Obesity is a rapidly growing public health threat as well as an economic problem in the United States. The recent changes in eating habits, especially the relative increase of food away from home (FAFH) consumption over the last three decades raised the possibility of causal linkage between obesity and FAFH. This study confirms the positive, significant association between the body mass index and FAFH consumption in adults, consistent with previous findings in the economic and nutrition literature. This work goes a step further, however. We demonstrate FAFH consumption at quick-service restaurants has a significantly larger effect on body mass index than FAFH consumption at full-service restaurants. Further disaggregation of FAFH by meal occasion reveals that lunch consumed away from home has the largest positive effect on body mass index compared to other meal occasions (breakfast, dinner and snacks). Survey data with missing observations or latent variables are not rare phenomena. The missing value imputation methods are combined into two groups, contingent upon the existence or absence of an underlying explicit statistical model. Explicit modeling methods include unconditional mean value imputation, conditional mean and regression imputation, stochastic regression imputation, and multiple imputation. The methods based on implicit modeling include hot deck and cold deck imputation. In the second essay, we review imputation methods commonly used in the agricultural economics literature. Our analysis revealed strong preference of researchers for the regression imputation method. We consider several alternative (regression, mean and median) single imputation methods to impute and to append prices of foods consumed at home (foods commercially purchased and prepared from ingredients) from the National Health and Nutrition Examination Survey (NHANES) dietary intake data. We also demonstrate the superiority of regression imputation method compared to the mean and median imputation methods for commercially prepared foods. For ingredient foods, the results are ambiguous with no imputation method clearly outperforming the others.

Obesity is a rapidly growing public health threat as well as an economic problem in the United States. The recent changes in eating habits, especially the relative increase of food away from home (FAFH) consumption over the last three decades raised the possibility of causal linkage between obesity and FAFH. This study confirms the positive, significant association between the body mass index and FAFH consumption in adults, consistent with previous findings in the economic and nutrition literature. This work goes a step further, however. We demonstrate FAFH consumption at quick-service restaurants has a significantly larger effect on body mass index than FAFH consumption at full-service restaurants. Further disaggregation of FAFH by meal occasion reveals that lunch consumed away from home has the largest positive effect on body mass index compared to other meal occasions (breakfast, dinner and snacks). Survey data with missing observations or latent variables are not rare phenomena. The missing value imputation methods are combined into two groups, contingent upon the existence or absence of an underlying explicit statistical model. Explicit modeling methods include unconditional mean value imputation, conditional mean and regression imputation, stochastic regression imputation, and multiple imputation. The methods based on implicit modeling include hot deck and cold deck imputation. In the second essay, we review imputation methods commonly used in the agricultural economics literature. Our analysis revealed strong preference of researchers for the regression imputation method. We consider several alternative (regression, mean and median) single imputation methods to impute and to append prices of foods consumed at home (foods commercially purchased and prepared from ingredients) from the National Health and Nutrition Examination Survey (NHANES) dietary intake data. We also demonstrate the superiority of regression imputation method compared to the mean and median imputation methods for commercially prepared foods. For ingredient foods, the results are ambiguous with no imputation method clearly outperforming the others.

Since the release of the new iPad in 2010, few studies have explored the idea of tablet- based menus in restaurants. Since this is a new topic in the hospitality industry, there has not been literature that explores how personal traits influence the adoption intention of tablet-based menus. This study aims to explain the impact of innovation characteristics and individual differences on customer intentions to adopt tablet-based menus in restaurants of different service levels. With a random sample of 430 participants collected via Amazon’s Mechanical Turk, a regression analysis and an ANOVA test were performed. The results confirmed that only three variables (relative advantage, compatibility, and restaurant type) make a statistically significant contribution to predicting the adoption intention of tablet-based menus. It was also found that adoption intention of tablet-based menus differs across three restaurant types (quick-service restaurant, midscale restaurant, and upscale restaurant). The findings of this study provide an important insight to restaurant managers who may consider implementing tablet-based menus at their establishments. Limitations and ideas for future research are discussed.