Measuring safety performance indicators: Iranian power industry

Purpose The electricity distribution industry is among the most hazardous sectors globally, with Iran experiencing a particularly high rate of severe accidents. While safety performance is traditionally measured using reactive, lagging indicators (e.g. accident rates), modern safety science advocates the use of proactive, leading indicators to predict and prevent incidents. However, a validated model integrating both indicator types is lacking for the Iranian context. This study aims to develop and empirically validate an integrated safety performance evaluation model that links leading and lagging indicators to provide an actionable tool for the electricity distribution industry. Design/methodology/approach This applied, cross-sectional study was conducted within an Iranian power distribution company. A comprehensive framework was constructed, categorizing 14 leading safety criteria into organizational, technical and human dimensions. Data on these leading indicators were collected from 158 safety personnel across 27 subsidiaries using a binary-scored questionnaire. The relative weight of each dimension and criterion was determined using the fuzzy analytic hierarchy process (FAHP). An overall safety performance score (SPS) for each subsidiary and the mother company was calculated by integrating the leading indicator scores with their FAHP weights. Concurrently, standard lagging indicators – accident frequency rate (AFR), accident severity rate (ASR) and frequency-severity indicator (FSI) – were calculated from 1 year of archival data. Model validation was performed using Spearman’s correlation and linear regression analysis in Statistical Package for the Social Sciences (SPSS) to examine the relationship between the SPS (leading index) and the lagging indicators. Findings The overall company SPS was 0.67 out of 1.00. The organizational dimension was identified as the most critical (weight = 0.431), while the technical dimension was the weakest performer. Statistical analysis confirmed a significant negative correlation between the SPS and both AFR and ASR. Linear regression demonstrated that a one-unit increase in the SPS predicts a 55.0% decrease in AFR and a 54.6% decrease in FSI, thereby validating the predictive power of the proposed model. Research limitations/implications This study, like any other study, has faced limitations, including the fact that in this study, safety criteria were examined in three dimensions: organizational, technical and human. Other dimensions of safety, including environmental and behavioral dimensions, which have also been mentioned in the research background, can be considered, and appropriate criteria can be presented. Binary logic was used to score the indicators in this study, and in future research, the radar logic of the organizational excellence model can be used to measure the status of the indicators. This method helps to calculate the performance score for the company and also to define corrective actions to improve the safety performance status. One of the limitations of the research is that the study was conducted in one of the Iranian electricity distribution companies (Khorasan Razavi Province Electricity Distribution Company), and naturally, cultural characteristics can affect its results, thus limiting the possibility of generalizing its results to other countries. Practical implications The research findings can be used to develop safety policies for the power industry across three dimensions: organizational, technical and human. Based on the criteria and indicators of each of the mentioned dimensions, operational plans can be developed considering the score of each indicator. This is not a universal proposition, but it should be done based on the differing situations such as company and country. Social implications This study provides an empirically validated model that effectively links proactive safety management with reactive outcomes in a high-risk industry. The findings demonstrate that strategic improvement in leading indicators – particularly by addressing identified weaknesses in the technical dimension – can significantly reduce accident rates. The model offers managers a practical and diagnostic dashboard for transitioning from a reactive to a proactive, data-driven safety management strategy, with potential applicability in other high-risk sectors in similar cultural contexts. Originality/value This study offers a theoretical base for preventing workplace accidents and improving safety of power distribution companies in Iranian culture as a unique culture.