SELF-REPORTED BIG DATA FOR GOVERNANCE OF THE OCCUPATIONAL HEALTH AND SAFETY SYSTEM

Enterprises' dilemma to focus on legal compliance or safety has been widely presented in literature. This duality has also created or amplified a number of deficiencies in the inspection system. In this paper, it is argued that collection and exchange of big data on compliance and incidents between enterprises and the authorities is required to converge compliance and safety priorities, along with a necessary shift from compliance control to compliance management and from self-regulation to regulated self-reporting. Research presented in this paper aimed to develop the necessary means to support workplace risk governance in this direction. It started with the development of a conceptual model, linking legal measures to workplace risks. To apply this model, a framework was developed to identify and structure some thousands of risks and prevention measures in workplaces. This framework was codified in a web application to demonstrate its applicability and practicality. This application is designed as a standardized structured interface for self-reporting of compliance and incident data from enterprises to authorities, providing automatic feedback and support to compliance management. The proposed scheme could improve objectivity, transparency and equality, also supporting evidence-based policy making and research.