Accounting Education: Does Institutional Support Moderates Accounting Educators Acceptance Behaviour and Conscientiousness Trait towards Behavioural Intention?

This study examines the interaction effects of institutional support between the educators’ conscientiousness traits and acceptance behaviour towards their behavioural intention to adopt educational technology. Simple random sampling and questionnaire survey methods were used on university educators from several public universities in Malaysia, offering bachelor's degree programmes in accounting. Data were analysed using structural equation modelling to achieve the study’s objectives. This study found that perceived usefulness, attitudes, and conscientiousness are significant, suggesting their important role as predictors of educational technology adoption among accounting educators. Meanwhile, institutional support is able to moderate the acceptance behaviour of accounting educators in the matter of usefulness, ease of use, and attitudes towards the behavioural intention to use educational technology; however, it has no interaction effect on conscientiousness. This study provides valuable insights into understanding the factors influencing accounting educators’ intention to integrate technology in the teaching and learning activities of the 21st century environment. Keyword: Acceptance Behaviour, Accounting Educator, Conscientiousness, Personality Traits, Technology Adoption, 21st Century Teaching And Learning.


Introduction
The advancement of technology has changed educators' fundamental activities in teaching-learning, research, scholarship, and service to society (Rana, 2017). Technology is an excellent medium to enhance classroom teaching and learning activities by encouraging educators to acquire, communicate effectively, plan teaching aids, and assist students' selfexpression and opinions (Khan, Hasan, & Clement, 2012;Mohd Yusof & Tahir, 2017). It also significantly influenced the accounting education in higher institutions globally with the emergence of new skills, new teaching delivery, and competency acquisition (McKinney, Yoos, & Snead, 2017;Pincus, Stout, Sorensen, Stocks, & Lawson, 2017). Technology produces changes in curriculum design and impacts the accountant's nature of work (O'Connell et al., 2015). Thus, employers and industries nowadays are expecting accounting graduates to be equipped not only with a high level of accounting skills but also with a good ICT knowledge (Ogundana, Ibidunni, & Jinadu, 2015) and deep knowledge of machine learning techniques (ICAEW, 2018), as a new way of thinking and acting for future accountant. However, in reality, educational technology adoption is still at a low rate despite its acknowledged benefits (Gaiziuniene & Janiunaite, 2018) to both learners and educators. The reasons are the lack of interest and knowledge, incompetence, and unawareness of the changes (Senik & Broad, 2011;O'Connell et al., 2015) of educational technology availability. Therefore, while considering the innovative educational technology trend, it is important to observe educators' intention to equip themselves with the required technological skills for their professional development (Karahanna & Straub, 1999;Eady & Lockyer, 2013;Ghavifekr et al., 2016). An individual's decision to adopt technology is centred on the personal interest, social influence (Karahanna & Straub, 1999) and attitude, whether they feel such technologies are favourable or unfavourable, which is rooted in their attributes and experience with technology (Quazi & Talukder, 2011;Alshmrany & Wilkinson, 2017). Early studies indicate that human behaviour and learning manners were a positive determinant of the intention and adoption of innovations (Compeau & Higgins, 1995). Therefore, comprehending the perceptual behaviour for the successful adoption of educational technology among educators is obligatory (Buckenmeyer, 2010;Hénard & Roseveare, 2012). Among others, individual acceptance behaviour towards the choice of technology would rely on individual's belief, or influenced by other factors; for example, the organisation's regulation and superiors' instructions requiring the use of technology (Durodolu, 2016;Mirzajani, Mahmud, Fauzi Mohd Ayub, & Wong, 2016), or based on their views whether it is effective or an additional cost and liability when integrated. Although, the personality and background of the individual, or other external factors could also influence educators' intention to adopt technology (Babalola & Tiamiyu, 2012). Therefore, this study attempts to examine accounting educators' conscientiousness trait and acceptance behaviour moderates by the institutional support towards the behavioural intention to adopt educational technology.

Literature Review Behavioural Intention on the Technology Adoption
Previous studies have examined various mechanisms to understand the individual intention to adopt a technology. The studies involved a complex system and multiple variables in understanding this phenomenon (Abu Karsh, 2018). As a result, many researchers attempted to include various models and theories to explain educators' intention to adopt a technology. Prior studies also indicate that educators can either feel motivated or demotivated to incorporate technologies in teaching activities (Mirzajani et al., 2016;Govender & Maistry, 2017;Uerz, Volman, & Kral, 2018). Accordingly, much effort is required to comprehend the factors that could influence individual intentions to adopt technology, especially among educators. Gaiziuniene and Janiunaite (2018) asserted that it is critical to find factors influencing the adoption process to ensure such technology can be optimally integrated into educational institutions. An early study by Karahanna and Straub (1999) revealed personal interest and social influence among the influencing factors of individual intention to adopt the technology. The authors refer to personal interest as the attitude required to achieve a particular behaviour that might be shaped by affective information, past behaviour, and cognitive information; while, social influence refers to social pressure, such as subjective norms. Meanwhile, Roberts, Kelley, and Medlin (2007) suggested some individual factors that may influence the intention to adopt the technology. For example, educators may adopt technology to learn new skills and achieve personal goals; to make the subject more interesting, or perceived it would improve their teaching, while students will be favourably receptive, leading to a reputation for good teaching. In the same vein, Quazi and Talukder (2011) revealed an individual adoption of innovation or technology depends on their attitude, whether they feel those technologies are favourable or unfavourable. These authors' findings are encouraging, as they confirmed that the usage behaviour of those adopting technology is profoundly rooted in their attributes and experience with technology. Accordingly, many studies reveal several variables that brought up various theories and models created to describe the mechanism behind educators' intention to adopt technology in teaching and learning activities (Scherer, Siddiq, & Tondeur, 2019). For instance, literature examining users' acceptance and adoption of technology (Venkatesh & Davis, 2000;Lee, Kozar, & Larsen, 2003;Fathema, Shannon, & Ross, 2015;Durodolu, 2016;Weerasinghe & Hindagolla, 2017) confirmed that the behavioural intention to adopt technology can be influenced by the voluntary use perspective or even in non-voluntary settings.
As revealed by the literature, educators' characteristics, such as behaviour, attitudes, and beliefs, are important elements to comprehend educators' intention to adopt a technology. However, many other attributes should be explored to identify the patterns and characteristics of educators who embraced technology in their teaching and learning activities (Dougherty, 2015). Thus, for this study, the primary focus is to understand educators' intention to adopt educational technology by considering their personality traits and acceptance behaviour as the main attributes that might predict their behavioural intention to adopt a technology. Göncz (2017) stated that studies related to educators' adoption of educational technology could be highlighted in the psychology-related field to analyse educators' typologies, desirable and undesirable features, professional behaviours, professional identity, and personality.

