Abstract

This research aims to demonstrate that the abundant marketing data that companies are using to explore new business opportunities can be an equally fertile source for uncovering an undesirable social attitude or behavior that may be relevant to firms' business. Companies may benefit from this knowledge when developing innovative new programs that aim to benefit society, such as corporate social responsibility initiatives. In this study, we examine boy-girl gender discrimination in China as manifested in parents' purchase decisions on behalf of their children across different markets. Our study in itself is significant, because it is the first large-scale empirical work to clearly verify the phenomenon of boy-girl discrimination, taking advantage of e-commerce marketing data. Specifically, we compare the clothing expenditures on boys versus girls using a rich, household-specific data set obtained from two online retailers. We find that the patterns of gender inequality vary systematically across different geographic markets, as the relative expenditure difference on boys versus on girls is bigger in less developed areas as compared with metropolitan areas, and this relative expenditure difference is closely tied with socioeconomic conditions, education levels, and birth rates of a district. Managerial and social implications are discussed.

Keyword

boy-girl discrimination ;  cause-related marketing ;  corporate social responsibility ;  gender inequality

Abstract

This research aims to demonstrate that the abundant marketing data that companies are using to explore new business opportunities can be an equally fertile source for uncovering an undesirable social attitude or behavior that may be relevant to firms’ business. Companies may benefit from this knowledge when developing innovative new programs that aim to benefit society, such as corporate social responsibility initiatives. In this study, we examine boy-girl gender discrimination in China as manifested in parents’ purchase decisions on behalf of their children across different markets. Our study in itself is significant, because it is the first large-scale empirical work to clearly verify the phenomenon of boy-girl discrimination, taking advantage of e-commerce marketing data. Specifically, we compare the clothing expenditures on boys versus girls using a rich, household-specific data set obtained from two online retailers. We find that the patterns of gender inequality vary systematically across different geographic markets, as the relative expenditure difference on boys versus on girls is bigger in less developed areas as compared with metropolitan areas, and this relative expenditure difference is closely tied with socioeconomic conditions, education levels, and birth rates of a district. Managerial and social implications are discussed.

Keyword

boy-girl discrimination ;  cause-related marketing ;  corporate social responsibility ;  gender inequality

Abstract

Product returns will soon cost firms a trillion dollars annually; e-commerce appears more at risk for these costs relative to offline channels. Retailers thus need strategic insights into which factors increase customers’ return propensity and when the short-term costs of returns might be offset by future customer purchases. This article reports two studies that use transaction data from two large apparel e-tailers. Study 1 demonstrates that due to the differences between mobile and traditional online channels, consumers form a larger consideration set when they shop through mobile channels, and this distinctive search practice spills over onto return rates. That is, return rates are lower in mobile channels, and the use of the mobile channel also alters the effect of discount promotions. The findings in Study 2 suggest that future purchases by consumers, after a return, are contingent on category characteristics. In categories in which customers can easily learn from their previous return experience, product returns positively increase future spending, but the opposite is true in categories in which it is difficult to learn.

Keyword

Channel use ;  Customer learning ;  E-commerce ;  E-tailer ;  Product returns ;  Smartphone