안형준 교수

Despite the widespread adoption of Enterprise Resource Planning (ERP) systems, many companies struggle to achieve the expected benefits from ERP. It has been especially observed that many companies fail to gain significant benefits from ERP in the post-implementation stage, regardless of their initial implementation success. This paper focuses on this post-implementation stage and empirically studies the factors that influence the performance of ERP in this stage. Through the review of related l

This paper presents a novel approach to automated product recommendation based on the popularity characteristics of products. Popularity plays a significant role in the consumer purchasing process but has not been given much attention in recommendation research. A three-dimensional model of popularity is used to develop popularity classes of products. These are joined with the MovieLens dataset to create a hybrid movie recommendation system that combines genre and popularity information. As comp

The advancement of Internet-based commerce has created a turbulent market environment by allowing easier introduction of new products, services, and suppliers. For this dynamic environment, information systems need to be designed in a flexible way to meet the changing requirements. This paper proposes a flexible transaction framework for multi-agent systems so that agent systems can adapt to various types of changes in the dynamic electronic market environment. The flexible transaction framework

Utilizing conversation policies, also called as interaction protocols, is one of the various efforts that have been devoted to the improvement of agent interoperability. The research on conversation policies so far has focused on developing conversation policy models and standard conversation policies. However, in a dynamic agent society where new agents can be introduced and transaction sequences can be changed, there is a need for agents to handle ad-hoc conversation policies. For this reason,

One of the widely used methods for product recommendation in internet storefronts is matching product features with target customer profiles. When using this method, it is very important to choose a suitable subset of features for recommendation efficiency and performance, which, however, has not been rigorously researched so far. In this paper, we utilise a dataset collected from a virtual shopping experiment in a Korean internet book shopping mall to compare several popular methods of feature