Acceptance Behaviour Constructs of PU, PEU, and AU
The constructs of perceived usefulness (PU), perceived ease of use (PEU), and attitude towards use (AU) are derived from the technology acceptance model (TAM), which was developed by Davis, Bagozzi, and Warshaw (1989). It was well-received by researchers to relate the technology acceptance and determine the reason for the action, whether to accept or disapprove of the information technology (Park, 2009). It can also predict the likelihood of a new technology being adopted by an organisation, a group, or an individual (Breedt, 2015). According to Ajzen and Fishbein (1980), TAM is considered an influential extension of the theories of reasoned action (TRA). Some studies also suggest a mandatory use of TAM, and it is one of the factors for compliance impact in individual behaviour and technology-related studies (Schepers & Wetzels, 2007). The main reason for the prevalent use of TAM is because of its parsimonious structure that is considered able to provide ample explanation and prediction across diverse technologies, samples, different settings, expertise level, and theoretical framework (Abbasi, 2011), as well as its strong applicability, generalisability, and practical implications (Scherer et al., 2019).
In addition, users' acceptance of technology is a critical factor for the success or failure of the technology assimilation (Mirzajani et al., 2016;Ayele & Birhanie, 2018). Integrating technology with education is a complex process, and understanding technology acceptance and usage behaviour among educators is challenging for educational institutions (John, 2015;Khlaif, 2018). In this conception, individuals' actual use of a technology system is influenced, either directly or indirectly, by determinants such as behavioural intentions, attitude, perceived usefulness, and perceived ease of use of the technology (Kim, Chun, & Song, 2009;Durodolu, 2016). Consequently, TAM highlighted the variable of perceived usefulness (PU) and perceived ease of use (PEU) as the two main determinants to predict individuals' behaviour towards using technology (Godoe, 2012). In this study, the concept of perceived usefulness (PU) is defined as the individuals' tendency towards believing that using a particular system could improve their job performance (Lai, 2017). In the context of educational technology, PU is related to the level of reliability, effectiveness, and costeffectiveness resulting from the use of any innovation or technologies in teaching-learning activities (Tanduklangi, 2017). Individual perception of usefulness will also base on the capacity of a particular technology or system that is believed to have a positive use than others (Durodolu, 2016). Meanwhile, perceived ease of use (PEU) is when a person believes that using a particular system could be free of effort (Lai, 2017;Momani & Jamous, 2017). In the educational technology context, an individual will form initial perception regarding the perceived easiness of using a particular technology based on the general beliefs that a particular technology is easy and effortless, helpful, reduce uncertainty, and flexible to be used (Hussain, Mkpojiogu, & Yusof, 2016;Tanduklangi, 2017). In this context, there are numerous findings in a wide range of technology acceptance literature by assessing the myriads of technologies.
On the other hand, attitude (AU) is described as individuals' perspective or evaluation about an object together with a set of beliefs about that object or behaviour (Fishbein & Ajzen, 1975). Nevertheless, the role of attitude variable in TAM was found to have a weak role as the mediator between the construct and technology adoption or intention (Lai, 2017). Therefore, several researchers (e.g., Baker, Al-Gahtani, & Hubona, 2007;Lee, Cerreto, & Lee, 2010;Altawallbeh, Thiam, Alshourah, & Fong, 2015) repositioned this variable to have a direct relationship found that attitude can be a positive determinant that will influence individuals to adopt a technology. In describing educators' attitudes toward technology, several predictors, such as confidence (Abedalaziz, Jamaluddin, & Leng, 2013) and perception (Nikian, Nor, & Aziz, 2013) as factors leading to a positive attitude towards accommodating technologies in the teaching-learning process or other associated activities. Within the context of the present study, it can be said that if the accounting educators have positive attitudes towards the use of educational technology, they would have the tendency and intention to adopt technology into their teaching and learning activities, and vice versa. Therefore, for this study, the acceptance model and its underlying construct become the major focus to achieve the study's objective in determining factors for accounting educators' acceptance and examining the moderation role of institutional support towards the behavioural intention to adopt educational technology.

