Abstract

Consumers can have difficulty expressing their buying intentions on an explicit level. The most common explanation for this intention-action gap is that consumers have many cognitive biases that interfere with rational decision-making. The current resource-rational approach to understanding human cognition, however, suggests that brain environment interactions lead consumers to minimize the expenditure of cognitive energy according to the principle of Occam’s Razor. This means that the consumer seeks as simple of a solution as possible for a problem requiring decision-making. In addition, this resource-rational approach to decision-making emphasizes the role of inductive inference and Bayesian reasoning. Together, the principle of Occam’s Razor, inductive inference, and Bayesian reasoning illuminate the dynamic human-environment relationship. This paper analyzes these concepts from a contextual perspective and introduces the Consumer Contextual Decision-Making Model (CCDMM). Based on the CCDMM, two hypothetical strategies of consumer decision-making will be presented. First, the SIMilarity-Strategy (SIMS) is one in which most of a consumer’s decisions in a real-life context are based on prior beliefs about the role of a commodities specific to real-life situation being encountered. Because beliefs are based on previous experiences, consumers are already aware of the most likely consequences of their actions. At the same time, they do not waste time on developing contingencies for what, based on previous experience, is unlikely to happen. Second, the What-is-Out-there-in-the-World-Strategy (WOWS) is one in which prior beliefs do not work in a real-life situation, requiring consumers to update their beliefs. The principle argument being made is that most experimental consumer research describes decision-making based on the WOWS, when participants cannot apply their previous knowledge and situation-based strategy to problems. The article analyzes sensory and cognitive biases described by behavioral economists from a CCDMM perspective, followed by a description and explanation of the typical intention-action gap based on the model. Prior to a section dedicated to discussion, the neuroeconomic approach will be described along with the valuation network of the brain, which has evolved to solve problems that the human has previously encountered in an information-rich environment. The principles of brain function will also be compared to CCDMM. Finally, different approaches and the future direction of consumer research from a contextual point of view will be presented.

Highlights

  • The sheer number of consumption opportunities on the market outweighs consumers’ ability to assess them

  • This paper presents the Consumer Contextual Decision-Making Model (CCDMM), which is a new interpretation of a consumer’s decision-making from a contextual perspective

  • Whereas traditional economic models do not provide framework for connecting effects to environmental properties and are silent about decision-making context, traditional behavioral models maintain a consumer’s context sensitivity as source of many cognitive biases. These models may seem contradictory and mutually exclusive, CCDMM is not an alternative for traditional models; rather it may be viewed as an extension of them

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Summary

Introduction

The sheer number of consumption opportunities on the market outweighs consumers’ ability to assess them This limitation to human mental capacity is a problem for most decisionmaking models. Traditional models for consumer decisionmaking (Samuelson, 1938; Luce and Raiffa, 1989; Barry and Howard, 1990) assume that people are driven by explicit reasoning across all options These models conceptualize consumer decisions as a matter of choosing the best option from those available (Kőszegi, 2010). These models assume that people respond only to the features of the options available to them independent of context and unaffected by other available alternatives or temporal order. The observed behavior of consumers is much more complex than these traditional models assume (Dijksterhuis et al, 2006)

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