Potential Diffusion of Expert Systems in Forecasting

Abstract

We drew upon findings from the diffusion literature to assess the prospects for the diffusion of expert systems in forecasting. Forecasters judged potential adoption of expert systems in relation to two techniques that had been widely adopted in the past, Box-Jenkins and scenarios. They also rated each technique on seven innovation characteristics: relative advantage, compatibility, divisibility, communicability, complexity, product risks, and psychological risks. The respondents were classified by four forecaster roles: researcher, educator, practitioner, and decision maker. In general, the expected probabilities of adoption for expert systems were slightly higher than for the two other techniques. Additionally, the respondents rated expert systems nearly equivalent to Box-Jenkins and scenarios on relative advantage and communicability. In relating the probabilities of adoption to the characteristic ratings, the groups perceived significant negative psychological and product risks with expert systems. However the experts, especially practitioners and decision makers, rated expert systems positive on compatibility, divisibility, and communicability, so it may be desirable to ensure that these positive traits are stressed with potential adopters, especially researchers and educators.

Keywords: Forecasting, diffusions, expert forecasting, forecaster, forecast system