Conscientiousness Trait Construct
The Big Five personality traits model is one of the most prominent models used in contemporary studies to grasp personality's most prominent features (Zaidi, Abdul Wajid, Zaidi, Zaidi, & Zaidi, 2013). Soto and Jackson (2013) explained that prior studies leading to the development of the five-factor personality model originated from two main sources: lexical research and the many research assessing traits using multiple personality inventories. In particular, the Big Five personality traits can be found in many studies on personality and proved to be highly stable over time (Cobb-Clark & Schurer, 2012;Zaidi et al., 2013). Besides, numerous studies assess personality traits to comprehend the behavioural intention over technology adoption as there is a rising interest in personality as an explanatory tool in information system literature (Farhad Khan, Iahad, & Miskon, 2014). Soto (2018) suggested that each of the Big Five personality traits can predict several important life outcomes, which are influenced by biological (for instance, differences in personality change that is somewhat inbred) and environmental factors (for example, situations affecting individuals based on numerous life experiences). In this regard, understanding the educators' personality traits could provide an overview of how they accommodate the intention to adopt educational technology.
Nonetheless, for this research context, only conscientiousness is chosen out of the five personality traits. The rationale for this is its extensive use in past literature, which proved its association and influence on individuals' personalities, including those working in the accounting profession (Wells, 2018). Hence, implying that studying the accounting educators' personality traits, particularly conscientiousness and intention to adopt technology, would lead to a better understanding that may ensure optimal technology integration into the education curriculum. Earlier studies have identified conscientiousness's positive influence on educational performance and work performance in education and learning contexts (Pornsakulvanich et al., 2015) for the western perspectives. Furthermore, Barnett, Pearson, Pearson, and Kellermanns (2015) claimed some studies mentioned conscientiousness has interesting implications and suggests it can be considered a direct predictor and serves as an indirect predictor when studying behaviours through intentions. The notion is consistent with Svendsen, Johnsen, Almås-Sørensen, and Vittersø (2013), who found conscientiousness is related to behavioural intention and adoption to use hypothetical software technology. Therefore, it is expected that conscientiousness individuals will have an intention to use technology because these individuals also demonstrate traits, such as dependable, accountable, careful, thorough, orderly, playful, flexible and time-saving. On the other hand, this construct will also be used to extend the indirect effect analysis concurrently with the moderation effect of institutional supports towards behavioural intention.

Moderating Effect of Institutional Support
This study employs institutional support as the moderating variable. The variable selection is based on the organisational support theory (OST), which is materialised from the exchange theories that postulated organisation support is the catalyst for a positive employee outcome that may be reflected through their attitude, behaviour, and performance (Eisenberger, Huntington, Hutchison, & Sowa, 1986;Rhoades & Eisenberger, 2002;Eisenberger & Stinglhamber, 2011). Initially, it was grounded on the social exchange theory of Gouldner (1960) and Blau (1964), which explains that when a person does a favour to someone, he or she expects something in return for their favour (Rhoades & Eisenberger, 2002). This suggests that if the organisation satisfy the employee needs, value their contributions, and pay attention to their interests; then, employees will experience job satisfaction, which in turn, will benefit the organisation and increase perceived organisational support (Ahmed, Nawaz, Ali, & Islam, 2015;Akgunduz, Alkan, & Gök, 2018). Additionally, a growing number of studies have used organisational support as a moderator to study its effect with myriads of variables. For example, Jain, Giga, and Cooper (2013) identified the effect of organisational support as a moderator between stressors and citizenship behaviour, which confirmed the moderating role of organisational support in the relationship. Meanwhile, Palmer, Komarraju, Carter, and Karau (2017) studied the relationship of dark triad personality traits (namely, narcissism, Machiavellianism, and psychopathy) with counterproductive behaviour (namely, theft, production deviance, withdrawal, and abuse), moderated by organisational support. Both studies suggest organisational support is a strong moderating variable. Another study by Cheng, Chen, Teng, and Yen (2016) provide evidence that organisational support shows a significant moderating role in a relationship between job crafting and job outcome. Typically, organisational support includes governance and policy; training and mentoring; infrastructure; managerial support; incentives and financial capability (Gilakjani, Leong, & Ismail, 2013;Prasad, Lalitha, & Srikar, 2015).
Therefore, within the context of the present study, if the accounting educators perceive that their organisation or institution treated them sincerely and fairly and looking after their welfare, they are likely to develop a high intention of educational technology adoption. On the other hand, an investigation by Ayele and Birhanie (2018) discovered that incentive factors (such as promotion, financial benefits, workload, and time reduction) are not motivating factors for educators' acceptance and use of technology. Moreover, Gordon, Gratz, Kung, Moore, and Urbizagastegui (2018) postulated that lack of organisational support for faculty development and training, the absence of robust technology and applications, and lack of support for students enrolling in online courses are among the reasons for faculty members' reluctance in adopting technology in the classroom. According to these authors, although educators have the necessary skills and motivation to apply technologies in class, without organisational support, such effort will cease without initiation. Due to the various conceptions from previous outcomes and studies dominantly based on western perspectives, the context and results might differ if similar research is extended to developing countries with distinct cultures, individual personalities, and acceptance behavior. Therefore, to materialise this study, the research framework is illustrated as below:

