# Determinants of Emotion Recognition System Adoption: Empirical Evidence from Malaysia
## 5 Discussions  
In the previous section, the study's findings were discussed. Table 12 revealed that five hypotheses were supported in this study, with four of them stemming from direct relationships and one being a supported moderation hypothesis. The first research objective of this study was to gauge the sentiments of Malaysian youth towards ERS. These sentiments serve as an indicator of whether Malaysian youth hold a positive or negative attitude towards ERS, a technological advancement. A positive attitude would suggest that ERS implementation might be relatively straightforward for ERS engineers and scientists, whereas a negative attitude indicates that it would require more cautious consideration. Analyzing the mean scores presented in Table 6, it is apparent that the mean score for AT is 5.319, indicating that the majority of respondents hold a positive attitude towards ERS and agree that ERS can be a beneficial technological advancement. This should serve as encouragement for innovators and relevant technology actors to engage further in R&D to produce more ERS or solutions utilizing emotion AI. Next, the research aimed to identify the determinants of ERS adoption. Five variables were put forth as possible determinants of ERS adoption readiness. As reported in Table 12, four variables were confirmed as significant determinants of BI. The four variables were AT, SN, PBC, and AW. AT, SN, and PBC were the original constructs of TPB. Thus, the findings are consistent with numerous prior studies [50; 51; 52; 53]. H\({}_{1}\) was not supported (see Table 12), suggesting that FC does not significantly affect BI. This is not consistent with identified earlier studies [60; 64; 65; 66; 67]. However, it is important to note that most of the earlier studies did not investigate ERS adoption specifically. For example, refs. [66; 67; 68] highlighted that infrastructural resources and specific policies may play a role in influencing user behavior and intention to use technology. The findings regarding H\({}_{1}\) suggest that there might be a lack of adequate structural support within the country to encourage users to adopt ERS. Alternatively, it could also indicate that there is a lack of clear and effective initiatives tailored to promoting or facilitating the implementation of ERS in the country. Thus, ERS practitioners should lobby for more targeted policies and initiatives for ERS diffusion from the government. Figure 5: Slope analysis (TA \(\times\) FC). H\({}_{2}\) was supported (see Table 12), suggesting that AT significantly affects BI. The finding is consistent with previous studies [52; 53; 69; 70] that have suggested that AT can be one of the important factors in ensuring the accelerated adoption of a specific technology [47] based on compatibility and adaptability in using the technology [108]. In other words, AT is an antecedent affecting a user's behavioral intention to adopt ERS. Since it was also confirmed earlier that Malaysian youths show a positive sentiment towards ERS, it can be expected that they have a good behavioral intention to adopt ERS. Organizations intending to launch an ERS solution should supplement its deployment with a prelaunch marketing campaign to create positive AT in the target market. H\({}_{3}\) was supported (see Table 12), suggesting that SN significantly affects BI. SN was defined as the level of approval by important others [53; 54; 82], and the decision to use or adopt a technology can be influenced by the social circle of the individual or user--for example, family members, peers within organizations or institutions, and friends that have been exposed to or experienced the behavior of interest, i.e., using such technology. SN was used to investigate whether peer approval, management perception, and social pressure from friends or families influenced adoption readiness for ERS. This finding is similar to previous studies [69; 70; 71].