Research Report—Principles for Examining Predictive Validity: The Case of Information Systems Spending Forecasts

Abstract

Research over two decades has advanced the knowledge of how to assess predictive validity. We believe this has value to information systems (IS) researchers. To demonstrate, we used a widely cited study of IS spending. In that study, price-adjusted diffusion models were proposed to explain and to forecast aggregate U.S. information systems spending. That study concluded that such models would produce more accurate forecasts than would simple linear trend extrapolation. However, one can argue that the validation procedure provided an advantage to the diffusion models. We reexamined the results using an alternative validation procedure based on three principles extracted from forecasting research: (1) use ex ante (out-of-sample) performance rather than the fit to the historical data, (2) use well-accepted models as a basis for comparison, and (3) use an adequate sample of forecasts. Validation using this alternative procedure did confirm the importance of the price-adjustment, but simple trend extrapolations were found to be more accurate than the price-adjusted diffusion models.