Figure 1: Research Framework
Based on the discussed literature review and proposed research model, the following hypotheses are formulated while considering the study's main objective; that is to examine the direct influence between constructs and indirect effect of INS as the moderating variable for the accounting educators' acceptance behaviour and conscientiousness trait towards their intention to adopt. There is a positive influence of perceived usefulness (ACPU) on the intention to adopt educational technology by the accounting educator H2 There is a positive influence of perceived ease of use (ACPEU) on the intention to adopt educational technology by the accounting educator H3 There is a positive influence of attitude towards use (ACAU) on the intention to adopt educational technology by the accounting educator H4 There is a positive influence of conscientiousness trait (PTCO) on the intention to adopt educational technology by the accounting educator Indirect Effect Hypotheses H5 The positive relationship between ACPU and BITA will be stronger for higher INS H6 The positive relationship between ACPEU and BITA will be stronger for higher INS H7 The positive relationship between ACAU and BITA will be stronger for higher INS H8 The positive relationship between PTCO and BITA will be stronger for higher INS Note: BITA -Behavioural Intention to Adopt; ACPU -Acceptance Behaviour of Perceived Usefulness; ACPEU -Acceptance Behaviour of Perceived Ease of Use; ACAU -Acceptance Behaviour of Attitude towards use; PTCO -Personality Trait of Conscientiousness; INS -Institutional Support.

Material and Methods
The simple random sampling and questionnaire survey methods were administered on 275 university educators from twelve universities. A total of 195 respondents completed the questionnaires that were received within five months of distribution. The targeted sample was accounting educators teaching accounting subjects in universities in Malaysia, offering at least a bachelor's degree programme majoring in an accounting discipline. The reasons for nominating this population are that public universities in Malaysia are among the high ranked in the QS World University Ranking. The number of accounting graduates produced is prominent compared to the private university (Abd Jalil, 2018). Thus, reflecting the major supplies for the various industry professionals. The survey items are segregated into 15 adapted items in Section A (BITA1 -BITA15), assessing the behavioural intention to adopt educational technology. Next, the 21 adapted items in Section B measure the acceptance behaviour of perceived usefulness (ACPU1-ACPU7), perceived ease of use (ACPEU1-ACPEU7), attitude towards use (ACAU1-ACAU7). In Section C, it consists of 6 conscientiousness statements, PTCO1 -PTCO 6. Meanwhile, for institutional support related to individual benefits, five items were denoted, from INS1 -INS9. The items were assessed using a five-point Likert Scale, ranging from 1 = "Strongly Disagree" to 5 = "Strongly Agree".
All measurements have been refined to fit the current study's context. The study's sources were extracted from previous literature of reputable indexed publications (Barnett et al., 2015;Porter & Graham, 2016;Gholami, Abdekhoda, & Gavgani, 2018). A reflective type of questions was embedded in the exogenous and endogenous constructs. Outputs from this study were analysed using the SmartPLS v3.3.2 software, following the current requirement and rules of thumb for outer and inner measurement models. A thorough examination using structural equation modelling was employed to achieve the study's objectives. Instrument measurements details are presented in Table 2, which has been examined with the indicator loadings. Furthermore, this study also aims to reveal the significant effects of the testing moderation effects. Therefore a two-stage approach is an appropriate approach to examine the interaction effects (Hair, Hollingsworth, Randolph, & Chong, 2017). Ramayah, Cheah, Chuah, Ting, and Memon (2018) proposed a two-stage approach considered the best method to see the significant moderating effects and achieve the most accurate estimates with high statistical power.

Profile of Respondents
The demographic analysis result shows female respondents were the dominant gender, with 146 (74.9%) compared to the male with 49 (25.1%) respondents. The majority of the respondents were between 40 and 49 years old (53.3%), followed by those between 30 and 39 years old (30.2%), while 16.4% of the respondents were under 50 years old and above. About 66.2% of the respondents possessed Philosophy Doctorate, whereas 32.8% have a Master's Degree, and 1% have a professional qualification. In terms of current academic positions, more than half of the respondents (59.5%) are senior lecturers, followed by 22.6% associate professors and 13.8% lecturers. Professor and assistant professor shared the same percentage (2.1%). In this study, about 52.3% of the respondents have frequently used educational technology, while the rest mentioned they rarely used it.

