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  4. Evaluating Qualitative Predictions: A Novel Method Applied to the Automotive Powertrain Transition
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Evaluating Qualitative Predictions: A Novel Method Applied to the Automotive Powertrain Transition

Journal
Academy of Management Proceedings
Type
conference paper
Date Issued
2025
Author(s)
Noel Kretz  
;
J. Peter Murmann  
DOI
10.5465/AMPROC.2025.24324abstract
Abstract
In times of high uncertainty and low data, expert judgment is the preferred foresight approach to guide strategic decision-making. We have developed a method for the retrospective assessment of qualitative predictions made by experts. This method is applied to the automotive powertrain transition. With our novel method, we analyzed the historical expert judgment from 1970 to 2009 to explore how accurate and consensual the experts have predicted the relative potential of alternative powertrain across this period. We collected 89 qualitative expert predictions and transformed them into quantitative probabilistic forecasts to measure their accuracy and the wisdom of the expert “crowd”. To compare the accuracy of expert judgment with those of top managers, we coded 85 press releases of a leading car company related to alternative powertrains. The novel method shows that experts predicted significantly better than random guessing but the car company did not. Interestingly, we can show that a consensus forecast already emerged in the mid-1990s that battery electric powertrains would dominate fuel-cell powertrains. The leading car company did not see this for at least another 13 years.
Language
English (United States)
Keywords
Foresight
crowd wisdom
expert judgment
case study
automotive
HSG Classification
not classified
Volume
2025
Number
1
Event Title
Academy of Management Meeting, Wharton Technology and Innovation Conference
Official URL
https://journals.aom.org/doi/10.5465/AMPROC.2025.24324abstract
URL
https://www.alexandria.unisg.ch/handle/20.500.14171/124175
Subject(s)

business studies

Division(s)

IFB - Institute of Ma...

File(s)
Thumbnail Image
Name

AOM 2025 Submission - FINAL.pdf

Type

Main Article

Size

531.58 KB

Format

Adobe PDF

Checksum (MD5)

79089c0fdd4c82e675a17bc7b3afea91

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