Journal Articles
Abstract
Tourism researchers have identified the impact of perceived risk on destination choice and travel behaviour, and differences in general traveller risk perceptions based on both traveller and trip characteristics. However, such research has neglected the travel of international university students, despite the expansion and economic importance of this market. This paper outlines an exploratory study conducted on international university students studying at an Australian university. A total of 407 valid responses from the sample were achieved. Factor analysis identified four main risk factors which were labelled ‘human-induced risk’, ‘social–psychological risk’, ‘financial risk’, and ‘health risk’. Student origins were found to influence risk perceptions. In particular, Asian students perceived higher levels of human-induced and social–psychological risks compared with students primarily from America and Europe. Travel experience and repeat visitation significantly reduced risk factors apart from health risks. Financial risks were higher for students planning to travel in Australia compared with Asia and America. The implications for destination marketing are considered, and future research avenues based on the results are outlined.
Citation
Deng, R., & Ritchie, B. W. (2018). International university students’ travel risk perceptions: An exploratory study. Current Issues in Tourism, 21(4), 455-476. https://doi.org/10.1080/13683500.2016.1142939
Abstract
Dynamic environmental circumstances can sometimes be incompatible with proactive human intentions of being safe, leading individuals to take unintended risks. Behaviour predictions, as performed in previous studies, are found to involve environmental circumstances as predictors, which might thereby result in biased safety conclusions about individuals’ inner intentions to engage in unsafe behaviours. This research calls attention to relatively less-understood worker intentions and provides a machine learning (ML) approach to help understand workers’ intentions to engage in unsafe behaviours based on the workers’ inner drives, i.e., personality. Personality is consistent across circumstances and allows insight into one’s intentions. To mathematically develop the approach, data on personality and behavioural intentions was collected from 268 workers. Five ML architectures—backpropagation neural network (BP-NN), decision tree, support vector machine, k-nearest neighbours, and multivariate linear regression—were used to capture the predictive relationship. The results showed that BP-NN outperformed other algorithms, yielding minimal prediction loss, and was determined to be the best approach. The approach can generate quantifiable predictions to understand the extent of workers’ inner intentions to engage in unsafe behaviours. Such knowledge is useful for understanding undesirable aspects in different workers in order to recommend suitable preventive strategies for workers with different needs.
Citation
Gao, Y., González, V. A., Yiu, T. W., Cabrera-Guerrero, G., & Deng, R. (2022). Predicting construction workers’ intentions to engage in unsafe behaviours using machine learning algorithms and taxonomy of personality. Buildings, 12(6), 841. https://doi.org/10.3390/buildings12060841