Examining Virtual Learning Acceptance Among Higher Learning Institutions' Lecturers: A UTAUT Theory Approach

: This study examined virtual learning acceptance among Higher Learning Institutions' lecturers using the UTAUT theory approach, notably the Institute of Accountancy Arusha and the Institute of Finance Management. A descriptive research design was used in this study, and a convenient sampling technique was employed. Both primary and secondary data were used in the data collection exercise. The data collected was processed and analysed using the Statistical Package for Social Sciences. Findings revealed that performance expectancy, effort expectancy, and social influence positively affect behavioural intention. HLI's lecturers accept virtual learning. The results revealed that facilitating conditions positively affect user behaviour to accept virtual learning. The findings of this study showed that behavioural intention positively affects user behaviour when taking virtual learning. This study recommends that future studies be designed to compare and contrast the predictive power of the different competing theories, such as the theory of planned behaviour, the technology acceptance model, the theory of reasoned action, and the diffusion of innovation theory.


Introduction
The coronavirus pandemic of 2019 (COVID-19) ravaged the planet, forcing half of the world to shut down by April 2020.Countries worldwide have made drastic efforts to halt the virus's spread.While these steps are necessary, they have significant consequences in every element of people's lives.For example, the temporary closure of schools, colleges, and other educational institutions has resulted in more than 91 per cent of students worldwide, or approximately 1.6 billion students, remaining indoors and unable to attend classes as usual (UNICEF, 2020).Such upheaval in the education system is unparalleled.
Furthermore, the crisis has greatly impacted the education of the most disadvantaged and marginalised students.Even though the COVID-19 epidemic is interrupting global learning and education, governments and other organisations are attempting to offer their students as much "non-stop teaching and learning" as possible (Bao, 2021& Lie, 2020).For example, as soon as COVID-19 broke out in Wuhan, China, millions of Chinese students and teachers switched from traditional to online learning (Bao, 2021).Alsabawy et al. (2021) described virtual learning technology as a wide variety of communication, information, and associated technologies that support teaching, learning, and assessment; it is learning that is electronic, Internet-enabled, or Web-enabled.
Computers, mobile communications, newsgroups, and interactive television are all used in virtual learning (Xu & Wang, 2019).Because of the quick shift in technological advancement and the globalisation trend in higher education, and the elimination of barriers among students, new approaches and perspectives for virtual learning have emerged.Information technology is employed in education to help students study more effectively while also assisting lecturers with administrative work (Elsink & Dobra, 2020;Lubua et al., 2017;Semlambo et al., 2022).
In reality, virtual learning has steadily become a crucial aspect in improving teaching and learning and deciding an institution's success or failure (Arafat 2022).As a result, technology is rapidly being integrated into classrooms to improve and enhance the learning of lecturers and students.To be successful, lecturers must accept and use virtual learning systems appropriately.As an educated part of society, lecturers are thought to have a solid fundamental understanding of the benefits of employing technology for virtual learning and would thus use it.However, in Tanzanian higher learning institutions (HLIs), lecturers' higher levels of expertise do not always translate into significantly better levels of acceptance and use of virtual learning (Sindhi, 2022).The variable level of acceptability among lecturers is a crucial hurdle to implementing virtual learning in education.Indeed, the acceptance of virtual learning is influenced by various circumstances.However, there is no agreement on which elements contribute to the acceptability of virtual learning.Over time, this has resulted in several technology adoption theories.These theories describe the interdependence of several factors that influence the acceptance and application of virtual learning.The Unified Theory of Acceptance and Use of Technology (UTAUT) is one of the most used theories in virtual learning and other fields.
This study, therefore, aimed to examine virtual learning acceptance among HLI's lecturers using a UTAUT Theory Approach with special reference to the Institute of Accountancy Arusha and the Institute of Finance Management.The researcher intended to establish how determinants of UTAT theory improve teaching and encourage lecturers in HLIs in Tanzania to use virtual learning.

Literature Review
Unified Theory of Acceptance and Use of Technology (UTAUT) Venkatesh et al. (2016) created the Unified Theory of Acceptance and Use of Technology (UTAUT) model to unify earlier TAM-related studies.To predict the behavioural intention of the use of technology and an existing technology used primarily in organisational contexts, UTAUT identifies four key factors (i.e., performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FI)) and four moderators (i.e., age, gender, experience, and voluntariness) (Venkatesh et al., 2016).PE refers to lecturers' view that virtual learning would assist them in achieving their institutional goals in this study, and ease of use of the virtual learning system is referred to as EE.In contrast, the virtual learning system's perceived importance is called SI (Chang et al., 2017).According to Walters (2014), FC represents the extent to which lecturers feel institutional resources assist using the virtual learning system.BI has been described as the degree to which a person has made intentional decisions about whether or not to engage in a specific future behaviour (Marz, 2016).
The goal of employing UTAUT is to determine user acceptability and usage behaviour concerning technology, as shown in Figure 1.

