Effects of personality on the efficacy of judgmental adjustments of statistical forecasts

PurposeThe purpose of this paper is to investigate the effects of personality dimensions (conscientiousness, neuroticism, extraversion, agreeableness, openness to experience, locus of control) on the efficacy of judgmental adjustments of statistical forecasts.Design/methodology/approachThis paper uses a two-level hierarchical linear model to analyze a large data set obtained from an organizational setting (a quick service restaurant chain) that includes 3,812 judgmental adjustments of sales forecasts made by 112 store managers.FindingsThe results indicate that the average forecast accuracy improves as a result of judgmental adjustments, but performance of individual forecasters varies considerably based on their personality. Specifically, the trait of openness to experience tends to improve forecast accuracy while extraversion and external locus of control have negative effects.Originality/valueIntegration of human judgment with analytics algorithms is a major challenge for organizations. Documenting the impact of these traits on forecast accuracy opens the door for forecasting support system design, training, personnel selection and correction strategies that can be applied to judgmental adjustments.

Judgmental adjustments in tourism forecasting practice: How good are they?

This study aims to evaluate the accuracy of different judgmental forecasting tasks, compare the judgmental forecasting behaviour of tourism researchers and practitioners and explore the validity of experts’ judgmental behaviour by using the Hong Kong visitor arrivals forecasts over the period 2011Q2−2015Q4. Delphi-based judgmental forecasting procedure was employed through the Hong Kong Tourism Demand Forecasting System, an online forecasting support system, to collect and combine experts’ adjusted forecasts. This study evaluates forecasting performance and explores the characteristics of judgmental adjustment behaviour through the use of a group of error measures and statistical tests. The findings suggest a positive correlation between forecast accuracy and the level of data variability, and that experts’ adjustments are more beneficial in terms of achieving higher accuracy for series with higher variability. Industry practitioners’ forecasts outperformed academic researchers, particularly in making short-term forecasts. However, no significant difference was found between the two panels in making directionally correct forecasts. Experts’ judgmental intervention was found most useful for those series most in need of adjustment. The size of adjustment was found to have a strong and significantly positive association with the direction of forecast adjustment, but no statistically significant evidence was found regarding the relationship between accuracy improvement and adjustment size.

A New Approach to Forecasting Container Throughput of Guangzhou Port with Domain Knowledge

Although judgmental models are widely applied in practice to alleviate the limitation of statistical models ignoring domain knowledge, they are still suffering from many kinds of biases and inconsistencies inherent in subjective judgments. Moreover, most of the prior studies are often concentrated on making judgmental adjustments to statistical projections and ignore incorporating domain knowledge in other forecasting steps. This paper proposes a framework under which domain knowledge are integrated with the whole forecasting process and a new forecasting method is developed. The new method is applied to forecasting the container throughput of Guangzhou Port, one of the most important ports of China. In order to test the effectiveness of the new method, the authors compare its performance with that of the ARIMAX model. The results show that the new method significantly outperforms the ARIMAX model.