Browsing by Author "McDonald BW"

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    Gender-perceived workplace stressors by New Zealand construction professionals
    (Taylor and Francis Group, 2024-02-19) van Heerden A; Boulic M; McDonald BW; Chawynski G
    The construction workplace is male-dominated and stressful, but little is known about gender-based differences in its stressors. This research examined the effect of gender and personal attributes on stressors in the New Zealand construction industry at four major levels: (1) individual, (2) group, (3) organizational, and (4) extra-organizational. Target respondents were professional construction members from Site Safe New Zealand, with 317 completed questionnaires and statistical analysis using the two-sample t-test, Kruskal-Wallis test, and Levene’s test. The findings show that females have higher qualifications than males, but males have about ten years more experience and more completed projects than their female counterparts. Males reported significantly higher technical skills than females and there was no significant difference between genders regarding sector involvement. At the individual level, females were most affected by role conflict stress and the perception of different treatment because of gender. Males felt significantly higher stress over the variable 'on/off-site office/administration building conditions’. At the group level, there were no significant gender differences, but sexual harassment warranted further investigation. Within the organizational and extra-organizational levels, no variables differed significantly between genders. The construction workforce has a strong gender imbalance and efforts are needed to address this through better work-life balance.
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    Who is most likely to offend in my store now? Statistical steps towards retail crime prevention with Auror
    (Australian Mathematical Society, 14/08/2017) McDonald BW; Hall L; Zhang XP
    Auror is establishing itself both locally and internationally as a leader in retail crime solutions. In mid-2015 a study group of mathematicians and statisticians teamed up with Auror to analyse data from the first two and a half years of their venture to identify and prevent retail theft. The aim was to explore methods for nominating the top ten individuals most likely to offend in a particular store at a particular time. Various methods were employed to explore the relationships between retail crime incidents, including generalised linear models, regression trees and similarity matrices. The relationships identified were then used to inform predictions on individuals most likely to reoffend. The focus of the current analysis is to model the behaviour of reoffenders. At the time of the study group the project was still in the early phases of data collection. As data collection proceeds, prediction methods will likely give better and better intelligence to aid crime prevention efforts.