Team diversity and categorization salience : capturing diversity-blind, intergroup biased, and multicultural perceptions

It is increasingly recognized that team diversity with respect to various social categories (e.g., gender, race) does not automatically result in the cognitive activation of these categories (i.e., categorization salience), and that factors influencing this relationship are important for the effects of diversity. Thus, it is a methodological problem that no measurement technique is available to measure categorization salience in a way that efficiently applies to multiple dimensions of diversity in multiple combinations. Based on insights from artificial intelligence research, we propose a technique to capture the salience of different social categorizations in teams that does not prime the salience of these categories. We illustrate the importance of such measurement by showing how it may be used to distinguish among diversity-blind responses (low categorization salience), multicultural responses (positive responses to categorization salience), and intergroup biased responses (negative responses to categorization salience) in a study of gender and race diversity and the gender by race faultline in 38 manufacturing teams comprising 239 members

Knowledge discovery in databases of biomechanical variables: application to the sit to stand motor task

BACKGROUND: The interpretation of data obtained in a movement analysis laboratory is a crucial issue in clinical contexts. Collection of such data in large databases might encourage the use of modern techniques of data mining to discover additional knowledge with automated methods. In order to maximise the size of the database, simple and low-cost experimental set-ups are preferable. The aim of this study was to extract knowledge inherent in the sit-to-stand task as performed by healthy adults, by searching relationships among measured and estimated biomechanical quantities. An automated method was applied to a large amount of data stored in a database. The sit-to-stand motor task was already shown to be adequate for determining the level of individual motor ability. METHODS: The technique of search for association rules was chosen to discover patterns as part of a Knowledge Discovery in Databases (KDD) process applied to a sit-to-stand motor task observed with a simple experimental set-up and analysed by means of a minimum measured input model. Selected parameters and variables of a database containing data from 110 healthy adults, of both genders and of a large range of age, performing the task were considered in the analysis. RESULTS: A set of rules and definitions were found characterising the patterns shared by the investigated subjects. Time events of the task turned out to be highly interdependent at least in their average values, showing a high level of repeatability of the timing of the performance of the task. CONCLUSIONS: The distinctive patterns of the sit-to-stand task found in this study, associated to those that could be found in similar studies focusing on subjects with pathologies, could be used as a reference for the functional evaluation of specific subjects performing the sit-to-stand motor task

Network-Based Marketing: Identifying Likely Adopters Via Consumer Networks

Network-based marketing refers to a collection of marketing techniques that take advantage of links between consumers to increase sales. We concentrate on the consumer networks formed using direct interactions (e.g., communications) between consumers. We survey the diverse literature on such marketing with an emphasis on the statistical methods used and the data to which these methods have been applied. We also provide a discussion of challenges and opportunities for this burgeoning research topic. Our survey highlights a gap in the literature. Because of inadequate data, prior studies have not been able to provide direct, statistical support for the hypothesis that network linkage can directly affect product/service adoption. Using a new data set that represents the adoption of a new telecommunications service, we show very strong support for the hypothesis. Specifically, we show three main results: (1) “Network neighbors”—those consumers linked to a prior customer—adopt the service at a rate 3–5 times greater than baseline groups selected by the best practices of the firm’s marketing team. In addition, analyzing the network allows the firm to acquire new customers who otherwise would have fallen through the cracks, because they would not have been identified based on traditional attributes. (2) Statistical models, built with a very large amount of geographic, demographic and prior purchase data, are significantly and substantially improved by including network information. (3) More detailed network information allows the ranking of the network neighbors so as to permit the selection of small sets of individuals with very high probabilities of adoption