The Effects of Casualisation on Mental Wellbeing and Risk Management in the Offshore Oil and Gas Industry

2023-05-15T08:30:40+08:00

This qualitative study was conducted with the aim of identifying psychosocial hazards in Australian offshore oil and gas facilities. Twenty-nine offshore oil and gas workers were interviewed via video link. Results indicated that, apart from the presence of a high-risk work environment as a source of mental and physical strain, there are organisational-specific stressors that cause workers' significant distress. Research results from NVivo analysis revealed that casualisation of the workforce was a major psychosocial hazard for offshore oil and gas workers, which resulted in feelings of insecurity, vulnerability and disconnection from work teams. In addition, a lack of stable income, an absence of opportunities to plan for the future and unsettled living arrangements worsen an already precarious existence. Findings show that a culture of blame and fear persists in some organisations, along with a lack of accountability and fear of making mistakes. The process of hiring, firing and rehiring was found to be a common practice by organisations in order to avoid their duty under the Fair Work Act amendments to offer casual conversion to their employees. Findings can be used to help inform organisational policies and assist in the development of risk control measures to minimise psychosocial hazards for offshore workers.

Designing an Efficient Restaurant Recommendation System Based on Customer Review Comments by Augmenting Hybrid Filtering Techniques

2023-06-15T08:45:43+08:00

Recommendation systems are being widely employed in order to provide users with a tailored set of services. They are primarily designed to generate advice or ideas (like restaurants, tourist places, medicines, movies, etc.) that address user concerns and can be efficiently utilized in a variety of industries. In today’s world, where we have a plethora of dining options available, choosing the right restaurant that matches our preferences can be a daunting task. To simplify this process and provide personalized recommendations, restaurant recommendation systems have emerged as a valuable tool. By leveraging the power of natural language processing (NLP), these systems can analyze textual data, such as user reviews and restaurant descriptions, to generate tailored suggestions for users. NLP is one of the machine learning techniques for intelligently and effectively analyzing, comprehending, and extracting meaning from human language. By utilizing techniques like sentiment analysis and named entity recognition, the system can understand user queries and match them with relevant restaurant attributes. It can consider factors such as cuisine type, price range, location, ambiance, and customer reviews to generate accurate and relevant recommendations. In the current study, the evaluation’s findings reveal that the suggested ExtraTreeRegressor algorithm outperforms other algorithms in terms of performance. The novelty of this research lies in the fact that here hybrid filtering is employed, which is not yet implemented in similar studies. The goal of this research article is to provide a more accurate and reachable list of suggested eateries. The results and conclusion show that the suggested approach produces good accuracy.

Universal Journal of Operations and Management

The Effects of Casualisation on Mental Wellbeing and Risk Management in the Offshore Oil and Gas Industry

Designing an Efficient Restaurant Recommendation System Based on Customer Review Comments by Augmenting Hybrid Filtering Techniques