Marketing

Scarcity has long been known to impact consumers' choices. Yet, the impact of shelf-based scarcity in retail environments, created by stocking level depletion, has received almost no attention in the literature. Indeed, little research to date has even examined if consumers will attend to shelf-based scarcity in retail environments, much less how this cue can impact choice. A priori, given the inherently noisy and cue-filled nature of retail environments, it is quite reasonable to expect that shelf-based scarcity would play little to no role in consumers' choices.

Capacity decisions in service operations often involve a trade-off between operating cost and the level of service offered to customers. Although the cost of attaining a pre-specified level of service has been well-studied, there isn't much research studying how customer service levels affect revenue and profit. This paper conducts an empirical study to analyze how waiting in a queue in the context of a retail store affects customer purchasing behavior. Our methodology uses a novel technology based on digital imaging to record periodic information about the queuing system.

Although people have been shown to rely on feelings to make judgments, the conditions that moderate this reliance have not been systematically reviewed and conceptually integrated. This article addresses this gap by jointly reviewing moderators of the reliance on both subtle affective feelings and cognitive feelings of ease-of-retrieval.

A great deal of research in consumer decision-making and social-cognition has explored consumers’ attempts to simplify choices by bolstering their tentative choice candidate and/or denigrating the other alternatives. The present research investigates a diametrically opposed process, whereby consumers complicate their decisions.

We examine how consumers update their confidences in ordinal (relative) judgments while evaluating sequential product-ranking and source-accuracy data in percentage versus frequency formats. The results show that when sequential data are relatively easier to mathematically combine (e.g., percentage data), consumers revise their judgments in a way that is consistent with an averaging model but inconsistent with the normative Bayesian model.

In this research we propose a web-based adaptive self-explicated approach for multi-attribute preference measurement (conjoint analysis) with a large number (ten or more) of attributes. Our approach overcomes some of the limitations of previous self-explicated approaches. We developed a computer-based self-explicated approach that breaks down the attribute importance question into a sequence of constant-sum paired comparison questions.