T his paper puts forward arguments in favor of a pluralist approach to IS research. Rather than advocating a single paradigm, be it interpretive or positivist, or even a plurality of paradigms within the discipline as a whole, it suggests that research results will be richer and more reliable if different research methods, preferably from different (existing) paradigms, are routinely combined together. The paper is organized into three sections after the Introduction. In §2, the main arguments for the desirability of multimethod research are put forward, while §3 discusses its feasibility in theory and practice. §4 outlines two frameworks that are helpful in designing mixed-method research studies. These are illustrated with a critical evaluation of three examples of empirical research.

Scientometrics is the study of the quantitative aspects of the process of science as a communication system. It is centrally, but not only, concerned with the analysis of citations in the academic literature.In recent years it has come to play a major role in the measurement and evaluation of research performance. In this review we consider: the historical development of scientometrics, sources of citation data, citation metrics and the "laws" of scientometrics, normalisation, journal impact factors and other journal metrics, visualising and mapping science, evaluation and policy, and future developments.

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One approach to induction is to develop a decision tree from a set of examples. When used with noisy rather than deterministic data, the method involves three main stages-creating a complete tree able to classify all the examples, pruning this tree to give statistical reliability, and processing the pruned tree to improve understandability. This paper is concerned with the first stage-tree creationwhich relies on a measure for "goodness of split," that is, how well the attributes discriminate between classes. Some problems encountered at this stage are missing data and multi-valued attributes. The paper considers a number of different measures and experimentally examines their behavior in four domains. The results show that the choice of measure affects the size of a tree but not its accuracy, which remains the same even when attributes are selected randomly.

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