Knowledge Management Systems in Multinational Corporations: Typology and Transitional Dynamics

Knowledge Management Systems (KMSs) are becoming part of the agenda in many of today's leading firms. Through in-depth case studies of seven multinational companies (MNCs), examined over a period of four years, we identify four distinctive types of KMS - Fragmented, Content-based, Process-based and Capability-based - and their characteristics. These characteristics include both the structure, dimension and flow of the knowledge itself, and the staffing, training and reward systems in place to support the KMS. Relevant organisational factors influencing KMS design and implementation include the level of resource commitment by the headquarters, the structure of the firm, and the distribution of roles between headquarters and subsidiaries. Key external influences include the degree of environmental change and pressures to follow industry norms in technology investment. Our empirical evidence suggests that while some MNCs adopt the same type of KMS for relatively long periods of time, others transit between different KMS types. Moreover, transitions between types may follow either an increased resource commitment and augmented KMS sophistication, or a decreased resource commitment and reduced KMS complexity. Insights from the strategy-structure-process and change management literatures are utilised to examine these transitions between KMS types. Key lessons for MNC managers are provided in relation to both the initial adoption of a KMS and the handling of transitions between different systems.     

Rethinking Marketing Programs for Emerging Markets

This article points to a fundamental inconsistency in the emerging market strategies of multinational firms. On the one hand, they seek billions of new consumers in the emerging markets of China, India, Indonesia, and Latin America—on the other, their marketing programs are scarcely adapted for these markets. The result is low market penetration, disappointing market shares and poor profitability. These multinationals are trapped by their own devices in gilded cages, serving the affluent few but ignoring the potential of the billions of new consumers that attracted them in the first place. In this article we propose that, in order to attract billions of new consumers, the marketing programs of multinationals need to be rethought from the ground up. We identify three key factors that characterize emerging markets: (1) low incomes, (2) variability in consumers and infrastructure, and (3) the relative cheapness of labor, which is often substituted for capital. We draw on numerous case studies from around the world to illustrate how to incorporate these realities into marketing programs. We conclude with a discussion of the implications of such an approach for the multinational’s core strategic assumptions.     

Dramatically reducing cycle times through flash development

The reduction of cycle time has emerged as a fundamental element of new product development strategy. Many companies look solely at speed enhancement and increase the human, capital and technical resources allocated to development projects. However, additional resources do not guarantee significant reductions in cycle time. As well as speed enhancement activities, there exist two other sets of activities that are key to shortening the development cycle: those that act to prevent delays (friction) in the development process and those that define the scope of the final outcome (direction). This paper describes flash development as an alternative approach to reducing product development cycle times. Flash development works by creating a process that focuses on speed, friction and direction. The process is illustrated through an analysis of the Galileo Project at Nortel Networks.     

Bayesian semiparametric modeling and inference with mixtures of?symmetric distributions

We propose a semiparametric modeling approach for mixtures of symmetric distributions. The mixture model is built from a common symmetric density with different components arising through different location parameters. This structure ensures identifiability for mixture components, which is a key feature of the model as it allows applications to settings where primary interest is inference for the subpopulations comprising the mixture. We focus on the two-component mixture setting and develop a Bayesian model using parametric priors for the location parameters and for the mixture proportion, and a nonparametric prior probability model, based on Dirichlet process mixtures, for the random symmetric density. We present an approach to inference using Markov chain Monte Carlo posterior simulation. The performance of the model is studied with a simulation experiment and through analysis of a rainfall precipitation data set as well as with data on eruptions of the Old Faithful geyser.     

A modified false discovery rate multiple-comparisons procedure for discrete data, applied to human immunodeficiency virus genetics

Summary.  To help to design vaccines for acquired immune deficiency syndrome that protect broadly against many genetic variants of the human immunodeficiency virus, the mutation rates at 118 positions in HIV amino-acid sequences of subtype C versus those of subtype B were compared. The false discovery rate (FDR) multiple-comparisons procedure can be used to determine statistical significance. When the test statistics have discrete distributions, the FDR procedure can be made more powerful by a simple modification. The paper develops a modified FDR procedure for discrete data and applies it to the human immunodeficiency virus data. The new procedure detects 15 positions with significantly different mutation rates compared with 11 that are detected by the original FDR method. Simulations delineate conditions under which the modified FDR procedure confers large gains in power over the original technique. In general FDR adjustment methods can be improved for discrete data by incorporating the modification proposed.