Assessment of Reflective Measurement Internal Consistency and Convergent Validity
This study applies a two-stage modelling technique following the steps recommended by Ringle, Sarstedt, Mitchell, and Gudergan (2018), the current updated SEM-PLS in information system research by Benitez, Henseler, Castillo, and Schuberth (2020), to develop and examine the reflective measurement model for reliability and validity of the items and constructs, and subsequently to engage with the structural model (Hair, Hult, Ringle, & Sarstedt, 2017). Several assessments have been performed following the rules of thumb, namely internal consistency reliability, convergent validity, and discriminant validity to evaluate the model's results (Henseler, Ringle, & Sinkovics, 2009;Chin, 2010;Roldán & Sánchez-Franc, 2012;Hair, Hult, et al., 2017). The results depicted in Table 2 also include the factor loading estimates of this study. The ranges are from 0.559 to 0.906 and significant at a 1% level, suggesting the measures' reliability. For this study, all possible outer and inner paths were drawn, and outputs from the reflective measurement analysis were presented in diagrams and tabulated accordingly. 0.758 *** 0.261 ** Note 1. BITA -Behavioural Intention to Adopt; ACPU -Acceptance Behavior of Perceived Usefulness; ACPEU -Acceptance Behavior of Perceived Ease of Use; ACAU -Acceptance Behavior of Attitude towards use; PTCO -Personality Trait of Conscientiousness; INS -Institutional Support Note 2. Loading indicators are significance when * p < 0.05, ** p < 0.01, *** p < 0.001 and not significance when † p < 0.10, (one-tailed test).
In order to achieve the uni-dimensionality of the constructs and ensure all indicators have equal factor scores loaded, the indicator loadings must be above 0.708, indicating that 50% or more of the variance in the observed variables were explained (Hair, Hult, et al., 2017). However, for the threshold loadings' value above 0.4, 0.5, 0.6, or 0.7, the indicators will be retained (Wülferth, 2013); if the loadings are below 0.4, then the reflective indicator must be removed from the model (Hulland, 1999;Avkiran & Ringle, 2018). Based on Table 2, it appears that the majority of the indicator loadings are above 0.5 since the AVE achieved the required minimum threshold of 0.50. Four indicators (ITA1, ITA3, ITA4, and ITA5) were removed one at a time from the lowest loadings, which contributed to the endogenous construct's AVE value of below 0.50. Besides, there are three moderating variable items (INS1, INS4, and INS5) that have been removed due to their low factor loadings value, which is below 0.5. The removal of the items from the model involved only 13.73% of the whole measurement; thus, it can be assumed that it is a credible instrument design (Hair, Black, Babin, & Anderson, 2010;Hair, Hult, et al., 2017), especially when the testing is conducted in the Asian region and not from the western perspectives. On the other hand, Ramayah et al. (2018) stated that removing items from the model that were less than 20% is acceptable and confirmatory factor analysis (CFA) can be performed, especially when adapting the instrument for the context of the study.
Comparatively, all constructs of the model are considered satisfactory and strongly reliable, whereby both reliability scores assessment criterion using Dillon-Goldstein's ρ and the strict assessment of PLS consistent Algorithm of Dijkstra-Henseler's ρA, were above 0.70 (Nunnally & Bernstein, 1994;Dijkstra & Henseler, 2015;Hair, Black, Babin, & Anderson, 2018;Benitez et al., 2020). None of the variable scores from the three assessments' criterion exceeded the problematic values of 0.95, which suggests redundancy.

Discriminant Validity
The recent discriminant assessment is extended by using the heterotrait-monotrait (HTMT) ratio, as proposed by recent research (Henseler, Ringle, & Sarstedt, 2015;Voorhees, Brady, Calantone, & Ramirez, 2016), particularly in information system research (Benitez et al., 2020). Table 3 illustrates the discriminant validity results of HTMT, which indicate a satisfactory level for all constructs. The HTMT values present a lower value than 0.90 for the lenient threshold and the recommended strict threshold of less than 0.85 (Voorhees et al., 2016;Franke & Sarstedt, 2019). Furthermore, the two-sided of 5% and 95% percentile confidence interval (lower and upper CI) of HTMT does not include the value of 1, indicating that the latent variables are significantly different from 1 on any of the constructs  hence, confirming the discriminant validity.  Table 4 shows the collinearity diagnostics using the VIF values results from all  indicators of INS2, INS3, INS6, INS7, INS8, and INS9, as well as all the 11 accepted items for the endogenous variable in the modified structural model and the relative contribution of the measurements. From the above diagnostics output, it can be concluded that there is no problem with the outer VIF values results that are more than 3.3 or 5, hence indicating no collinearity issues. Moreover, all indicators have met the t-value of >1.96, indicating that the relative contributions are established on institutional support and behavioural intention to adopt.

Evaluation of path coefficients, significance levels, and their effect sizes
Several standard assessment criteria have been applied to assess the structural model, including the coefficient of determination (R2), the blindfolding-based cross-validated redundancy, measuring the Q2, and testing the statistical and relevance of the path coefficients (Hair, Risher, Sarstedt, & Ringle, 2019). Table 5 explains the direct effect and the moderation effect's result of the exogenous and endogenous constructs. Three hypotheses were supported (H1, H3, and H4), whereby the p-value < .001 positively influenced the main effect of endogenous constructs. The coefficients of ACPU (β1 = 0.376, t = 4.021), ACAU (β2 = 0.258, t = 2.555), and PTCO (β3 = 0.178, t = 2.967) show a significant and strong positive influence of ITA, except for the effect of ACPEU. Additionally, using the recommended confidence intervals to measure the results' precision, the percentile bootstrap confidence interval for the path coefficient estimate is considered statistically different from zero at a 5% significance level when its p-value is below 0.05 or when the 95% bootstrap percentile confidence interval constructed around the estimate does not include zero. Figure 3 illustrates the R2 result of 0.483 (48.3%), which is considered substantial (Cohen, 1988) and indicates a strong magnitude of the variance in the intention to adopt, explained, and predicted by the exogenous constructs. The value above the minimum threshold is widely embraced by many recent works (Salloum, Mohammad Alhamad, Al-Emran, Abdel Monem, & Shaalan, 2019; Herrador-Alcaide, Hernández-Solís, & Hontoria, 2020) on the adoption of innovation and information system field. Furthermore, the PLS model of the tested paths demonstrates evidence of predictive relevance, with Q2 of 0.238 indicating the model's index of reconstruction goodness by model and parameter estimations (Andreev et al., 2009), which measures the extent of the model's prediction success (Urbach & Ahlemann, 2010).  Table 5 also explains the results' indirect effects between the exogenous and endogenous constructs. Hypothesis 6 was supported at p-value<.01, while the other two hypotheses (H5 and H7) were supported with p-value<.05. All the supported hypotheses are significantly influenced by the moderation effect between the exogenous and endogenous constructs. The coefficients of ACPU*INS (β = 0.124, t = 1.946), ACPEU*INS (β = 0.172, t = 2.662), and ACAU*INS (β = 0.116, t = 1.933) show a significant and strong positive influence of BITA, except the moderation effect of PTCO. Additionally, using the recommended confidence intervals to measure the results' precision, the percentile bootstrap confidence interval for the path coefficient estimate is considered statistically different from zero at a 5% significance level when its p-value is below 0.05 or when the 95% bootstrap percentile confidence interval constructed around the estimate does not include zero.
Overall, the effect sizes of the significant hypotheses suggested by Cohen (1998) and Kenny (2018) ranged from small to medium, and few outputs indicate medium to large. The findings are consistent with many studies in the education field, which mentioned that the small effects interpreted by Cohen's standard are often large and meaningful (Kraft, 2020), and difficult to achieve large effect sizes (Bakker et al., 2019). Meanwhile, a minimum of 0.02 is recommended for practical significance (Franzblau, 1958;Lipsey, 1998), specifically in the education context.

