## 5 Discussion
**_Knowledge Acquisition is significant to Organisational Learning_\" was accepted and verified that learning organisations use knowledge to continually catalyse the competency of their in-house cultures in order to grow and succeed, which signifies that 'Organisational Learning' is a pillar for WBET. * **Hypothesis 15 (H15). **_Cl/Kaizen in Knowledge Acquisition is significant to Perceived Usefulness_\" was rejected. This hypothesis was based on the PDCA-like research approach to knowledge management by [PERSON], et al. (2018, 2019). However, as is the case in hypotheses H11, H12, and H13, Kaizen has had no impact except on process training. Furthermore, this variable may require further research that specifically dissects the Kaizen tools with further multiple relations to enable a better understanding of its significance. * **Hypothesis 16 (H16). **_Knowledge Acquisition is significant to Attitude_\" and **Hypothesis 17 (H17). **_Technical Training is significant to Attitude_\" were both accepted. In reference to Table 6, OUM revealed that 'Technical Training' was significant to 'Attitude' (with \(t\) = 1.992
* and \(p\) = 0.046), which is considered the strongest determinant in explaining 57% of variance and 40% of predictive relevancy. Since the inclusion of the 'Attitude' variable was arguable in the literature review, especially by [PERSON] et al. (2018) in terms of resistance to change, the predictive relevancy results lead this research project to be a major identifier of attitude in any organisation adopting OUM. In the case of the sponsoring organisation, the result may be interpreted in the sense that there is no resistance to change since the drivers of 'Attitude' were clearly identified by OUM. These include 'Technology Training' and two more significant variables, namely 'Knowledge Acquisition' to Attitude with \(t\) = 2.848 *
* and \(p\) = 0.004, and 'User Competency' with \(t\) = 5.297 *
* and \(p\) = 0. * **Hypothesis 18 (H18). **_Attitude is significant to Behavioural Intention to Use_\" was accepted. However, \"Hypothesis 19 (H19). **_Attitude is significant to BIM Uptake_\" was rejected. The reason for this manifested in the definition of the 'System Uptake' variable of TAM, which [PERSON] et al. (2018) defined as the positive or negative acceptance of a specific technology based on the relationship of all TAM variables. Therefore, the definition implies analogy to the defined 'Attitude' variable, as a positive or negative feeling about an aspect or event. Therefore, it is almost impossible to relate the two attitudinal variables. * **Hypothesis 20 (H20). **_The Social Factors variable is significant to Perceived Usefulness_\" was rejected. Based on [PERSON] et al. (2018), the introduction of social influence and its significant impact on 'Behavioural Intention to Use', this research realised that the best fit for the 'Social Factors' variable would be 'Organisational Support', as stated in the literature review. However, regardless of either view, the 'Social Factors' variable has no influence over any other variable in the study. Indeed, both the explained variance and predictive relevance for this variable are extremely weak requiring further investigation to clarify its relative impact in future studies. * **Hypothesis 21 (H21). **_User Competency is significant to Social Factors_\" was accepted, and is perhaps one of a few explanatory variables to 'Social Factors', as argued in the literature review. Since 'Social Factors' is representative of in-house cultures ([PERSON], 2018), the hypothesis succeeded in signifying the relationship with less than 5% error variance. However, unless the 'Social Factors' variable is capable of signifying another relationship, such as behavioural intention, it is futile to make additional sense of its relationship to 'User Competency'. * **Hypothesis 22 (H22). **_User Competency is significant to BIM Uptake_\" was accepted.