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This paper aims to use the five-factor model’s (FFM: emotional instability, introversion, openness to experience, agreeableness and conscientiousness) personality traits and the need for arousal to explain millennials’ habitual and addictive smartphone use and resultant materialistic inclinations. The study also test the mediating role of addictive use in the relationship between habitual use and materialism.

Participants’ self-reported data (n = 705) from a sample of millennials were gathered using a cross-sectional survey approach conducted in Malaysia and studied using structural equation modelling with partial least squares (PLS-SEM).

The results discover that emotional instability, openness to experience, agreeableness and need for arousal have a significant influence on habitual smartphone use. Conversely, introversion and conscientiousness have no significant impact on habitual use. Fascinatingly, millennials’ habitual use positively influences their materialism. Furthermore, addictive smartphone use positively affects materialism and mediates the relationship between habitual use and materialism.

The FFM, a prominent personality trait model, has been used in numerous studies to predict usage intention. However, the particular dimension of the FFM personality traits that drive habitual and addictive smartphone use to trigger materialistic tendencies among millennials needs to be exposed in an emerging market context. The results emphasise the need to consider this demographic’s personalities when attempting to comprehend how habitual use and materialism occur. This study also provides practitioners with helpful information in creating targeted interventions to encourage healthy smartphone use behaviours and reduce possible adverse effects related to addictive smartphone use and materialistic attitudes.

Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.

To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.

This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.

This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.