Trusting Forecasts

Dilek Onkal, Sinan Gonul, Shari De Baets

Research output: Contribution to journalArticlepeer-review

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Abstract

Accurate forecasting is necessary to remain competitive in today’s business environment. Forecast support systems are designed to aid forecasters in achieving high accuracy. However, studies have shown that people are distrustful of automated forecasters. This has recently been dubbed ‘algorithm aversion’. In this study, we explore the relationship between trust and forecasts, and if trust can be boosted in order to achieve a higher acceptance rate of system forecasts and lessen the occurrence of damaging adjustments. In a survey with 134 executives, we ask them to rate the determinants of trust in forecasts, what trust in forecasting means to them and how trust in forecasts can be increased. The findings point to four main factors that play a role in trusting forecasts: (1) the forecast bundle, (2) forecaster competence, (3) combination of forecasts, and (4) knowledge. Implications of these factors for designing effective forecast support and future-focused management processes are discussed.
Original languageEnglish
Article numbere19
Number of pages10
JournalFutures & Foresight Science
Volume1
Issue number3-4
Early online date13 Jun 2019
DOIs
Publication statusPublished - 25 Oct 2019

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