Rigidities of imagination in scenario planning: Strategic foresight through ‘Unlearning’

George Burt and Anup Karath Nair

Technological Forecasting and Social Change, 2020, vol. 153, issue C

Abstract: The emergence of strategic foresight from scenarios has constantly puzzled theorists. Whilst practitioners and scholars of scenario planning contend that scenarios generate strategic foresight by both stretching a manager's mental model by exposing them to a wide range of equally plausible futures, and triggering and accelerating processes of organisational learning, the true nature of this link between strategic foresight and organisational learning remains vague and undertheorised. Our paper tackles this puzzle by explicitly focussing on how strategic foresight emerges from the organisational learning process that unfolds during scenario planning. We undertook a 24-month long longitudinal study capturing both ‘actions’ and ‘reflections’ of a leading Scotch whisky manufacturer during their scenario planning exercises. Surprisingly, and perhaps counterintuitively, our findings unearth the role of ‘unlearning’ rather than ‘learning’ as a key mechanism that leads to the emergence of strategic foresight within the scenario planning process. Further reflection on the ‘unlearning process’ reveals that unlearning involves a ‘letting go’ or relaxing of deeply held assumptions and this in turn inadvertently leads to strategic foresight. Overall, by developing and introducing ‘unlearning’ as a key mechanism for the generation of strategic foresight, our paper aims to improve the effectiveness of scenario planning interventions as practiced.

Keywords: Strategic foresight; Scenarios; Organisational learning; Rigidities; Learning traps; Unlearning (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:153:y:2020:i:c:s0040162519310169

DOI: 10.1016/j.techfore.2020.119927

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