Purpose This study aims to explore the differences in the travel behaviour of Indonesian youth of Generations Y and Z in the pre-, during and post-travel stages and their associated use of information and communication technology. Design/methodology/approach Data were gathered through a questionnaire that was distributed via the internet for six weeks; 569 people provided their full responses. Chi-square tests and linear regression were used for data analysis. Findings These generations use digital media and word of mouth differently when searching for travel information. The differences are also apparent in the pre-, during and post-travel stages. Generation Z tends to use digital media and share travel experiences through a certain social media platform more frequently than Generation Y. Research limitations/implications This study covers the travel history prior to and during the COVID-19 pandemic and equalises the situation in these two periods. The number of samples was relatively small to capture the current population of both generations. Practical implications This study promotes a new understanding of the travel behaviours of the two generations based on the stages of the travel examined. The findings suggest that the travel industry can distinguish between promotional media and types of services to serve each of the generational cohorts more effectively. Originality/value To the best of the authors’ knowledge, this is the first study to reveal differences in travel behaviour between Generations Y and Z in Indonesia.

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Uncontrolled environments have often required face recognition systems to identify faces appearing in poses that are different from those of the enrolled samples. To address this problem, probabilistic latent variable models have been used to perform face recognition across poses. Although these models have demonstrated outstanding performance, it is not clear whether richer parameters always lead to performance improvement. This work investigates this issue by comparing performance of three probabilistic latent variable models, namely PLDA, TFA, and TPLDA, as well as the fusion of these classifiers on collections of video data. Experiments on the VidTIMIT+UMIST and the FERET datasets have shown that fusion of multiple classifiers improves face recognition across poses, given that the individual classifiers have similar performance. This proves that different probabilistic latent variable models learn statistical properties of the data that are complementary (not redundant). Furthermore, fusion across multiple images has also been shown to produce better perfomance than recogition using single still image.