6401.7489.7110.000
IRSN0.1801.6192.4790.013
Table 5. Structural Model Results.
Table 5. Structural Model Results.
RelationshipsPath CoefficientS.Et Valuesp Values5.0%95.0%Sig. LevelDecision
PEOU → INT0.1320.0562.3650.0180.0250.247**Supported
PEOU → PU−0.0040.0680.0550.956−0.1280.136nsnot supported
PU → INT0.4530.0637.2220.0000.3250.571***Supported
IR → INT0.1380.0642.1600.0310.0100.259**Supported
IR → PU0.3990.0884.5300.0000.2340.577***Supported
IR → PEOU0.4900.0716.9290.0000.3510.631***Supported
TR → INT0.2510.0723.4670.0010.1070.387***Supported
TR → PU0.4460.0825.4640.0000.2690.590***Supported
TR → PEOU0.2750.0664.1780.0000.1450.406***Supported
* p < 0.01, ** p < 0.05, *** p < 0.01 level of significance; ns = not significant.
Table 6. Mediation Results.
Table 6. Mediation Results.
RelationshipsBetaS.Et Valuesp Values2.50%97.50%Sig. LevelDecision
IR →PEOU → INT0.0620.0321.9380.0530.0100.134*Supported
IR → PU → INT0.1860.0483.8720.0000.1010.287***Supported
TR → PU → INT0.1980.0454.3640.0000.1070.285***Supported
TR → PEOU → INT0.0330.0162.1240.0340.0060.068***Supported
PEOU → PU → INT−0.0010.0300.0310.976−0.0580.058nsnot supported
* p < 0.01, ** p < 0.05, *** p < 0.01 level of significance; ns = not significant.
Table 7. Importance-Performance Map Analysis (IPMA).
Table 7. Importance-Performance Map Analysis (IPMA).
ConstructImportancePerformance
IR0.33161.349
PEOU0.15273.770
PU0.50573.173
TR0.58876.617

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