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. Potential Diffusion of Expert Systems in Forecasting J. Scott ArmstrongThe Wharton School, University of Pennsylvania Thomas Yokum Angelo State University AbstractWe 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.

Expert Systems for Forecasting

“…After all, how many of us would be agreeable to our organizations replacing us with an expert system? Armstrong and Yokum (2000) found that potential adopters perceived significant risks associated with the use of expert systems. On the other hand, they viewed expert systems positively with respect to their compatibility with their job, the ability to experiment with parts of the system, and ease of understanding.…”

Amplifying the learning effects via a Forecasting and Foresight Support System

“…Institutes have already begun conforming to change by incorporating novel methods of teaching to courses syllabi and making use of computers, when possible. In particular, expert systems have reasonable prospects for diffusion and proliferation (Armstrong and Yokum, 2001). However, they are not yet actively represented in traditional lectures of Business Forecasting and Management courses.…”