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

Abstract

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.

Judgmental Adjustments of Algorithmic Hotel Occupancy Forecasts: Does User Override Frequency Impact Accuracy at Different Time Horizons?

“…Multiple tourism studies explored judgmental forecasting in general, and the more particular question of subjective adjustments of algorithmic forecasts. Recently, Lin (2019) demonstrated that expert adjustments of forecast of tourist arrivals to Hong Kong proved more beneficial when the algorithmic predictions had large variability. Similarly, earlier studies by Song et al (2013), Lin (2013) and Lin et al (2014) provide support to the notion of increased in accuracy when time-series algorithmic predictions of tourists’ arrivals are adjusted by expert judgments through a Delphi survey.…”