Consumer Behavior

Peer-to-Peer (P2P) is a communication structure in which individuals interact directly, without going through a centralized system. The implications of this architecture go far beyond the technological realm; the ability of individuals to share digital content files, including audio and video material, in real time, facilitates communication and, at a deeper cultural level, promotes community without hierarchy or strict control.

he Internet is not simply a set of interconnected links and protocols---it is also a construct of the imagination, an inkblot test into which everyone projects their desires, fears, and fantasies. Some see enlightenment and education. Others see pornography and gambling. Some see sharing and collaboration. Others see spam and viruses. Yet when it comes to the impact on the democratic process, the answer seems unanimous. The Internet is good for democracy. It creates digital citizens active in the teledemocracy [1] of the Electronic Republic [2] in the e-nation [3].

In the intervening years since publication of the chapter "Affect and Consumer Behavior" (Cohen & Areni, 1991) in the Handbook of Consumer Behavior (Kassarjian & Robertson, 1991), research in consumer behavior dealing with affect has exploded, making it one of the field's central research topics. Within psychology more generally, Schimmack and Crites (2005) located 923 references to affect between 1960 and 1980 and 4,170 between 1980 and 2000.

We identify gaps and propose several directions for future research in preference measurement. We structure our argument around a framework that views preference measurement as comprising three interrelated components: (1) the problem that the study is ultimately intended to address; (2) the design of the preference measurement task and the data collection approach; (3) the specification and estimation of a preference model, and the conversion into action. Conjoint analysis is only one special case within this framework.

We develop a structural demand model that captures the effect of out-of-stocks on customer choice. Our estimation method uses store-level data on sales and partial information on product availability. Our model allows for flexible substitution patterns which are based on utility maximization principles and can accommodate categorical and continuous product characteristics. The methodology can be applied to data from multiple markets and in categories with a relatively large number of alternatives, slow moving products and frequent out-of-stocks.

This paper explores the politics of the Kelo backlash by analyzing the content of public opinion published in editorials, op-eds, editorial cartoons, and letters-to-the editor on the pages of the nation's daily papers. Work by Nader, Diamond, and Patton (2006) analyzes public opinion from five polls conducted in fall 2005, after the decision came down.

The Institute of Medicine identified "timeliness" as one of six key "aims for improvement" in its most recent report on quality. Yet patient delays remain prevalent, resulting in dissatisfaction, adverse clinical consequences, and often, higher costs. This tutorial describes several areas in which patients routinely experience significant and potentially dangerous delays and presents operations research (OR) models that have been developed to help reduce these delays, often at little or no cost.

We consider a revenue management, network capacity control problem in a setting where heterogeneous customers choose among the various products offered by a firm (e.g., different flight times, fare classes, and/or routings). Customers may therefore substitute if their preferred products are not offered. These individual customer choice decisions are modeled as a very general stochastic sequence of customers, each of whom has an ordered list of preferences. Minimal assumptions are made about the statistical properties of this demand sequence.

We analyze a planning model for a firm or public organization that needs to cover uncertain demand for a given item by procuring supplies from multiple sources. Each source faces a random yield factor with a general probability distribution. The model considers a single demand season. All supplies need to be ordered before the start of the season. The planning problem amounts to selecting which of the given set of suppliers to retain, and how much to order from each, so as to minimize total procurement costs while ensuring that the uncertain demand is met with a given probability.