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The purpose of this paper is to estimate the impacts of women's education on the mean age of women at first birth (denoting the timing of fertility) and total fertility rate (TFR, denoting the level of fertility) using cross-country panel data.

The estimations proceed in two steps: first, the timing and level of fertility regressions are separately estimated, and second, two regressions are estimated at the same time as a form of a system equation to accommodate the correlations between error terms.

It is found that a higher women's education tends to delay of child birth or family formation. In addition, there exists a negative relationship between the female secondary school enrollment ratio and TFR, meaning that the opportunity costs of childbearing and rearing increases when the level of women's education enhances. However, the authors have also found that the impacts of women's higher education on TFR is statistically insignificant in a few cases of estimations without sample selections.

Fertility decline is a shift of childbearing to older ages. The delay of child birth or family formation is the major cause of the recent fertility decline, because a late women's age at first birth reduces the chances of having any further children. This implies that the timing and level of fertility are highly correlated to each other. In particular, many studies showed that women's education and employment have been identified as major parameters for the increase in women's age at first birth. Nonetheless, little attention has been paid to an empirical analysis of the relationship between women's education and the timing of fertility. Therefore, this paper is an extension of previous studies, estimating the relationship between women's education and the timing and level of fertility at the same time.

Understanding customers' revisiting behavior is highlighted in the field of service industry and the emergence of online communities has enabled customers to express their prior experience. Thus, purpose of this study is to investigate customers' reviews on an online hotel reservation platform, and explores their postbehaviors from their reviews.

The authors employ two different approaches and compare the accuracy of predicting customers' post behavior: (1) using several machine learning classifiers based on sentimental dimensions of customers' reviews and (2) conducting the experiment consisted of two subsections. In the experiment, the first subsection is designed for participants to predict whether customers who wrote reviews would visit the hotel again (referred to as Prediction), while the second subsection examines whether participants want to visit one of the particular hotels when they read other customers' reviews (dubbed as Decision).

The accuracy of the machine learning approaches (73.23%) is higher than that of the experimental approach (Prediction: 58.96% and Decision: 64.79%). The key reasons of users' predictions and decisions are identified through qualitative analyses.

The findings reveal that using machine learning approaches show the higher accuracy of predicting customers' repeat visits only based on employed sentimental features. With the novel approach of integrating customers' decision processes and machine learning classifiers, the authors provide valuable insights for researchers and providers of hospitality services.