Roadmapping in the Era of Uncertainty: How to Integrate Data-Driven Methods with Expert Insights

Sungjoo Lee (), Kook Jin Jang (), Myung Han Lee () and Seong Ryong Shin ()
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Sungjoo Lee: Ajou University
Kook Jin Jang: Hyundai Motor Company
Myung Han Lee: Hyundai Motor Company
Seong Ryong Shin: Hyundai Motor Company

Foresight and STI Governance (Foresight-Russia till No. 3/2015), 2021, vol. 15, issue 2, 39-51

Abstract: Roadmapping has long been regarded as a practical tool for supporting decision-making for science and technology innovation and it has received recent attention for its potential use in responses to uncertainty. Indeed, roadmapping enables forward-looking strategy making and thus helps to reduce uncertainty. Accordingly, numerous studies have been conducted to propose new approaches to roadmapping for a wide range of contexts, including the data-driven and expert-based approaches. Although these two main approaches have distinct advantages and disadvantages, few previous studies have focused on how to integrate them into roadmapping to better support decision-making related to science and technology innovation. To address this research gap, this study investigated how to integrate data-driven approaches with expert insights during roadmapping. For this purpose, a workshop-based roadmapping method was combined with data-driven methods to test this approach in the context of technology planning for the automobile industry. An ethnographic approach was used to collect data on when, where, and how data analysis must be conducted to support experts’ discussions. The research findings open a discussion regarding how to integrate data-driven methods with expert insights during roadmapping based on the trade-offs between the two types of data, that is, hard data for data-driven methods and soft data from expert insights, and suggest possible opportunities for future roadmapping developments.

Keywords: roadmapping; uncertainty; data-driven approach; expert insights; foresight (search for similar items in EconPapers)
JEL-codes: O21 O32 O33 (search for similar items in EconPapers)
Date: 2021
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