Relationship between personality trait and multi-national construction workers safety performance in Saudi Arabia

Given the large economic and social costs of work-related accidents and injuries, it is not surprising that organisations strive to reduce them; this creates a need to improve the safety performance of the whole construction industry. Health and Safety statistics in general appear to suggest a levelling off of safety performance across the construction industry as a whole and this implies that improving safety beyond the current level of attainment calls for a radical look at how safety is addressed by the industry. Such a radical approach needs to explore alternatives to current practices in safety improvement. Although it is acknowledged that human factors are involved in 80-90% of work-related accidents and incidents, the focus of safety research in recent years still addresses only organisational and environmental factors, rather than variables at the level of the individual. Occupational personality models suggest that the ability to understand, predict and control incidents could minimize their potential transition into accidents. The safety behaviour of the individual worker forms part of such occupational personality modelling. Understanding the safety behaviour of construction workers should provide opportunities for improvement beyond traditional practices in the quest to improve safety management. The study on which this thesis is based aimed to develop a conceptual framework for improving safety performance on sites. This was achieved by exploring, on the one hand, the relationship between the personality traits of individual workers and their safety behaviour (safety participation, safety compliance and safety motivation), and incident rates on the other. The data for the analysis was drawn from multi-cultural construction workers in Saudi Arabia. The emergence of the Big Five personality model has been widely accepted as a valid and reasonably generalisable taxonomy for personality structure and has been used by numerous researchers as a framework to explore the criterion-related validity of personality in relation to job performance. This study employed the Big Five categorisation of traits to explore the relationship between fundamental dimensions of personality and potential for involvement in accidents and incidents. The principal findings from the study showed a very good level of acceptance by practitioners in Saudi Arabia for the conceptual framework developed for managing safety behaviour. The study also established that some personality traits moderated the effects of safety behaviour for incident rates. In addition, the analysis revealed that individual workers characterised by conscientiousness and openness are least likely to experience incidents, and consequently, accidents and injuries at work. However, individuals characterised by high extraversion, neuroticism and low agreeableness are more likely to be v involved in incidents, and potentially, accidents and injuries. These important findings have significant ramifications for the way safety development and training for construction workers should be addressed in the future. Recommendations from the study culminated in the development of a conceptual framework for improving safety performance which aimed to minimize incidents attributable to the worker. The framework relies on the attitudes and behaviours of employees in proposing mitigation strategies for the construction industry

Encoding the Enforcement of Safety Standards into Smart Robots to Harness Their Computing Sophistication and Collaborative Potential:A Legal Risk Assessment for European Union Policymakers

Until robots and humans mostly worked in fast-paced and yet separate environments, occupational health and safety (OHS) rules could address workers’ safety largely independently from robotic conduct. This is no longer the case: collaborative robots (cobots) working alongside humans warrant the design of policies ensuring the safety of both humans and robots at once, within shared spaces and upon delivery of cooperative workflows. Within the European Union (EU), the applicable regulatory framework stands at the intersection between international industry standards and legislation at the EU as well as Member State level. Not only do current standards and laws fail to satisfactorily attend to the physical and mental health challenges prompted by human–robot interaction (HRI), but they exhibit important gaps in relation to smart cobots (“SmaCobs”) more specifically. In fact, SmaCobs combine the black-box unforeseeability afforded by machine learning with more general HRI-associated risks, towards increasingly complex, mobile and interconnected operational interfaces and production chains. Against this backdrop, based on productivity and health motivations, we urge the encoding of the enforcement of OHS policies directly into SmaCobs. First, SmaCobs could harness the sophistication of quantum computing to adapt a tangled normative architecture in a responsive manner to the contingent needs of each situation. Second, entrusting them with OHS enforcement vis-à-vis both themselves and humans may paradoxically prove safer as well as more cost-effective than for humans to do so. This scenario raises profound legal, ethical and somewhat philosophical concerns around SmaCobs’ legal personality, the apportionment of liability and algorithmic explainability. The first systematic proposal to tackle such questions is henceforth formulated. For the EU, we propose that this is achieved through a new binding OHS Regulation aimed at the SmaCobs age.<br/

Big Data as a Technology of Power

The growing importance of big data in contemporary society raises significant and urgent ethical questions. In the academic literature and in the media, the dominant response to many of these ethical questions is to re-examine the role and importance of privacy protections, but I argue that it is far more fruitful to investigate the relationship between power and big data. As algorithmic processes are increasingly used in decision-making processes, it is crucial that we understand the ways in which big data can be used as a technology of power. Only then can we properly understand the ways in which the use of big data impacts on and reorganises society, and go on to develop effective, tailored protections for individuals against harm from the use of big data. First, I show that the rise of big data highlights the limits of privacy protections, as big data-based analytics allow for personal information to be inferred in ways that circumvent privacy protections and problematises the category of personal information. In order to properly protect people from the potential harms that can arise from the use of big data in decision making, I argue that we must also examine the relationship between big data and power. In this thesis, I will present an argument for a pluralistic understanding of power, and a lens through which we can identify the kinds of power being exercised in the contexts we are investigating. Power is best understood as an umbrella term that refers to a diverse range of phenomena across an equally diverse range of domains or contexts. We can use this attitude to examine the central features of an exercise of power to identify the relevant theoretical accounts of power to draw on in understanding the modes of power present in a context. In Chapter 4, I will demonstrate the value of this approach by using it to analyse four contexts where big data is used as a technology of power, showing that we cannot use a single theoretical understanding of power across all exercises of power. Following this, I examine the impacts of big data on the operation of power. While many in the literature see big data as necessitating the development of new theoretical understandings of power, I argue that there are important historical continuities in power. Big data can be picked up and used as part of existing kinds of power just as any new technology can, and while this may change the efficiency, range, and effectiveness of exercises of power, it does not change their fundamental nature. However, there are impacts on the operation of power that are unique to big data, and one of these impacts I consider here is that the inferential capabilities of big data shift power from acting on human subjects and towards acting on data doubles (fragmentary digital representations of people). This leads to significant ethical problems with ensuring that power is exercised accountably. Finally, I will demonstrate these problems in Chapter 7 through examining four more contexts in which big data is used as a technology of power, showing how the shift to the data double as the subject of power undermines the effectiveness of accountability as a check on the abuse of power