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Online shopping cart abandonment presents a major problem for online fashion apparel retailers today. This exploratory research aims to validate scales that measure antecedents of online shopping cart abandonment (OSCA) and examine how these reasons contribute to OSCA behaviour. The findings indicated that the eight different reasons (financial reasons, organisational tool, time pressure, intangibility, privacy issues, aesthetic design, social influences and entertainment factors) that drive OSCA are distinct and account for unique variance in the model, validating the measures. Also, the findings revealed that financial reasons and using the cart as an organisational tool are the top two reasons why consumers abandon their carts. This study provides researchers with a better theoretical understanding of the reasons why consumers abandon their online shopping carts. It validates the various reasons why consumers abandon their shopping carts and provides valuable managerial insights on how online marketers may enhance the translation of online browsing behaviour into actual purchases.

Are the learning procedures of genetic algorithms (GAs) able to generate optimal architectures for artificial neural networks (ANNs) in high frequency data? In this experimental study, GAs are used to identify the best architecture for ANNs. Additional learning is undertaken by the ANNs to forecast daily excess stock returns. No ANN architectures were able to outperform a random walk, despite the finding of non-linearity in the excess returns. This failure is attributed to the absence of suitable ANN structures and further implies that researchers need to be cautious when making inferences from ANN results that use high frequency data.