Abstract Online group buying is a burgeoning business model of Internet shopping, in which people with the same merchandise interests form a group and co-purchase goods with favorable prices. The buyer who launches the co-purchase is called the initiator, and other buyers are called the co-purchasers. Although recommending co-purchasers for a target buyer (co-purchase initiator) on the group buying is an interesting problem, existing studies have paid few attention to this topic. Different from the collaborator recommendation that only considers users with high similarity to the target user, co-purchaser recommendation takes both users with high and weak similarity into account, and the recommendation results can achieve high recall and diversity. However, the task turns out to be a challenging problem since it is hard to make a precise recommendation for buyers with weak similarity. To address the problem, we propose the following two methods. In the first one, we directly impose a penalty to the weak similar co-purchasers in the embedding space. To further improve the recommendation performance, in the second one, we smoothly increase the co-occurrence probability of the weak similar co-purchasers by truncated bias walk. Our experimental results on real datasets show that the proposed methods, particularly the latter, can effectively complete the co-purchaser recommendation and has high recommendation performance. In addition, considering that co-purchase may last longer, the total recommendation result can be generated in multiple stages and adjust the current recommendation list based on the feedback from the recommendation of previous stages. It is a trick for all co-purchaser recommendation methods to make the total result better.

We investigate an e-commerce stages of growth model in a cross-cultural business context for small Chinese firms in New Zealand. Research findings from 14 case studies show that the Chinese owners/managers of these small firms have a high power distance, and their attitude toward e-commerce technology directly influences their firms’ e-commerce growth process. It was found that the higher the stage of e-commerce adoption, the greater the need for owners having a more positive attitude toward e-commerce, more innovativeness and enthusiasm, and more technology literacy. The higher the tolerance for ambiguity and the higher the risk-taking propensity, the higher the stage of e-commerce adoption achieved. In addition, firms at lower growth stages of e-commerce adoption are highly rated on individualism, while those firms at higher growth stages of commerce adoption are highly rated on collectivism. The research has implications for small business managers operating in a cross-cultural business context as they move through the different stage of e-commerce adoption.

Beamlets are combined with zero-tree coding algorithm to create a new coding method, particularly suitable for lines and curves. Beamlets are a multiscale collection of line segments at a range of scales, location, having a variety of lengths and orientations. The new coding scheme - named JBEAM is more efficient - in terms of bit rates - in coding binary curvy images in simulations.