An examination of factors affecting accuracy in technology forecasts

Abstract

Private and public organizations use forecasts to inform a number of decisions, including decisions on product development, competition, and technology investments. We evaluated technological forecasts to determine how forecast methodology and eight other attributes influence accuracy. We also evaluated the degree of interpretation required to extract measurable data from forecasts. We found that, of the nine attributes, only methodology and time horizon had a statistically significant influence on accuracy. Forecasts using quantitative methods were more accurate than forecasts using qualitative methods, and forecasts predicting shorter time horizons were more accurate that those predicting longer time horizons. While quantitative methods produced the most accurate forecasts, expert sourcing methods produced the highest number of forecasts whose events had been realized, indicating that experts are best at predicting if an event will occur, while quantitative methods are best at predicting when. We also observed that forecasts are as likely to overestimate how long it will take for a predicted event to occur as they are to underestimate the time required for a prediction to come to pass. Additionally, forecasts about computers and autonomous or robotic technologies were more accurate than those about other technologies, an observation not explained by the data set. Finally, forecasts obtained from government documents required more interpretation than those derived from other sources, though they had similar success rates.