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PurposeThe purpose of this study is to propose a systematic method for the diffusion of forecasting technology in the pre‐launch stage.Design/methodology/approachThe 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.FindingsThe 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/implicationsThe 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 implicationsThe 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/valueThe 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.

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We address the power generation mix problem that considers not only nuclear and fossil fuels such as oil, coal and LNG but also renewable energy technologies. Unlike nuclear or other generation technologies, the expansion plan of renewable energy is highly uncertain because of its dependency on the government policy and uncertainty associated with technology improvements. To address this issue, we conduct a delphi survey and forecast the capacity of renewable energy. We further propose a stochastic mixed integer programming model that determines an optimal capacity expansion and the amount of power generation using each generation technology. Using the proposed model, we test eight generation mix scenarios and particularly evaluate how much the expansion of renewable energy contributes to the total costs for power generation in Korea. The evaluation results show that the use of renewable energy incurs additional costs. †