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This study used social representation theory to explore the role of categorization, derived attributes, and degree of liking in predicting acceptance, measured as post-exposure liking and preferred frequency to use a new functional drink.
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Religious commitment and trust in science predicted acceptance of all contexts of evolution when both variables were included in the model (see Table 6).
In total, 62 (66%) were found to predict acceptance in at least one study.
Likewise, trust in science and political orientation predicted acceptance of all evolution contexts when both variables were included in the model.
Among these models, the Technology Acceptance Model (TAM) is believed to be one of the most well-researched, robust and parsimonious model in predicting user acceptance of technology (Cornell et al., 2011; Venkatesh & Davis, 2000).
The tests were carried out for several heliostats, located in representative positions relative to the predicted acceptance contours of the concentrator.
Descriptive statistics for the variables used to predict acceptance of evolution are shown in Table 1.
We were striving for parsimony in our effort to create an equation that could be effectively used to predict acceptance of evolution and explain the greatest level of variance in acceptance using the fewest number of variables.
The study reported here sought to identify factors that predict acceptance of technologies based on OSS after training in these solutions.
Any variations in results from year to year owes more to respondents' inability to predict acceptance rates and total admissions with unfailing accuracy than to any inherent flexibility in the quotas.
The variation in percentage of acceptance by major is not significant, and major cannot be used to predict acceptance to medical school.
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