Forecasting diffusion of innovative technology at pre‐launch: A survey‐based method

Abstract

Purpose

The purpose of this study is to propose a systematic method for the diffusion of forecasting technology in the pre‐launch stage.

Design/methodology/approach

The authors designed survey question items that are familiar to interviewees as well as algebraically transformable into the parameters of a logistic diffusion model. In addition, they developed a procedure that reduces inconsistency in interviewee responses, removes outliers, and verifies conformability, in order to reduce the error and yield robust estimation results.

Findings

The results show that the authors' method performed better in the empirical cases of digital media broadcasting and internet protocol television in terms of sum of squared error compared with an existing survey‐based method, a regression method, and the guessing‐by‐analogy method. Specifically, the authors' method can reduce the error by using the conformability and outlier tests, while the consistency factor contributes to determining the final estimate with personal estimates.

Research limitations/implications

The procedure proposed in this study is confined to the presented logistic model. Future research should aim to extend its application to other representative diffusion models such as the Bass model and the Gompertz model.

Practical implications

The authors' method provides a better quality of forecasting for innovative new products and services compared with the guessing‐by‐analogy method, and it contributes to managerial decisions such as those in production planning.

Originality/value

The authors introduce the concepts of conformability and consistency in order to reduce the error from personal biases and mistakes. Based on these concepts, they develop a procedure to yield robust estimation results with less error.

Citation

Kim, T., Hong, J. and Koo, H. (2013), "Forecasting diffusion of innovative technology at pre‐launch: A survey‐based method", Industrial Management & Data Systems, Vol. 113 No. 6, pp. 800-816. https://doi.org/10.1108/IMDS-11-2012-0414