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Questionnaires are a widely used research method in human resource management (HRM), and multi‐item psychometric scales are the most widely used measures in questionnaires. These scales each have multiple items to measure a construct in a reliable and valid manner. However, using this method effectively involves complex procedures that are frequently misunderstood or unknown. Although there are existing methodological texts addressing this topic, few are exhaustive and they often omit essential practical information. The current article therefore aims to provide a detailed and comprehensive guide to the use of multi‐item psychometric scales for HRM research and practice, including their structure, development, use, administration, and data preparation. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have emerged as a state‐of‐the‐art tool in very different research fields due to its adaptive power to the decision space since they do not presuppose any linear relationship between data. Some of the main disadvantages of these trending models are that the choice of the network underlying architecture profoundly influences the performance of the model and that the architecture design requires prior knowledge of the field of study. The use of questionnaires is hugely extended in social/behavioral sciences. The main contribution of this work is to automate the process of a DNN architecture design by using an agglomerative hierarchical algorithm that mimics the conceptual structure of such surveys. Although the train had regression purposes, it is easily convertible to deal with classification tasks. Our proposed methodology will be tested with a database containing socio‐demographic data and the responses to five psychometric Likert scales related to the prediction of happiness. These scales have been already used to design a DNN architecture based on the subdimension of the scales. We show that our new network configurations outperform the previous existing DNN architectures. [ABSTRACT FROM AUTHOR]

In Australia, the transportation sector is the third largest contributor of CO-e emissions. In order for Australia to meet the ratified target of 5% below 2000 emissions levels by 2020, a number of changes must be put in place. Sustainable transport design allows for more carbon-friendly travelling alternatives in urban and suburban areas where transportation activities mostly occur. In order for the design to be successfully accepted by the community, a good understanding of people's travel behaviour and preferences of transportation mode is required. This paper offers a review of current transportation research in the context of Australia that spans a number of disciplines including economics, engineering and psychology. The objectives are to assess the key factors that have been empirically shown to affect individual transportation mode choice, identify the shortcoming and gaps in the literature, and recommend how this research can be used to aid in future carbon reduction through better understanding of people's travelling behaviour. [ABSTRACT FROM AUTHOR]