Business survival in dynamic and competitive environments depends on an ability to analyze consumption information and determine which products consumers want. Collecting consumption data, hence, becomes a primary activity for product and marketing strategy development. Businesses typically gather consumption data from consumers shopping in the retail market. Data for items purchased are transmitted through retail information systems to inventory controllers for product-ordering decisions. Although such decisions may reflect consumer behavior in the retail market, they cannot reflect consumer desires and wants. Since consumers can purchase only what retailers offer, consumer purchases represent consumer product choices only for limited offerings, rather than consumer needs and expectations. Thus, understanding consumer needs and wants, as well as online consumer behavior, is a significant issue for businesses wanting to provide customers with the best value (Stibel, 2005;Wisniewski, 2001).The means-end chain (MEC) methodology is typically applied to arrive at consumer value satisfaction (Gutman, 1982). Reynolds and Gutman (1988) adopted the logic of MECs to develop a laddering technique for understanding consumer product cognition. When a cutoff value is determined, the salient attribute-consequence-value ( A-C-V ) linkages, by computing the frequency of consumer A-C-V linkages for a given product, can be determined and displayed in a hierarchical value map (HVM). Marketers can use HVMs to generate product, advertising, and segmentation strategies. This study, which is based on that by Reynolds and Olson (2001), applies the MEC methodology to the analysis and understanding of consumer decision making and adopts dynamic programming (DP) analysis to enhance the computing procedure in the conventional laddering technique. Figure 1 exhibits the conceptual framework of the DP approach. In laddering data collection, all data are input into a table for the summary implication matrix (SIM). The DP and systematic procedures can transform the laddering data into meaningful information for consumer product cognition. Such information can assist marketers in understanding consumer perceptions and in developing effective marketing strategies.In the traditional MEC methodology, the cutoff value is predetermined subjectively by the researcher in order to establish an HVM. In the present study, DP analysis overcomes the controversy associated with subjective cutoff value decisions and is the basis for establishing the information system, which stores consumer preference perceptions of a particular product. The following is a list of study objectives.1. To capitalize on the advantages of DP in analyzing MEC data sets, this study integrates DP and laddering procedures to gather consumer preference information.2. This study uses an information flowchart to demonstrate the logic of DP, leading to an analysis of consumer decision making. Such an analysis will provide marketers with salient market intelligence for rapid response to m...