Interaction plot
The significant interaction effect of INS with the supported three hypotheses was further drawn using an interaction plot, as shown in Figure 3 until Figure 5. This interaction effect's size and nature are not easy to describe using the output merely from its coefficients. For this study, the two lines from the graph represent "Low Support" and "High Support" and plotting using the β values of the related constructs.
All the figures show the plotting of the β values for perceived usefulness (β = 0.581), institutional support (β = 0.170) and the interaction effect (β = 0.124) for ACPU, the plotting of the β values for perceived ease of use (β = 0.486), institutional support (β = 0.156), and the interaction effect (β = 0.172) for ACPEU and the plotting of the β values for attitude towards use (β = 0.593), institutional support (β = 0.121), and the interaction effect (β = 0.116) for ACAU. All interaction plots are intercepted at 3. The two lines represent high INS with a steeper gradient compared to low INS, indicating that the positive relationship and significance interaction are stronger when the INS is high. Specifically, the result implies that the high ACPU, ACPEU, and ACAU of educational technology will encourage accounting educators to have a behavioural intention to adopt with high institutional support compared to the low institutional support condition. Therefore, the INS difference between "high support" and "low support" situations provides a profound interaction effect between ACPU, ACPEU, ACAU, and INS, suggesting that the interaction is capable of moderating significantly between ACPU, ACPEU, ACAU, INS, and BITA.

Discussion on Findings Direct Effects on ACPU, ACPEU, ACAU, and PTCO
The findings of the main effects indicate that perceived usefulness (ACPU), attitudes towards use (ACAU), and conscientiousness (PTCO) are significant; thus, they can be predictors of the intention to adopt educational technology. Meanwhile, the perceived ease of use (ACPEU) is found to be insignificant. In line with many other studies, ACPU is a strong predictor that influences individuals' intention to adopt educational technology (Kanwal & Rehman, 2017;Al-Maroof & Al-Emran, 2018). Researchers posited that when educators realise the high value of educational technology, they will eventually transform their teaching and learning activities by adopting technology (Akinde & Adetimirin, 2017; McKenney & Visscher, 2019). Next, attitudes (ACAU) were also found to be a strong determinant that influences a person's intention to adopt a technology. Attitudes can be described as individuals' positive or negative feelings about performing a particular behaviour (Fishbein & Ajzen, 1975). Attitudes would guide an individual's experience and decide the effect of such experience through the behaviour (Bohner & Dickel, 2011). In this study, educators with positive attitudes towards using technology in teaching and learning will potentially be leaning towards implementing or embedding technology in their instructional process (Elkaseh, Wong, & Fung, 2016). This has been highlighted in many studies that found a positive attitude towards technology use will result in more efficient use of technology in the teaching and learning process by educators (Guillén-Gámez & Mayorga-Fernández, 2020). Meanwhile, the conscientiousness (PTCO) construct described as individuals' inclination towards self-discipline, meticulousness, and accomplishment (Zaidi et al., 2013) is also an important predictor of the intention to adopt a technology. Accounting educators tend to have similar characters as professional accountants, such as their sensing, thinking, and judging (Bealing, Baker, & Russo, 2006), attention to detail, creativity, flexibility, and excellent organisation (Myler, 2015). Thus, there is a high probability that educators with high conscientiousness would integrate technology into their instructional activities.