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UTAUT presents a sophisticated understanding of how the determinants of intention and behaviour vary over time.It is critical to note that most of the significant linkages in the model are regulated (Luhamya, Bakkabulindi, and Muyinda, 2017).Using the UTAUT model aims to allow the researcher to determine the strength of the predictors, performance expectation (PE) and effort expectancy (EE) on lecturers' willingness to accept and use virtual learning technology for teaching.According to Venkatesh et al. (2003) and Bagozzi (2007), as quoted by Luhamya et al. (2017), the UTAUT model may describe the technological adoption behaviour of lecturers at a specific institution of learning.

Figure 1. UTAUT Theory
Source: UTAUT Theory adopted from Venkatesh et al. (2016) For the purpose of this study, the moderating variables, namely Gender, Age, Experience and Voluntariness of use, have been included.From the UTAUT, the following four hypotheses were formulated and tested (H1-H5): H1: Performance expectancy has a positive effect on behavioural intention to accept Virtual learning H1a: Gender has a mediating effect on the relationship between performance expectancy and behavioural intention to accept virtual learning H1b: Age has a mediating effect on the relationship between performance expectancy and behavioural intention to accept virtual learning H2: Effort expectancy has a positive effect on behavioural intention to accept virtual learning H2a: Gender has a mediating effect on the relationship between effort expectancy and behavioural intention to accept virtual learning H2b: Age has a mediating effect on the relationship between effort expectancy and behavioural intention to accept virtual learning H2c: Experience has a mediating effect on the relationship between effort expectancy and behavioural intention to accept virtual learning H3: Social influence has a positive effect on behavioural intention to accept virtual learning H3a: Age has a mediating effect on the relationship between social influence and behavioural intention to accept virtual learning H3b: Experience has a mediating effect on the relationship between social influence and behavioural intention to accept virtual learning H3c: Voluntariness of use has a mediating effect on the relationship between social influence and behavioural intention to accept virtual learning H4: Facilitating conditions have a positive effect on user behaviour to accept virtual learning H4a: Age has a mediating effect on the relationship between facilitating conditions and user behaviour to accept virtual learning H4b: Experience has a mediating effect on the relationship between facilitating conditions and user behaviour to accept virtual learning H4c: Voluntariness of use has a mediating effect on the relationship between Facilitating conditions and user behaviour to accept virtual learning H5: Behaviour intention has a positive effect on user behaviour to accept virtual learning Literature Review Hamidi and Chavoshi (2018) found no association between gender and performance expectations, effort expectancy, or social influence.Abbad (2021) investigated the UTAUT model to understand the use of elearning systems better.The findings revealed that performance and effort expectations influenced behavioural intent to utilise e-learning systems, but social influence did not.Furthermore, the findings demonstrated the direct impact of behavioural goals and favourable environments on using e-learning systems.Similarly, Phrino (2021) discovered that increasing perceived utility increases the propensity to employ learning technologies.Mulla et al. (2021) indicated that performance anticipation was the primary cause of digital learning environment acceptability (together with social influence).Furthermore, Lucy (2021) discovered that higher degrees of social presence influence teachers' involvement in using ICT or LMS technologies.
Al-Adwan et al. ( 2018) discovered a favourable influence of EE on BI.Despite this, Phrino (2021) found that the negative impact of EE on BI and social power and perceived simplicity of use enhanced the perceived utility of a webbased learning system.Using two UTAUT constructs, Aboki (2021) explored the Federal University of Wukari lecturers' performance expectations and effort expectancy influences in teaching.The findings demonstrated that performance expectations and effort expectancy positively impacted lecturers' ICT usage for teaching.
Chao (2019) used the expanded unified theory of acceptance and use of technology (UTAUT) model with the addition of perceived enjoyment, mobile self-efficacy, satisfaction, trust, and perceived risk moderators to investigate consumers' behavioural intention to utilise mlearning.The findings demonstrated that contentment, trust, performance expectation, and effort expectancy substantially and positively affected behavioural intention.Perceived enjoyment, performance expectations, and effort expectancy were all related to behavioural intention favourably.Perceived satisfaction was highly influenced by mobile self-efficacy.Perceived risk also had a substantial negative moderating influence on the connection between performance expectancy and behavioural intention.Radovan and Kristl (2017) investigated the influence of a fundamental Theory of Acceptance and Use of Technology (UTAUT) structural model, with the CoI framework serving as a supplement.The latter adds three additional elements to LMS usage for educational purposes, illustrating the complex cognitive and social characteristics of virtual teaching.The results showed that the immediate social effect at work is the most important element for LMS adoption by university professors, while the creation of the learning process is heavily influenced by the features of the LMS tools and the perceived utility of the application.Furthermore, social Influence is significantly associated with individuals' learning management system behaviour intentions and thus positively influences consumer behaviour in adopting e-learning systems.
COVID-19 fear can influence e-learning system performance expectations, social influence (SI), and behaviour intention (Nabity-Grover et al., 2020).In other words, the existence of COVID-19 anxiety enhances the connection between PE, SI, and BI.The moderating effect of COVID-19 fear was statistically significant and had a negative impact on PE while having a positive impact on SI, implying that the more COVID-19 fear a student experiences, the less likely they are to adopt e-learning systems due to performance expectations.At the same time, they are more likely to use online tools after hearing insights from their peers, friends, instructors, and classmates (Raza et al., 2020).On the other hand, Masalu (2020) found that despite having a medium grand mean for the scores of reply statements, social impact, website quality, and enabling conditions had no significant direct effect on behavioural intention to utilise e-learning services.Furthermore, the data demonstrated a clear relationship between behavioural intention and actual behavioural usage of e-learning services.Matuso (2021) investigated the extent to which the Technology Acceptance Model (TAM) elements predict the intention to adopt and use technology in learning.The study's findings revealed that gender, age, and experience moderated performance expectations, effort expectancy, and social influence, all related to behavioural intention to utilise online learning.Furthermore, behavioural intention shows a favourable and substantial relationship with actual usage.Lary (2017) investigated the behavioural intentions and utilisation of online learning among lecturers.The study found that the moderating influence of gender, age, and experience might be supported in enabling circumstances and online learning user behaviour.It was also discovered that the lecturers' genuine behavioural purpose for using online learning and the supporting settings were predictors of their use of online learning.
According to the available literature, extensive studies have been conducted on adopting and using virtual learning.However, the literature reveals two significant gaps.First, the preponderance of these studies has focused on developed countries, while developing countries receive relatively less attention; second, they strongly emphasize students rather than lecturers.To fill this void, this study examined virtual learning acceptance among HLI's lecturers using A UTAUT Theory Approach with special reference to the Institute of Accountancy Arusha and the Institute of Finance Management.

