A multivariate Poisson mixture model for marketing applications

This paper describes a multivariate Poisson mixture model for clustering supermarket shoppers based on their purchase frequency in a set of product categories. The multivariate nature of the model accounts for cross-selling effects between the purchases made in different product categories. However, for computational reasons, most multivariate approaches limit the covariance structure by including just one common interaction term, or by not including any covariance at all. Although this reduces the number of parameters significantly, it is often too simplistic as typically multiple interactions exist on different levels. This paper proposes a theoretically more complete variance/covariance structure of the multivariate Poisson model, based on domain knowledge or preliminary statistical analysis of significant purchase interaction effects in the data. Consequently, the model does not contain more parameters than necessary, whilst still accounting for the existing covariance in the data. Practically, retail category managers can use the model to devise customized merchandising strategies.     

Executive succession: Organizational antecedents of ceo characteristics

Based on 195 succession events in Business Week 1000 firms, this study examines the organizational antecedents of CEO demographic characteristics. Study findings suggest that antecedent conditions of lower firm profits and firm growth are associated with the selection of outsider CEOs. Additionally, R&D intensity is associated with the selection of CEOs having technical functional backgrounds and higher levels of education.     

Effectiveness and Efficiency of Cross-Border Knowledge Transfer: An Empirical Examination

abstract    Based on a sample of 102 US organizations, this study examines the impact of knowledge characteristics, recipient learning intent, source attractiveness, and relationship quality on the effectiveness and efficiency of knowledge transfer from the international business affiliates of these organizations. Findings indicate that recipient learning intent and source attractiveness positively impact the effectiveness of knowledge transfer. In addition, recipient learning intent was found to have a positive effect on knowledge transfer efficiency. In particular, results highlight the strong positive impact that the quality of the relationship between the source and the recipient has on both the efficiency and effectiveness of cross-border knowledge transfer. The study also indicates that knowledge value is positively associated with recipient learning intent and that knowledge value, rarity and non-substitutability influence source attractiveness. Finally, findings suggest that the relationship between knowledge characteristics and knowledge transfer is partially mediated by recipient learning intent and source attractiveness.     

Regional Accounts of India: Methods,New Estimates,and Their Uses

For large economies with substantial regional variation, it is of great importance for policymakers and economic analysis that macro‐economic statistics are broken down by region. This paper reviews the regional accounts in India, discusses their role in Indian federal and state policies, and provides new estimates to cover major data gaps. Statistics on domestic product by Indian state, broken down by industry, are regularly published. But despite demands and recommendations by various commissions and policymakers, a comprehensive system of regional accounts is yet to be developed. New estimates for the period 1993–2010 are presented for saving and the macro‐economic expenditure by Indian states, like final consumption, capital formation, and trade balance. They show, for example, that some of the fastest growing Indian states have increased their saving and investment rates to 50 percent of their domestic product.     

Modeling social interactions: Identification, empirical methods and policy implications

Social interactions occur when agents in a network affect other agents’ choices directly, as opposed to via the intermediation of markets. The study of such interactions and the resultant outcomes has long been an area of interest across a wide variety of social sciences. With the advent of electronic media that facilitate and record such interactions, this interest has grown sharply in the business world as well. In this paper, we provide a brief summary of what is known so far, discuss the main challenges for researchers interested in this area, and provide a common vocabulary that will hopefully engender future (cross disciplinary) research. The paper considers the challenges of distinguishing actual causal social interactions from other phenomena that may lead to a false inference of causality. Further, we distinguish between two broadly defined types of social interactions that relate to how strongly interactions spread through a network. We also provide a very selective review of how insights from other disciplines can improve and inform modeling choices. Finally, we discuss how models of social interaction can be used to provide guidelines for marketing policy and conclude with thoughts on future research directions.