Indirect Effects on ACPU, ACPEU, and ACAU
The findings demonstrate that all three hypotheses measuring the acceptance behavior are supported with a p-value < 0.01 for H6 and a p-value < 0.05 for H5 and H7. This means institutional support significantly moderates the relationship between the constructs of acceptance behavior and behavioural intention to adopt educational technology.
First, the findings of H5 as presented in the interaction plot suggest that different levels of perceived institutional support denoted as High INS and Low INS interact with the perceived usefulness towards BITA (Figure 3). The effect of perceived usefulness (ACPU) of educational technology differs significantly between those accounting educators who perceive High INS and Low INS, which means high support by institutions will intensify their perception of technology usefulness, hence, improving their intention to adopt educational technology. In line with the OST, the presence of support will reinforce individual intention to pursue the institution's direction. In this aspect, even simple support, such as reasoning, motivating, and promoting educational technology, would enhance accounting educators' opinions of the institution's effort to improve teaching and learning productivity. Furthermore, sufficient support will positively enhance educators' belief towards technology when they recognise the benefits and advantage of using technology in teaching and learning, as endorsed by the institution (Gaiziuniene & Janiunaite, 2018;Lawrence & Tar, 2018). Consequently, this could boost accounting educators' beliefs on the usefulness of educational technology when it improves their teaching practice, increases performance and productivity, improves the effectiveness and quality of work, and supports learning outcomes. Conversely, accounting educators who perceived educational technology is less useful will show less intention to adopt, despite high institutional support. As displayed in the interaction effect graph, there is no significant difference in the gradient slope when there is Low INS or High INS if the educators themselves perceived such technology is less useful. Therefore, institutional support does not significantly influence their intention to adopt if they perceived a particular technology as not practical. When such technology is deemed not supportive of the course learning plan, syllabus content, and opportunities to reflect students' understanding, educators will assume such technology is not useful (Kearney, Schuck, Aubusson, & Burke, 2017), hence, making them have less intention to adopt. As a result, educators will be more likely to persist using traditional methods than technology (Johnson, Jacovina, Russell, & Soto, 2016). Therefore, educators' understanding of technology is crucial as it will affect their expectations and perceptions of technology. Institutional support can take various forms to echoes the importance and usefulness of educational technology among accounting educators. For instance, superiors' encouragement, an extensive selection of user-friendly applications or software, and various educational programmes might change educators' perception towards using technology (Naujokaitiene, Tereseviciene, & Zydziunaite, 2015).
Second, the result of hypothesis H6 shows that accounting educators with a high level of perceived ease of use (ACPEU) of technology will have more intention to adopt educational technology when INS is present. This finding corroborates the OST, which specifies that perceived institutional support positively impacts employee engagement and commitment. This is confirmed by the interaction effects (Figure 4), indicating that High INS could strengthen the relationship between educators' ACPEU and BITA. In fact, with the p-value < 0.01, the interaction effect for perceived ease of use is stronger than perceived usefulness and attitudes towards intention to adopt educational technology. However, accounting educators who perceived technology as difficult to use will have less intention to adopt educational technology, whether organisational support is high or low. The perceived ease of use is based on the premise that individuals will intend to use technology if it is hassle-free, effortless or the potential users will not be facing complexity and trouble when engaging with a particular technology (Davis et al., 1989;Lai, 2017). In short, accounting educators are willing to learn and incline to use educational technology if they perceive it is relatively easy to use. Thus, users' experience when engaging with technologies will consider the ease of use, navigational clarity, and interactive design (Khlaif, 2018;Scherer et al., 2019), learnability, interaction, response time, efficiencies, andmemorability (Alshehri, Rutter, &Smith, 2019), which are among factors influencing educators' perceptions about technology ease of use. Institutional support, such as technical support, technical infrastructure, training, and instructional sustenance (Khlaif, 2018), could serve as factors that assist educators in understanding technology use for instructional purposes. Based on OST's scope, institutional support, such as motivation, knowledge empowerment, and sufficient resources, could help educators anticipate the user-friendliness and possible benefits of using technology. Thus, their intention to use it will be much higher even before any attempt to adopt educational innovation (Gaiziuniene & Janiunaite, 2018). The supportive environment can ease accounting educators' process of integration and understanding educational technology. This explains the result of moderation effects for this study. It indicates that with high institutional support, it can boost up the accounting educators' perceived ease of use; hence, increasing their intention to adopt.
Third, hypothesis H7 also shows a similar pattern of indirect effect as those mentioned above, indicating that institutional support significantly moderates the relationship between accounting educators' attitude (ACAU) and BITA. This is in line with the OST, implying that perceived institutional support would improve individuals' attitudes to willingly devote to the organisation and improve their involvement in promoting work performance. As rendered in the interaction effect plot (Figure 5), accounting educators' attitudes will change, and their intention to adopt will be higher if there is a High INS. On the other hand, those who have a low degree of positive attitude are likely not affected by a High or Low INS. A positive attitude towards technology use is crucial if educators are to integrate them in the teaching and learning process, and this may be influenced by various reasons, such as anxiety, confidence, or comfort age, gender, and experiences in using technology (Breedt, 2015;Shropshire, Warkentin, & Sharma, 2015). Positive attitudes could also develop educators' high interest and involvement that eventually enhance technology integration intention (Elkaseh et al., 2016;Bajabaa, 2017). Conversely, those with low or negative attitudes will likely inhibit this process and project unnecessary concern and anxiety in educators (Breedt, 2015), as they might anticipate a substantial additional work resulting from technology integration. Nevertheless, perceived institutional support has a crucial effect on educators' attitudes towards their job performance and commitment. Specifically, organisations' support will function as a motivation that affects educators' attitudes, including their belief, affection, and behavioural intention towards work (Nartey, Annan, & Nunoo, 2018). Such supports, for instance, training and incentives, including promotion, financial benefits, and reduced workload, are among the crucial determinants to encourage educators to use technology in their instructional activities (Ayele & Birhanie, 2018). Accounting educators may view these supports as easing, and this could be a driving force to develop more positive attitudes that increase their intention to use educational technology. Notably, a proper structure of institutional support could become a remedy for influencing accounting educators to adjust their attitudes, becoming more positive towards adopting educational technology. This is manifested in the interaction results that explain institutional support empowers the attitude towards intention to adopt.