Methodology
A descriptive research design was adopted in this study.A convenient sampling strategy was used to choose 128 academic staff members from the Institute of Accountancy Arusha and the Institute of Finance Management.This sampling approach was chosen due to the difficulty of accessing the sampling frame.The researcher visited the chosen institutions to give questionnaires to academic staff who agreed to participate in the study.Both primary and secondary data were collected at the end of the data-gathering process.A questionnaire was used to obtain preliminary data.For this study, the researcher administered closed-ended questionnaires to the subjects.The questionnaires were created with the study's objective in mind.Closed-ended Likert scale statements were used on the questionnaire forms (quantitative data).The questionnaire was scored on a five-point Likert scale (Strongly Agree = 5, Agree = 4, Not sure = 3, Disagree = 2, Strongly Disagree = 1).The questionnaire items were created after carefully evaluating the literature on this topic.Secondary data was gathered from various published and unpublished sources, including journal articles, seminars, conference proceedings, research reports, and dissertations.The Statistical Package for the Social Sciences (SPSS) was used to process and analyse the gathered data.

Results
In this section, the results were presented, followed by a discussion of the findings.The relationship between the independent and dependent variables was studied with a regression analysis.Source: Field Data (2022).
From the above table, the study findings showed a significant relationship between FE and BI, as the p-value is 0.038.Likewise, the study findings showed a significant relationship between FC and BI, as the p-value is 0.000.On the other hand, findings showed an insignificant relationship between SI and BI, as the p-value is 0.073.Also, the results revealed a significant relationship between FC and BI, as it shows the p-value is 0.001.Moreover, study findings showed a significant relationship between BI and BI as the p-value is 0.0001.Age has a mediating effect on the relationship between social influence and behavioural intention to accept virtual learning .000Supported

H3b
Experience has a mediating effect on the relationship between social influence and behavioural intention to accept virtual learning .000Supported

H3c
Voluntariness of use has a mediating effect on the relationship between social influence and behavioural intention to accept virtual learning .812Not Supported

H4
Facilitating conditions have a positive effect on user behaviour to accept virtual learning .001Supported

H4a
Gender has a mediating effect on the relationship between Facilitating conditions and user behaviour to accept virtual learning .000Supported

H4b
Age has a mediating effect on the relationship between Facilitating conditions and user behaviour to accept Virtual learning .000Supported

H4c
Experience has a mediating effect on the relationship between Facilitating conditions and user behaviour to accept Virtual learning .009Supported

H5
Behaviour intention has a positive effect on user behaviour to accept virtual learning .000Supported Source: Field Data (2022).
Experience: A mediation model of experience with EE, SI, and FC was tested to investigate the influence of the mediators in the model.The study findings showed an insignificant relationship between experience and EE, as the p-value is 0.766.However, the study findings showed a significant relationship between experience and SI, as the p-value is 0.0001.Also, findings showed a significant relationship between experience and FC, as the p-value is 0.009.
Voluntariness of use: A mediation model of experience with FC was tested to investigate the influence of the mediators in the model.The study findings showed an insignificant relationship between voluntariness of use and FC, as the p-value is 0.812.
UTAUT theory consists of four constructs; performance expectancy, effort expectancy, social influence, and facilitating conditions with moderating variables, namely Gender, Age, Experience, and Voluntariness of use.From Table 3 above, the findings show that all of the hypotheses tested by the researcher were supported.Only five hypotheses were not supported.These hypotheses were H1a, H2a, H2c, and H3c.

Discussion
The performance of information systems may be determined by system and information quality, which includes dependability, reaction time, content, and availability.The factors of usefulness are expanded to include service quality and system utilisation.Ease of use and system supportability should also be evaluated as usefulness determinants.This study revealed that performance expectancy positively affects behavioural intention to accept virtual learning.This result is consistent with Abbad (2021), and Pang et al. (2020) findings, who found that performance and effort expectations influenced behavioural intentions to utilise e-learning systems.
Furthermore, these findings are consistent with Phrino's (2021) findings, which suggest that increased perceived usefulness leads to a stronger inclination to employ learning technology.Similarly, Mulla et al. (2021) proposed that performance anticipation was the primary cause of digital learning environment acceptability (together with social influence).Similarly, Chao (2019) found that performance expectation was positively related to behavioural intention.As a result, a bigger influence on embracing virtual learning among HLI professors is a conviction in its use (PE).
