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With the rapid growth of online grocery shopping in South Korea, e-grocery retailers have faced intense competition. Consequently, they attempt to differentiate themselves by offering diversified delivery services, such as providing early morning delivery services or eco-friendly packaging. The purpose of this paper is to analyze consumer preferences with regards to delivery services with different delivery times and packaging types targeting South Korea’s online grocery markets. Specifically, six delivery types consisting of combinations of two delivery time options (dawn, daytime) and three packaging types (paper box, market cooler bag, personal icebox) are examined. A survey was conducted in July 2020 with 218 consumers who regularly buy fresh food online. The collected data were analyzed by means of a conjoint analysis and ANOVA. The present study finds that customers most strongly prefer dawn delivery using a personal icebox, followed by dawn delivery using a market cooler bag, with daytime delivery using a paper box being the least preferable. Furthermore, consumers value the packaging type more than the delivery time when selecting a delivery service. Lastly, the preferences of consumers regarding delivery service types differ according to their characteristics. This study is expected to contribute to the establishment of a delivery strategy for online grocery companies.

The rapid advances in information and communications technologies, combined with increased social awareness of today’s consumers, ushered in transformation in consumers’ purchase behavior. This big shift away from the traditional ownership-based economy into a sharing economy is causing an urgent necessity to understand the shifted decision mechanism. Although there have been quite a few previous studies on antecedents of consumers’ decision to engage in collective consumption, it is somewhat difficult to find previous research that focused on consumption values as antecedent predictors. To abridge this gap, this study aims to determine whether two predictors (social value and ethical value) affect consumers’ intention to utilize collective consumption. In addition, the current study verifies the role of perceived risk as a moderator between perceived trust and the intention to use collective consumption. For the purpose of hypothesis verification, the study uses multiple regression and hierarchical moderated regression analysis. Responses from a total of 246 respondents are used for statistical analysis. The respondents were selected from the students attending a university located in Seoul, South Korea. The results indicate that social value and ethical value exerted significant impacts on the perceived trust of collective consumption. However, the magnitude of the social value’s impact was slightly greater than that of ethical value. The result also confirms the mediating role of the perceived trust. Furthermore, perceived risk moderated the relationship between trust and the intention to utilize collective consumption.

Online customer reviews are a sustainable form of word of mouth (WOM) which play an increasingly important role in e-commerce. However, low quality reviews can often cause inconvenience to review readers. The purpose of this paper is to automatically predict the helpfulness of reviews. This paper analyzes the characteristics embedded in product reviews across five different product types and explores their effects on review helpfulness. Furthermore, four data mining methods were examined to determine the one that best predicts review helpfulness for each product type using five real-life review datasets obtained from Amazon.com. The results show that reviews for different product types have different psychological and linguistic characteristics and the factors affecting the review helpfulness of them are also different. Our findings also indicate that the support vector regression method predicts review helpfulness most accurately among the four methods for all five datasets. This study contributes to improving efficient utilization of online reviews.