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The mining industry has remained Zambia's dominant industry for almost a century. According to the report by International Council for Mines and Minerals (ICMM) for 2013, Zambia is highly dependent on copper mining as the core productive industry. Mining contributes to direct employment (approximately at 1.7%), foreign direct investment (approximately at 86%), gross domestic product (more than 12%) and government revenue (more than 25%). Regardless of these economical enactments, the accident frequency across the mines is very significant. In general, the mining industry is perceived to be a high-risk industry. The increase in the number of mining accidents is extremely costly, whether measured in terms of medical expenses and disability compensation, loss of production and wages or damage to plant and equipment. The human cost, in terms of death and suffering, is beyond calculation. In recent years, there has been some innovations in terms of technology regarding mining methods, and this has resulted in decreased accident occurrence in the mines. The human factors involved in the mine accidents need to be addressed further to reduce these rates. Therefore, the best approach is first to understand mine accident causality, and then this will be a foremost step in a pursuit to diminish the high rate of accidents. Effective remedies and measures can be designed if only accident process is properly understood. The understanding and interpretation of causes of accidents at workplaces can only be achieved by accident modelling techniques. The effective way of analysing industrial accidents has been proven by the Swiss Cheese Model, which is also applicable to this study. The Swiss Cheese Model describes an accident as an event which happen within organization due to the combination of different unsafe acts which may include latent conditions and front-line operators. The purpose of this study was to determine how systemic factors contribute to accidents at a copper mining company in Zambia. The analysed results were compared with those of other local mines as well as mines from developed and developing countries. The approach in this study involves using the existing framework developed by Bonsu (2013). The framework had used the concepts from the Mark III of the Swiss Cheese Model, Incident Cause Analysis, safety management principles and the Nertney Wheel. The sections involved in the existing framework of Bonsu (2013) are metadata, accident barrier analysis and causal analysis. The accident causality section is designed and described in the same way as the Mark III version of the SCM. This section is used for analysis of accident causality and is categorized into proximal, work place and systemic factors. The metadata section offers explanations on different factors that influence the happening of accidents at this copper mining company in Zambia. Metadata section captures the information on accidents analysed under the barriers and causing agency section of the framework. The variables under the metadata are time and date of accident, place of the accident, accident type, activity involved which resulted in the accidents, task schedule of the accidents, age of the victim, experience of the victim, job status, etc. The last section of the existing framework is the agency and barrier analysis and was designed by Bonsu (2013) to capture data on the safety barriers which were breached and accident causing agents in the accident report. The accident reports collected from the copper mining company in Zambia were used in the existing framework and the analysed results were presented as unsafe acts, workplace and systemic factors with linkages to each other. The most prominent type of unsafe acts recognized were routine violation (recognized in 38% of all the accident analysed), closely followed by slips and lapses (identified in 30%) and then mistakes (21%). Exceptional violation and non-human cause were the lowest at 9% and 2% respectively. Systemic and workplace factors were involved in 78.2% of the accident reports that were analysed. The most prominent workplace factor recognized was behavioural environment (25.8% of all cases analysed), closely followed by physical environment (23.4% of all cases analysed), then unsafe work practices (18.8% of the accidents analysed), then fit-for purpose equipment (16.4% of the accidents analysed) and finally competent people (15.6% of the accidents analysed). In general, under the category of accident analysis on workplace factors, all the five factors were significantly contributing to the causes of accidents at the mine site that was investigated as demonstrated by the closeness in percentages. In the case of systemic factors, inadequate supervision or leadership was the most prominent factor identified (22.6% in all accidents analysed). It was also found that physical environment (23.4% of all cases considered) was the second most dominant workplace factor recognized. The results obtained also revealed that some systemic factors were associated with specific workplace factors more than others. For instance, the result of behavioural environment (workplace factor) was usually due to poor leadership problem (systemic factor), problems seen in housekeeping (systemic factor), hazard identification (systemic factor), risk management (systemic factor), and designs (systemic factor), these were also the causes of poor physical environment. In the unsafe work practices (workplace factor), hazard identification was the most common systemic factor that was recognized whereas in fit for purpose equipment (workplace factor) the most common associated systemic factors were risk management, leadership, hazard identification and design. The results obtained in this study were compared to those obtained in the study of Mwansa (2021), which also applied the framework used in this study to the analysis of accident reports from another mine site of the same mining company in Zambia as used in this study. Similarities and differences were obtained under the accident characterization and causation sections. The operations in both studies are different in terms of mining methods and metallurgical processing plants. This may be responsible for some of the differences in the results obtained in both studies. For instance, in Mwansa's (2021) study, the most dominant unsafe act recognized was also routine violation (36% of all cases considered) whereas the most prominent workplace factors recognized were physical environment (36% of all cases considered) and unsafe work practices (27% of all cases considered). In Mwansa's (2021) study, the most prominent systemic factors recognized as contributing to physical environment were hazard identification, work schedule, risk management, maintenance management, leadership, housekeeping, and contractor management. The results obtained in this study were also compared with previous studies from different commodities across the globe. This was done to have broader picture when dealing with mine accidents. The causes of accidents identified in this study are of significance to the safety of the industry. Overall, based on the analysis carried out in this study for the copper mining site investigated, it can be concluded that systemic factors are the main causes of accidents rather than human error and violations.

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