Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Please note that all times are shown in the time zone of the conference. The current conference time is: 24th Aug 2026, 05:30:25am America, Santiago
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Daily Overview |
| Session | |
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1C: Entrepreneurship & Innovation Virtual location: VIRTUAL: Agora Meetings | |
| Presentation 5 | |
9:32am - 9:40am
Safety culture and job satisfaction among employees of a mining company in Pasco, Peru. Universidad Tecnológica del Perú UTP - (PE), Perú The present study examined the association between safety culture and job satisfaction within the context of the mining industry. The analysis focused on a sample of 100 employees from a mining company located in Pasco, Peru. A quantitative approach was adopted, featuring a correlational, cross-sectional, and non-experimental design. Data was collected using a 20-item Likert scale questionnaire and processed with SPSS v.28 software. After confirming the absence of normality using the Kolmogorov–Smirnov test, the Spearman's Rho coefficient was employed for the inferential analysis. The results revealed a positive and highly significant correlation between safety culture and job satisfaction (ρ = 0.687; p < 0.001), classified as a relationship of considerable magnitude. Among the specific dimensions, Supervision (ρ = 0.684) and Communication (ρ = 0.603) showed the strongest associations with safety culture, highlighting the critical role of leadership and effective information flow. In contrast, the Work Nature dimension showed a moderate positive correlation (ρ = 0.429), reaffirming that intrinsic factors carry less relative weight than hygiene factors in high-demand operational contexts. Overall, the findings position safety culture as a key determinant of job satisfaction in complex and high-risk environments. It is recommended that future research incorporate predictive techniques, such as Structural Equation Modeling and machine learning algorithms, to deepen the understanding of underlying causal and non-linear mechanisms. | |
