We conduct a firm‐level, 6‐year longitudinal analysis on the impact that racial diversity in human resources has on financial performance. When considering short‐term performance outcomes, we predict a curvilinear relationship between diversity and performance (i.e., firm productivity). Although we find evidence of a U‐shaped relationship between racial diversity and productivity, the relationship is stronger in service‐oriented relative to manufacturing‐oriented industries and in more stable vs. volatile environments. For longer‐term profitability, we propose and find support for more of a positive linear relationship between diversity and performance (i.e., Tobin's q) than a nonlinear one. This linear effect is stronger and more positive in munificent compared to resource‐scare environments. Thus, we aid in reconciling existing, often contradictory, studies by demonstrating the potential short‐term vs. long‐term impact of racial diversity on performance. We offer implications for future research on diversity considering the current and projected demographic landscape. Copyright © 2007 John Wiley & Sons, Ltd.

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We present a review of research studies that deal with personalization and synthesize current knowledge about these areas. We identify issues that we envision will be of interest to researchers working in the management sciences, taking an interdisciplinary approach that spans the areas of economics, marketing, information technology (IT), and operations research. We present a framework for personalization that allows us to identify key players in the personalization process as well as key stages of personalization. The framework enables us to examine the strategic role of personalization in the interactions between a firm and other key players in the firm's value system. We conceptualize the personalization process as consisting of three stages: (1) learning about consumer preferences, (2) matching offerings to customers, and (3) evaluation of the learning and matching processes. This review focuses on the learning stage, with an emphasis on utility-based approaches to estimate preference functions using data on customer interactions with a firm.Customization, Choice Models, Internet Marketing, Online Tracking, Learning Consumer Preferences, Recommendation Systems

Models of advertising response implicitly assume that the entire advertising budget is spent on disseminating one message. In practice, managers use different themes of advertising (for example, price advertisements versus product advertisements) and within each theme they employ different versions of an advertisement. In this study, we evaluate the dynamic effects of different themes of advertising that have been employed in a campaign. We develop a model that jointly considers the effects of wearout as well as that of forgetting in the context of an advertising campaign that employs five different advertising themes. We quantify the differential wearout effects across the different themes of advertising and examine the interaction effects between the different themes using a Bayesian dynamic linear model (DLM). Such a response model can help managers decide on the optimal allocation of resources across the portfolio of ads as well as better manage their scheduling. We develop a model to show how our response model parameters can be used to improve the effectiveness of advertising budget allocation across different themes. We find that a reallocation of resources across different themes according to our model results in a significant improvement in demand.Bayesian dynamic linear models, Gibbs sampling aggregate advertising models, wearout effects, forgetting effects, copy effects, scheduling of ad copy

The authors study the joint effects of creative format, message content, and targeting on the performance of digital ads over time. Specifically, they present a dynamic model to measure the effects of various sizes of static (GIF) and animated (Flash) display ad formats and consider whether different ad contents, related to the brand or a price offer, are more or less effective for different ad formats and targeted or retargeted customer segments. To this end, the authors obtain six months of data on daily impressions, clicks, targeting, and ad creative content from a major U.S. retailer, and they develop a dynamic zero-inflated count model. Given the sparse, nonlinear, and non-Gaussian nature of the data, the study designs a particle filter/Markov chain Monte Carlo scheme for estimation. Results show that carry-over rates for dynamic formats are greater than those for static formats; however, static formats can still be effective for price ads and retargeting. Most notably, results also show that retargeted ads are effective only if they offer price incentives. The study then considers the import of these results for the retailer's media schedules.