This study used a trait-state-occasion (TSO) model to isolate stable trait variance, occasion-specific state variance, and shared method related variance in a measure for leisure satisfaction in a Dutch nationally representative nine-year panel study. Findings indicate that satisfaction with leisure time is a consistently stronger indicator of overall leisure satisfaction than satisfaction with leisure activities. About half of the variance in leisure satisfaction is stable trait variance, with the remaining variance being mostly occasion-specific and to a lesser extent attributable to shared method variance and error. However, these findings depend on the age group we consider.Several socio-demographic variables relate directly to the trait aspect of leisure satisfaction. Our study underscores the importance of recognizing that over time leisure satisfaction measurements have considerable stable and more volatile elements and that one should control for shared method effects.
This study introduces a novel methodology for the post-analysis of operational predictability by leveraging timestamps collected through the Airport Collaborative Decision Making (A-CDM) framework. Focusing on the start-up and departure phases, the analysis highlights the importance of accurately planning and managing key timestamps, such as the Target Off-Block Time (TOBT) and Target Start-Up Approval Time (TSAT), which are critical for operational efficiency. Using one week of sample data from Schiphol Airport, this research demonstrates the potential benefits of the proposed framework in improving predictability during the start-up phase, particularly by identifying and analyzing outliers and anomalies. The start-up phase, a critical component of the outbound process, was broken down into subphases to allow for a more detailed assessment. The findings suggest that while 96% of flights maintain TOBT accuracy within ±20 minutes, 68% of flights miss their TOBT by 2 to 17.5 minutes, with 364 notable outliers. These deviations highlight areas for further investigation, with future work aiming to explore the impact of influencing factors such as weather, resource availability, and support tools. The proposed framework serves as a foundation for improving operational predictability and efficiency at airports.