According to the findings, gender did not mediate the association between performance expectation and behavioural intention to embrace virtual learning.On the other hand, age moderated the relationship between performance expectation and behavioural intention to embrace virtual learning.However, Matuso (2021) found that gender and age moderated each performance expectation since they relate to behavioural intention to use online learning.
When analysing the effort of virtual learning, it is critical to evaluate the characteristics of lecturers, teaching materials, and the design of learning activities.Findings indicated that effort expectancy positively affects behavioural intention to accept virtual learning.These findings are congruent with Al-Adwan et al. (2018), who discovered a favourable influence of effort expectation on behavioural intention.Abbad (2021), Aboki (2021), andChao (2019) discovered that effort expectation impacts lecturers' usage of ICT for teaching.However, Phrino (2021) found that effort expectation had a detrimental influence on behavioural intention.This is because virtual learning environments are currently relatively user-friendly, and effort expectations among technologically savvy professors often need to be higher.As a mediating variable, experience has a mediating effect on the relationship between effort expectancy and behavioural intention to accept virtual learning.Masalu (2021) discovered that gender, age, and experience moderated effort expectations because they all relate to lecturers' behavioural intention to use online learning.Furthermore, Chavoshi (2018) found no association between gender and performance expectations, effort expectancy, or social influence.
The external social surroundings associated with an individual's behaviour are vast.Intention, such as the reflection of peers, teachers, and personal social situations, is included in social influence.Personal motivation may be increased by combining learning with incentives.For example, the most successful promotion is based on rewards and appreciation.Therefore, a significant driving force in virtual learning might be the combination of appropriate incentives and praise.According to the findings of this study, social influence has no positive effect on behavioural intention to accept virtual learning, and voluntariness of use has no mediating effect on the relationship between social influence and behavioural intention to accept virtual learning.However, the relationship between social influence and behavioural intention to accept virtual learning and experience has a mediating effect on the relationship between social influence and behavioural intention to accept virtual learning.These findings are similar to Mulla et al. (2021), who discovered that social impact is the primary cause of digital learning environment acceptability.Lucy (2021) and Nabity-Grover et al. ( 2020) discovered that higher social presence influences teachers' use of ICT or LMS tools.Phrino (2021) found that social impact and perceived ease of use boosted the perceived utility of a web-based learning system.Similarly, Radovan and Kristl (2017) found that social influence is considerably favourably connected to individuals' behaviour intentions on learning management systems, implying that social influence has a beneficial impact on consumer behaviour in adopting elearning systems.Social influence is the greatest impact on lecturers' views of available resources for virtual learning usage on recognising the appropriateness of virtual learning software and technology and the requisite knowledge.Thus, the application of virtual learning begins with a social impact.Specifically, the more social impact supports virtual learning acceptability, the more it is perceived as a valuable teaching tool.
The availability of necessary assistance and resources for the proper application of technology is implied by Facilitating Conditions.According to this study, facilitating conditions positively affect user behaviour to accept virtual learning.These findings are consistent with those of Abbad (2021), who discovered that supportive environments positively influenced using e-learning systems.Similarly, Larry (2017) found that lecturers' perceptions of enabling conditions and their actual behavioural intention to utilise online learning were predictors of lecturers' use behaviour for online learning.These findings, however, contradict the findings of Masalu (2020), who found that conducive settings have no significant direct influence on behavioural intention to utilise e-learning services.Gender, age, and experience were found to moderate the association between enabling factors and user behaviour to accept virtual learning.These findings confirm the findings of Lary (2017), who stated that the moderating influence of gender, age, and experience might be supported in enabling circumstances and online learning user behaviour.Network infrastructures are a vital component of the adoption of virtual learning among HLI instructors and are an essential component of virtual learning.
Findings indicated that behaviour intention positively affects behaviour used to accept virtual learning.This result is congruent with Lary (2017), who discovered that supportive conditions and their real behavioural intention to utilise online learning are predictors of lecturers' online learning behaviour.Matuso (2021) discovered that behavioural intention has a favourable and substantial relationship with actual usage.The degree of commitment of lecturers to the acceptance of virtual learning to attain educational objectives may be used to measure behavioural intention toward virtual learning.Lecturer experiences are important components in the test of behavioural intention and use behaviour because they may be utilised to certify and offer a particular standard to the commitment of HLI's lecturers.

