By 2011, approximately 83% of Fortune 500 companies were using some form of social media to connect with consumers. Furthermore, surveys suggest that consumers are increasingly relying on social media to learn about unfamiliar brands. However, best practices regarding the use of social media to bolster brand evaluations in such situations remain undefined. This research focuses on one practice in this domain: the decision to hide or reveal the demographic characteristics of a brand's online supporters. The results from four studies indicate that even when the presence of these supporters is only passively experienced and virtual (a situation the authors term “mere virtual presence”), their demographic characteristics can influence a target consumer's brand evaluations and purchase intentions. The findings suggest a framework for brand managers to use when deciding whether to reveal the identities of their online supporters or to retain ambiguity according to (1) the composition of existing supporters relative to targeted new supporters and (2) whether the brand is likely to be evaluated singly or in combination with competing brands.

This paper presents a definitive description of neural network methodology and provides an evaluation of its advantages and disadvantages relative to statistical procedures. The development of this rich class of models was inspired by the neural architecture of the human brain. These models mathematically emulate the neurophysical structure and decision making of the human brain, and, from a statistical perspective, are closely related to generalized linear models. Artificial neural networks are, however, nonlinear and use a different estimation procedure (feed forward and back propagation) than is used in traditional statistical models (least squares or maximum likelihood). Additionally, neural network models do not require the same restrictive assumptions about the relationship between the independent variables and dependent variable(s). Consequently, these models have already been very successfully applied in many diverse disciplines, including biology, psychology, statistics, mathematics, business, insurance, and computer science. We propose that neural networks will prove to be a valuable tool for marketers concerned with predicting consumer choice. We will demonstrate that neural networks provide superior predictions regarding consumer decision processes. In the context of modeling consumer judgment and decision making, for example, neural network models can offer significant improvement over traditional statistical methods because of their ability to capture nonlinear relationships associated with the use of noncompensatory decision rules. Our analysis reveals that neural networks have great potential for improving model predictions in nonlinear decision contexts without sacrificing performance in linear decision contexts. This paper provides a detailed introduction to neural networks that is understandable to both the academic researcher and the practitioner. This exposition is intended to provide both the intuition and the rigorous mathematical models needed for successful applications. In particular, a step-by-step outline of how to use the models is provided along with a discussion of the strengths and weaknesses of the model. We also address the robustness of the neural network models and discuss how far wrong you might go using neural network models versus traditional statistical methods. Herein we report the results of two studies. The first is a numerical simulation comparing the ability of neural networks with discriminant analysis and logistic regression at predicting choices made by decision rules that vary in complexity. This includes simulations involving two noncompensatory decision rules and one compensatory decision rule that involves attribute thresholds. In particular, we test a variant of the satisficing rule used by Johnson et al. (Johnson, Eric J., Robert J. Meyer, Sanjoy Ghose. 1989. When choice models fail: Compensatory models in negatively correlated environments. (August) 255–270.) that sets a lower bound threshold on all attribute values and a “latitude of acceptance” model that s...

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