DYNAMIC CONSUMER DECISION MAKING PROCESS IN E-COMMERCE

Abstract

This dissertation studies the dynamic decision making process in E-commerce. In the first essay, we use eye tracking to investigate how consumers make information acquisition decisions on attribute-by-product matrices in online choice environment such as comparison websites. Hierarchical Hidden Markov Model is used to describe this process. The model consists of three connected hierarchical layers: a lower layer that describes the eye movements, a middle layer that identifies product- and attribute-based information acquisition modes, and an upper layer that flexibly captures switching between these modes over time. Findings of a controlled experiment show that low-level properties of the eye and the visual brain play an important role in dynamic information acquisition. Consumer switch frequently between two acquisition modes, and higher switching frequency increases decision time and reduces easiness of decision making. These results have implications for web design and online retailing, and may open new directions for research and theories of online choice.

The second essay investigates how usage experience with different types of decision aids contributes to the evolution of online shopping behavior over time. In the context of online grocery stores, we categorize four types of decision aids that are commonly available, namely, those 1) for nutritional needs, 2) for brand preference, 3) for economic needs, and 4) personalized shopping lists. We construct a Non-homogeneous Hidden Markov Model of category purchase incidence and purchase quantity, in which parameters are allowed to vary over time across hidden states as driven by usage experience with different decision aids. The dataset was collected during the period when the retailer first launched its web business, which makes it particularly suited to study the evolution of online purchase behavior. We estimate the model for the spaghetti sauce and liquid detergent categories. Results indicate that four types of decisions influence evolution of purchase behavior differently. Findings from this study enrich the understanding of how purchase behavior may evolve over time in online stores, and provide valuable insights for online retailers to improvement the design of their store environments.