Forecasting diffusion of innovative technology at pre‐launch

Abstract

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.

Developing an Internationalization Strategy Using Diffusion Modeling: The Case of Greater Amberjack

“…The current effort used ex ante estimation instead. This approach extends the work of Kim, Hong, and Koo, who used expert data to estimate P and Q values [15]. Although experts may be able to predict the customer reception of a new product, it is clear that involving consumers and confronting them with the new product in a "real-life", competitive store setting (with familiar alternatives present, marketing mechanisms deployed (i.e., price discounts), and information available) increase the external validity of results.…”

“…To estimate a mixed influence diffusion model, information about P (the coefficient of innovation) and Q (internal influence or contagion factor) is required. In contrast to some research that has used survey data involving expert interviews [15], the present work makes use of data from an online consumer experiment involving greater amberjack fillets that was part of the EU-funded "DIVERSIFY" project. The experiment investigated the probability for the adoption of high added-value farmed greater amberjack products (i.e., fillets) and mimicked as closely as possible a real-life, competitive choice setting of a national retailer's online store.…”

“…Forecasting market diffusion is an important tool when planning to address market internationalization [15]. It can help a firm understand and plan for the right production capacity at the right time.…”

Understanding Transferable Supply Chain Lessons and Practices to a “High‐Tech” Industry Using Guidelines from a Primary Sector Industry: A Case Study in the Food Industry Supply Chain

“…The analogy methodology has been widely used in case studies ranging from creating patents on new technologies [ 24 ] or used to determine the scale of R & D investments for renewable energy [ 25 ] and model predictive control strategies in supply chain management [ 26 ]. In the absence of enough data, guess-by-analogy has been considered prevalent and effective [ 27 ].…”

Pre-launch forecasting of a pharmaceutical drug

“…The main drawback of this approach is that it requires initial pre-launch forecasts. Subjective methods involve management judgments and specific product information that have to be converted into mathematical parameter estimates of a possible future diffusion process (Kim et al, 2013). This methodology is not appropriate when reliable judgments are not available.…”