Systematic behavior research for understanding consumer decision making

Abstract

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...

The means‐end cognitions of web advertising: a cross‐cultural comparison

“…After obtaining the accumulated A‐C‐V linkage frequencies for each sample country, the authors put them into the summary implication matrix for constructing the cognitive structure map of each country. The cognitive A‐C‐V structures of credit card web pages were constructed using the cutoff value originally developed by Pieters et al (1995) because their criteria can predetermine the cutoff value objectively (Lin, 2009). The main principle of determining the cutoff value is to use fewer A‐C‐V linkages to present the most content of cognitive structures.…”

“…The MEC model links a product's attributes to the consequences for consumers of using the product or service, and to the personal values that consumers place on its use. The linkage hierarchies of attribute‐consequence‐value allow marketers to understand consumers' cognitive structures clearly, enabling them to develop and assess effective advertising strategies (Lin, 2009; Lin and Wang, 2008; Parry, 2002; Reynolds and Olson, 2001; Wagner, 2007). Reynolds and Gutman (1988) concluded that the series of cognitive hierarchies in the MEC model can be applied to formulate advertising strategies.…”

“…Many approaches have been designed to formulate effective advertising strategies. In the marketing literature the predominant method of examining the text content of advertisements perceived by consumers is means‐end chain (MEC) analysis (Lin, 2009; Lin and Wang, 2008; Lin et al , 2006; Reynolds and Olson, 2001). According to MEC theory, product attributes are ways of obtaining consumer desires, through the consequences on consumption experience (Gutman, 1982).…”

Means-End Matrix and Deduction in Consumption Behavior Research

“…Many articles have focused on the integration of multivariable and MEC analyses (Fu & Wu, 2010;Goldenberg et al, 2000;Langerak et al, 1998;Lin, 2009;Orsingher & Marzocchi, 2003), while only some have mentioned the weaknesses of using the MEC methodology (Kaciak & Cullen, 2006;. The major issues related to the MEC methodology are as follows:…”

“…Desirable or undesirable consequences are post-consumption ones, which can be positive or negative. Positive feelings denote the benefits perceived by consumers, while negative feelings represent associated or perceived risks that influence consumer willingness to repurchase a product (Goldenberg, Klenosky, O’Leary, & Templin, 2000; Lin, 2009). Consequently, several researchers have adopted benefits (positive feelings) instead of consequences (Lin, 2003; Vriens & Ter Hofstede, 2000; Woodside, 2004), thus replacing the original attribute-consequence-value ( A - C - V ) chains in MEC analysis with attribute-benefit-value ( A - B - V ) chains.…”