Indirect Effect on Conscientiousness Trait (PTCO)
Next, the findings for hypothesis H8 suggest that INS do not influence accounting educators' conscientiousness (PTCO) towards BITA as the results were statistically insignificant. The attributes constitute these traits: persistent, ambitious, and well planner (Barnett et al., 2015), among others; they describe educators' work nature to plan their education effort. However, as mirrored by the findings, it might suggest that their persistence is not consistent. For instance, this could occur when conscientiousness and self-disciplined educators practise self-control and order in achieving a set of goals while adhering to their principles (Markovikj & Serafimovska, 2018). The notion suggests that accounting educators can control themselves through the process of planning, organising, and executing their duties or obligations. Nonetheless, individual with self-disciplined does not prefer controlled, or restrictions and they may dislike authoritarian disposition in their working environment (Kirkland, 2017;Cheng & O-Yang, 2018). Consequently, institutional support will not make self-disciplined accounting educators more disciplined; instead, they tend to feel vulnerable when the organisation puts too much control by imposing strict rules or regulations and rigid policies. Even energetic individuals who are active and lively at work would be affected by the high bureaucratic and rigid environment, rendering them no autonomy and flexibility in performing their tasks (Wang, Zhang, Thomas, Yu, & Spitzmueller, 2017). This condition might be presented by accounting educators, especially when they feel irritated with the controlled environment; they might feel discouraged to perform specified tasks such as integrating technology into their teaching and learning activities. Accordingly, educators may perceive unfairness from the organisation resulted from a diminished trust that would make them less likely to commit to the organisation and their job.

Conclusions
This study examines the direct influence and indirect effects of institutional support between accounting educators' personality traits and acceptance behaviour competencies on their behavioural intention to adopt the 21 st century educational technologies. Institutional support is selected for this study based on the view that individuals' beliefs can be influenced by how much organisations value their contribution and care about their well-being (Kirkland, 2017). Eventually, this would inspire individuals to enhance their skills, knowledge, and capability, resulting in increased productivity and performance. The findings of direct effects indicate that perceived usefulness, attitudes, and conscientiousness are significant. Thus, they could be the important predictors for the intention to adopt educational technology from the individual perspective, which differs from the typical studies mostly focused on the performance, students as the sample, and the organisations' views. Meanwhile, the indirect effect results show that institutional support significantly moderates perceived usefulness, perceived ease of use, and attitudes.
This study could contribute important knowledge and understanding of the factors that could influence accounting educator's intention to adopt educational technology. Theoretically, this study highlighted that all the variables in the technology acceptance model (TAM) are proven as important determinants that influence accounting educators to adopt technology in present technological environment. More importantly, the variable of attitude which was originally functioned as mediating variable is proven become important factors after it was repositioned as direct construct in this study. In addition, the integration of TAM, Big Five personality traits theory (i.e., conscientiousness trait) and the role of organisational support theory as the moderating variable would add the richness and insightful guidance for the future research. It is anticipated that these novel findings add new knowledge in the accounting education field by understanding the reasons influencing accounting educators' behavioural intentions to adopt educational technologies. Moreover, the main aspect that education institutions must not overlook is providing support for their educators by formulating learning goals and ensuring educators understand and implement technology in the instructional process. Besides, as revealed in the moderation effect findings, this is consistent with the OST, which advocates that with the right approach and suitable tools of institutional support, technology integration in education can be communicated effectively to accounting educators and eventually improve their acceptance and personality towards the behavioural intention to adopt. Other than that, the extension in literature to understand the context of the study become one of the theoretical implication for this study. A number of recent developments indicate that the factors influencing educational technology research become the most important issues faced by many organisations including university. This study managed to represent a contribution to the examination of the model and theories using primary data across the public university with a non-student sample. This is due to the study context that required the examination using academics since it is associated with the environmental and work-related activities and using a student sample would be limited due to their nature of not having or low technological experience and awareness. Therefore, focusing the academicians as the sample in this study would assist the university to find a comprehensive solution on the adoption of educational technology issues as they are the frontline and responsible in producing the future accountants as demanded by the industry particularly in the era of digitalization or industrial revolution 4.0.
As a conclusion, the educators could successfully assimilate the development of technology when the organisation facilitates the educator to explore their potential to transform quality instructions and identify specific information segments to solve technology integration barriers, such as lack of training, insufficient resources, unclear policy and mandate, future direction, and career progression. Besides, individuals' attributes measured in this study are also significant. They should be considered seriously by organisations since the transference of technological skills and knowledge depends on educators' perceptions of the technology applied in teaching and learning practices. Despite the fruitful findings, this study is not without limitations. First, this research is subject to social desirable responses. This might affect respondents' pure willingness to respond to personality trait questions if they assumed it as a sensitive measurement, resulting in response bias. Second, the sample population is selected from public universities. Future studies might consider private institutions to add to the richness of the results and also can be used for comparison. It is also recommended that future studies should also include other traits or other types of information system and education theories and models of personality. These efforts will lead to a better understanding of accounting educators' individualities and characteristics that could influence their intention to adopt educational technology in their teaching and learning activities. Considering the individual factors from the perspective of academician as the influencing driver towards technology adoption will construct more contributions in this area rather than typical investigation on the performance usage and student's acceptance.