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Fig. 3 Visualizations of the best-performing models. Visualizations of the models with the most weights produced by the Bayesvl package.

Figure 3 Visualizations of the best-performing models. Visualizations of the models with the most weights produced by the Bayesvl package.

Related Figures (9)

Fig. 1 Five tensions impinging on acceptance of emotional Al and non- conscious data harvesting.
Fig. 1 Five tensions impinging on acceptance of emotional Al and non- conscious data harvesting.
Fig. 2 Visualization of the empirical strategy. This diagram presents how elements of the TAM and minsponge model are coordinated to create th empirical strategy to help investigate the research questions. Overview of research design and methodology. Our metho- dology consists of three steps. First, we identify variables relevant to the study of the acceptance of non-conscious emotional data harvesting based on the original TAM. These variables are per- ceived utilities, perceived familiarity with the technologies, atti- tude toward AI systems. Second, we incorporate various factors from the mindsponge model of information filtering to inform/ nuance/expand TAM. These factors are personal core values (i.e., level of religiosity); environmental factors of culture (i.e., regions of home country) and politics (ie., political regime of the home country); and income level. Third, relying on Bayesian multi-level analysis, we build statistical models in which each of these per- sonal, political, and environmental factors form the varying intercepts in Bayesian network models (Ho et al., 2022; La and Vuong, 2019). Figure 2 provides a summary of how each afore- mentioned element (theoretical models, variables, statistical modeling choices) fit together into the empirical strategy of this study. The left side of Fig. 2 presents the above three steps, while the right side of Fig. 2 presents the process and standard of how to evaluate the models and how to report the research results.
Fig. 2 Visualization of the empirical strategy. This diagram presents how elements of the TAM and minsponge model are coordinated to create th empirical strategy to help investigate the research questions. Overview of research design and methodology. Our metho- dology consists of three steps. First, we identify variables relevant to the study of the acceptance of non-conscious emotional data harvesting based on the original TAM. These variables are per- ceived utilities, perceived familiarity with the technologies, atti- tude toward AI systems. Second, we incorporate various factors from the mindsponge model of information filtering to inform/ nuance/expand TAM. These factors are personal core values (i.e., level of religiosity); environmental factors of culture (i.e., regions of home country) and politics (ie., political regime of the home country); and income level. Third, relying on Bayesian multi-level analysis, we build statistical models in which each of these per- sonal, political, and environmental factors form the varying intercepts in Bayesian network models (Ho et al., 2022; La and Vuong, 2019). Figure 2 provides a summary of how each afore- mentioned element (theoretical models, variables, statistical modeling choices) fit together into the empirical strategy of this study. The left side of Fig. 2 presents the above three steps, while the right side of Fig. 2 presents the process and standard of how to evaluate the models and how to report the research results.
Table 1 (continued)
Table 1 (continued)
Table 3 Weight comparison. bootstrap), it is worth closely examining the outcome of Model 3_Public. We find that the results are similar to Model 2_ Public. As for the three less fitted models, Model 1_Public, Model 4 Public, and Model 5_Public, the results are quite consistent with the above. —0.0555-0.0453, respectively. Similar results are found in Model 3_Private and Model 1_Private as well.
Table 3 Weight comparison. bootstrap), it is worth closely examining the outcome of Model 3_Public. We find that the results are similar to Model 2_ Public. As for the three less fitted models, Model 1_Public, Model 4 Public, and Model 5_Public, the results are quite consistent with the above. —0.0555-0.0453, respectively. Similar results are found in Model 3_Private and Model 1_Private as well.
Fig. 4. The posterior distributions of variables in Model 2_Private.
Fig. 4. The posterior distributions of variables in Model 2_Private.
Table 4 Differences among the determinants of attitude toward the public vs. private sector's engagement with non-conscious data harvesting. relationship with the attitude toward the private sector. This insight suggests that there is less trust in the public sector’s handling of non-conscious data compared to the private sector, especially when the subject spends more time on social media. This result aligns with the results from a study of approximately 6000 American subjects, where Hidalgo et al., (2021) found people tend to judge AI more negatively when it is used by the government. Thus, according to the Bayesian mindsponge way of thinking, the result suggests acceptance toward non-conscious data harvesting is filtered through a With regards to converging results, the variables of familiarity with and perceived utility of AI technologies, as predicted by both TAM and the mindsponge model, are found to be positive correlates of attitude toward non-conscious emotional data harvesting in both sectors. In terms of differences, the most outstanding result centers on the dwelling time of a person on social media.
Table 4 Differences among the determinants of attitude toward the public vs. private sector's engagement with non-conscious data harvesting. relationship with the attitude toward the private sector. This insight suggests that there is less trust in the public sector’s handling of non-conscious data compared to the private sector, especially when the subject spends more time on social media. This result aligns with the results from a study of approximately 6000 American subjects, where Hidalgo et al., (2021) found people tend to judge AI more negatively when it is used by the government. Thus, according to the Bayesian mindsponge way of thinking, the result suggests acceptance toward non-conscious data harvesting is filtered through a With regards to converging results, the variables of familiarity with and perceived utility of AI technologies, as predicted by both TAM and the mindsponge model, are found to be positive correlates of attitude toward non-conscious emotional data harvesting in both sectors. In terms of differences, the most outstanding result centers on the dwelling time of a person on social media.
Fig. 5 . Posterior distributions of variables in Model 2_Public.
Fig. 5 . Posterior distributions of variables in Model 2_Public.
Fig. 6 . Visualization of the posterior distribution of the variables originated in the TAM.
Fig. 6 . Visualization of the posterior distribution of the variables originated in the TAM.
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emotional AI
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