Conclusion and Recommendation
The relevance and need for technology in today's academic world cannot be underestimated.ICT has not only transformed our society's learning and teaching processes, but it has also dictated the future of our knowledge world, thus the desire for major institutions of learning worldwide to join this path to avoid being left behind.This study concludes that performance expectancy, effort expectancy, and social influence positively affect behavioural intention.HLI's lecturers accept virtual learning.This study concludes that facilitating conditions positively affect user behaviour when accepting virtual learning.Also, this study concludes that behaviour intention positively affects the user behaviour to accept virtual learning.HLIs lecturers are encouraged to accept virtual learning by UTAUT theory determinants.In this way, virtual learning enables HLIs lecturers to advance their careers while carrying out their activities swiftly and enhancing learning.Because the current study only included lecturers from two institutions, it did not include lecturers from all Tanzanian higher learning institutions.Future studies interested in virtual learning in HLIs should contact other Tanzanian institutions and institutions from other developing countries to be more representative and generalise the results.Also, the fundamental formulation of the UTAUT theory was used in this work.As a result, it would be interesting to conduct research that compares and contrasts the predictive capacity of many competing theories, such as the theory of planned behaviour, the technology acceptance model, the Theory of Reasoned Action, and the Diffusion of Innovation Theory.

Table 2 . Coefficients of Mediation Factors
HLI's lecturers independently explain only 45.3% of the virtual learning acceptance among PE, EE, and SI.The FC explains only 21.6% of the virtual learning acceptance among HLI's lecturers, while BI explains only 45.3% among HLI's lecturers.Table1shows that the Durbin-Watson ranges from 0 to 4, with 0-2 values indicating positive autocorrelation and 2-4 values indicating negative autocorrelation.This suggests that there is no correlation with serial errors, and the model was thus adequately described.The table displays the measured Fstatistics of 34.208 for (PE, EE, SI > BI); Fstatistics of 12.206 for (FC > BI); F-statistics of