
doi: 10.1002/ffo2.19
AbstractAccurate 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: (a) the forecast bundle, (b) forecaster competence, (c) combination of forecasts, and (d) knowledge. Implications of these factors for designing effective forecast support and future‐focused management processes are discussed.
N100, N200
N100, N200
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 13 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
