In-Store Experimental Approach to Pricing and Consumer Behavior

Healthy Eating: Approaching the Selection, Preparation, and Consumption of Healthy Food as Choice Behavior

Healthy eating has important well-being and financial implications for our society. As such, it is critical that the field of behavior science and behavior analysis conduct more research in this area so that effective interventions may be developed. One barrier to addressing healthy eating may be conceptual. Far from being a single response, eating is comprised of a series of choice responses. These selection, preparation and consumption responses form a temporally delayed behavioral chain. When designing interventions to address healthy eating, therefore, one must not only consider the specific target response, but alternative response options, and the effect of changing one response on other choices in the chain. The purpose of this article is to refine the analysis of healthy eating behavior, provide examples of research conducted in this area, and discuss how these interventions may influence this chain of responses. It is hoped that by doing so, additional research will be conducted and disseminated so that individuals, organizations, and policy makers can implement more effective interventions for healthy eating.

The Use of Observational Technology to Study In-Store Behavior: Consumer Choice, Video Surveillance, and Retail Analytics

The store is the main laboratory for in-store experimental analysis. This article provides an introduction to a research program aimed at improving research practices in this laboratory, particularly emphasizing the importance of behavioral data and the new opportunities that technology offers. This complex modern-day Skinner box has sets of well-studied stimuli-behavior interactions that constantly adapt to the latest economic environment and as such constantly stretch the boundaries of behavioral analytic theory. However, the retail setting is highly important to applied behavior analysis for such issues as health, debt, environmental conservation, animal welfare, self-control, and consumer protection in general. This article presents a research strategy that emphasizes key environmental touch points throughout the customer journey in grocery retailing. We highlight the latest development by examining a particular research case and discussing the need for behavioral economic understanding of the start of the grocery journey, that is, the consumer choice of in-store product carrying equipment (e.g., cart, basket, or nothing). The conceptual system consists of a molecular four-term contingency framework as well as a more molar approach with conversion-rate modeling, where actual choice behavior is detected through video surveillance. The data are analyzed using a Shopper Flow© Tracking System in which software is designed to provide automatic data on shopper behavior and to assist human observers in tracking individual shopping trips. We discuss behavioral classifications, methodology, and implications related to the data from consumer tracking efforts.

Online Healthy Food Experiments: Capturing Complexity by Using Choice-Based Conjoint Analysis

The impact of complex environmental factors on consumer choices and preferences can be analyzed through the prism of consumer behavior analysis, whereas variations of marketing attributes and their impact on choice can be measured using conjoint analysis. Considering the case of the constantly growing online food selections, we discuss choice-based conjoint analysis and explore the opportunities for behavior analysts to examine the interrelationships of multiple variables and socially important choice settings, and to promote desired behaviors. We show a few examples of using trade-off analyses in online food retail to understand consumer behavior with respect to healthy food items. As demonstrated in these examples based on our own pilot research, conjoint analysis can be used for complex behavior-that which is not amenable directly to an experimental analysis-or as an efficient initial step before moving into further experiments or analyses using biometrics (e.g., eye-tracking) or web analytics conducted in different settings such as e-commerce, e-mail, social media, or on mobile platforms. This paper summarizes the personalized, data driven economic analysis that is possible with a choice-based conjoint analysis.