[German title: Expert Systems with Applications, 34 (3), 2008 – interessante Artikel]
Mining customer knowledge for product line and brand extension in retailing 1763-1776
Shu-Hsien Liao, Chyuan-Meei Chen and Chung-Hsin Wu
- Abstract: Retailing consists of the final activities and steps needed to place a product in the hands of the consumer or to provide services to the consumer. In fact, retailing is actually the last step in a supply chain that may stretch from Europe or Asia to the customer’s hometown. Therefore, any firm that sells a product or provides a service to the final consumer is performing the retailing function. On the other hand, product line extension, which adds depth to an existing product line by introducing new products in the same product category, can give customers greater choice and help to protect the firm from flanking attack by a competitor. In addition, a product line extension is marketed under the same general brand as a previous item or items. Thus, to distinguish the brand extension from the other item(s) under the primary brand, the retailer can either add secondary brand identification or add a generic brand. This paper investigates product line and brand extension issues in the Taiwan branch of a leading international retailing company, Carrefour, which is a hypermarket retailer. This paper develops a relational database and proposes Apriori algorithm and K-means as methodologies for association rule and cluster analysis for data mining, which is then implemented to mine customer knowledge from household customers. Knowledge extraction by data mining results is illustrated as knowledge patterns/rules and clusters in order to propose suggestions and solutions to the case firm for product line and brand extensions and knowledge management.
- Keywords: Retailing; Product line extension; Brand extension; Data mining; Association rules; Cluster analysis; Knowledge extraction
- doi: 10.1016/j.eswa.2007.01.036
Design of a knowledge-based performance evaluation system: A case of high-tech state-owned enterprises in an emerging economy 1795-1803
Wei-Kang Wang, Hao-Chen Huang and Mei-Chi Lai
- Abstract: This paper describes an intelligent decision support system for evaluating state-owned enterprises (SOEs) using DEA models. This decision support system converts numerical data into information that can be used to evaluate state-owned enterprises (SOEs). In this paper, we use DEA models, including CCR, BCC and FDH, to shed new light on the operational efficiency and determinants of state-owned enterprises in Beijing. We propose an evaluation framework for evaluating state-owned enterprises (SOEs), including a database management subsystem, a model base subsystem, a knowledge acquisition subsystem, and a dialogue subsystem. Finally, we adopt the logistic regression model to analyze the effect factors of inefficient state-owned enterprises and provide optimum decision-making for state-owned enterprises (SOEs).
- Keywords: Decision support system; Knowledge-based system; Performance evaluation; Data envelopment analysis
- doi: 10.1016/j.eswa.2007.01.032
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