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ABSTRACTSystem development efforts depend to a large degree upon how well information systems (IS) managers, IS specialists, and IS users work together in a project team structure. Yet, these individuals frequently work under different perceptions about matters of importance to development, management, and success. This paper introduces a framework for examining IS specialists' skill requirements from a multiple‐stakeholder perspective. Derived from discrepancy theory, the framework concedes that different stakeholders hold a variable set of expectations for IS personnel skill levels as well as a perception of skills held by IS personnel. We examine differences in expectation and performance expressed by each group and describe the impact of the discrepancy on user satisfaction, career satisfaction of IS specialists, and on job performance evaluations by IS managers. Results confirm that a discrepancy between an IS specialist's expectations of skill and their perceived skill self‐proficiency impacts career satisfaction. Similar relations hold for IS managers and users. Since different stakeholders may hold different perceptions, satisfaction of all parties becomes problematic unless a common frame of reference can be determined.

ABSTRACTAnalyzing scanner data in brand management activities presents unique difficulties due to the vast quantity of the data. Time series methods that are able to handle the volume effectively often are inappropriate due to the violation of many statistical assumptions in the data characteristics. We examine scanner data sets for three brand categories and examine properties associated with many time series forecasting methods. Many violations are found with respect to linearity, normality, autocorrelation, and heteroscedasticity. With this in mind we compare the forecasting ability of neural networks that require no assumptions to two of the more robust time series techniques. Neural networks provide similar forecasts to Bayesian vector autoregression (BVAR), and both outperform generalized autoregressive conditional herteroscedasticty (GARCH) models.