Knowledge management (KM) is a process that deals with the development, storage, retrieval, and dissemination of information and expertise within an organization to support and improve its business performance. Organizations are realizing that knowledge is a crucial resource for organizations and it should be managed judiciously. Organizations need to harness knowledge not only to stay competitive, but also to become innovative. KM requires a major shift in organizational culture and a commitment at all levels of a firm to make it work. Through a supportive organizational climate, ideally, through effective KM, an organization can bring its entire organizational learning and knowledge to bear on any problem, anywhere in the world, at anytime

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Purpose -Knowledge management (KM) has emerged as a strategy to improve organizational competitiveness. Our purpose is to identify the relationship between KM and the firm's competitive advantage. Design/methodology/approach -We review the existing literature on KM and strategy formulation. We utilize the resource-based view approach as a lens for fitting KM with strategic planning. The concept of strategy and KM integration is introduced. Findings -We relate KM strategies to business strategy through a thorough review of the literature. Research limitations/implications -Future research should investigate the circumstances under which KM can create a sustainable competitive advantage within the framework of the RBV. Another area involves the investigation of the degree of integration between the competitive strategy and KM strategy. Practical implications -A very useful source of information for practitioners to refine their thinking about KM and the firm strategic resources. It offers the practitioner a framework for understanding strategy concepts while illustrating how we can derive business results from KM. Originality/value -This paper offers practical help for corporate executives and practitioners embarking in a KM initiative or program.

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Members of brainstorming groups often pursue the same set of ideas rather than considering a wide and diverse range of ideas, which may reduce the number of ideas they produce. One way to reduce this cognitive inertia may be to encourage groups to engage in several simultaneous discussions or dialogues. This experiment, which studied groups brainstorming electronically, found that groups generated more ideas, more high-quality ideas, and more novel ideas when using multiple dialogues than when using single dialogues.

Classifying items into distinct groupings is fundamental in scientific inquiry. The objective of cluster analysis is to assign n objects to up to K mutually exclusive groups while minimizing some measure of dissimilarity among the items. Few mathematical programming approaches have been applied to these problems. Most clustering methods to date only consider lowering the amount of interaction between each observation and the group mean or median. Clustering used in information systems development to determine groupings of modules requires a model that will account for the total group interaction. We formulate a mixed‐integer programming model for optimal clustering based upon scaled distance measures to account for this total group interaction. We discuss an efficient, implicit enumeration algorithm along with some implementation issues, a method for computing tight bounds for each node in the solution tree, and a small example. A computational example problem, taken from the computer‐assisted process organization (CAPO) literature, is presented. Detailed computational results indicate that the method is effective for solving this type of cluster analysis problem.