Research on forecasting 355

finding that there were "significant differences across the 15 firms with respect to both error and confidence (p. 261)" suggests the possibility that results might be sensitive to the choice of mea- sures.

This study adds to a mounting body of evi- dence (some of which is summarized in Davis et ai.'s discussion) that suggests that caution must be employed in determining what information should be provided to analysts and decision makers.

associate editor, Nigel Meade that has not proved possible. Since the issues raised are fundamental to the importance of forecasting as perhaps the primary criterion for evaluating alternative model specifications, readers will hopefully be prompted to review the original articles in order to establish the rights and wrongs of this controversy.

Modelling or Forecasting Information Systems Spending

Reference

Armstrong, J.S. and F. Collopy, 1992, Error measures for generalizing about forecasting methods: Empirical com- parisons, International Journal of Forecasting, 8, 69-80.

- Fred Collopy

[Fred D. Davis, Carlson School of Management, University of Minnesota, 271 19th Avenue South, Minneapolis, MN 55455]

SSD! 0169-2070(95)00596-X

Gurbaxani, V. and H. Mendelson, 1990, An integrative model of information systems spend- ing growth, Information Systems Research, 1, 23-46. Collopy, F., M. Adya and J.S. Armstrong, 1994a, Principles for examining predictive va- lidity: the case of information systems spending forecasts, Information Systems Research, 5, 170- 179. Gurbaxani, V. and H. Mendelson, 1994, Model- ing vs. forecasting: the case of information systems spending, Information Systems Research, 5, 180-190.

The authors of the articles abstracted in 'Re- search on Forecasting' are invited to comment on the reviews. Wherever possible their com- ments will be incorporated into a mutually ac- ceptable text. With the review that follows by

Information systems spending is a significant part of most developed economies. According to Gurbaxani and Mendelson (1990), in future to be known as GM1, information systems spending can take up to 4% of total revenue in infor- mation system dependent sectors such as bank- ing and finance. Modelling and forecasting this spending is a legitimate research topic with obvious practical implications. The work pub- lished in this area so far has raised a number of issues; these include the relationship between modelling and forecasting, methods of model validation and the appropriate conditions for the use of diffusion models. This report will discuss a series of three papers by two sets of authors both with strongly held, but different, beliefs.

GM1 propose an "integrative price-adjusted S curve growth model' to describe the growth of information systems spending in the United States. This series used as the basic data in all the analyses describes spending which starts from virtually nothing in 1960 to $160 billion in 1987. The data is plotted in 1972 $ in Fig. 1. GM1 combine two hypotheses, one is that spending is price dependent, the other is that spending will be dominated by a growth process encapsulated by an S curve such as the logistic, the Gompertz or modified exponential.

Their price adjusted Gompertz model is

log(B,) = log(K) + At + b t log(A)

where B t is information systems spending, K is the saturation level, A and b are parameters of the Gompertz curve and A is a price adjustment parameter. The price adjusted diffusion models