Study on the Configuration Causal Factors of Electric power Generation Safety Incidents Based on Grounded Theory and fsQCA

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Previous research has mainly focused on the mechanisms and causal relationships of accidents. However, these incidents result from multiple factors working together, lacking systematic analysis. This study examines 161 electric power generation safety incidents from 2015 to 2022, utilizing grounded theory for coding to construct a causal model. The derived model is used as a conditional variable for fuzzy set qualitative comparative analysis (fsQCA), with accident severity as the outcome variable. Forty-five cases are selected for assigning values, and R language and fsQCA software are integrated for univariate necessary condition analysis, followed by configurational analysis. Results show the grounded theory-derived causal model includes six factors: human unsafe behavior, equipment factors, enterprise safety management, on-site safety management, safety qualifications of personnel, and environmental factors. Necessary condition analysis indicates incidents result from multiple conditions. Configurational analysis identifies seven paths condensed into three types: management deficiency, low safety qualifications, and unsafe behavior. Recommendations are proposed for each type, discussing intrinsic connections between variables based on conditional variables in configurational paths. The aim is to reduce electric power generation safety incidents, ensure personnel safety, and guarantee continuous electricity supply. Electric power production safety incidents Causal factors of accidents Grounded Theory fsQCA Configurational analysis Figures Figure 1 Figure 2 1 Introduction Sustainable and reliable energy supply plays a crucial role in the stable development of a nation (Awan and Khan, 2014 ; R and Sushil, 2019 ). Electricity, as a vital component of energy supply, is fundamental to various sectors such as basic household electricity consumption, industrial and agricultural production, and transportation (Guerra et al., 2016 ; Novosel, 2018 ). Hydropower accounts for approximately 16% of China's total electricity generation, making it the second-largest electric power generation method after thermal electric power (Li et al., 2018 ; Feng et al., 2019 ). However, due to the influence of recent El Niño events (Witze, 2023 ), there has been a sharp decrease in river flow in China, leading to the drying up of certain river sections. Significant fluctuations in river flow have rendered hydropower generation unstable, posing a considerable challenge to China's hydropower sector and adding pressure to electricity production (Gu et al., 2022 ; Yang et al., 2022 ). The field of electric power production is characterized by a considerable level of complexity and uncertainty (Bistline, 2015 ). Encompassing various stages such as generation, transmission, distribution, substations, and delivery (Liserre et al., 2016 ), electric power production involves diverse personnel, including maintenance personnel, operators, and construction workers (Pourbeik, 2010 ).The diversity and complexity of tasks within electric power production result in the existence of numerous potential hazards in practical work environments, such as the prevalence of high-temperature and high-pressure equipment, as well as the handling of hazardous chemicals and waste materials. This reflects the stark reality that the electric power production sector is prone to occurrences of personal injury accidents (Alhelou et al., 2019 ). According to the annual compilation of 'National Electricity Accidents and power Safety Events' released (China National Energy Administration, 2022 ), in the year 2021, there were 35 reported cases of electricity-related personal injury accidents nationwide, resulting in 38 fatalities. Within the field of electric power production, diverse types of personal injury accidents exist, and the frequency of such incidents is relatively high (Marhavilas et al., 2011 ). Common occurrences during the electric power production process include electrical shocks, falls from heights, pole and tower collapses, and object impacts (Gholizadeh et al., 2021 ) Electricity plays a central role in social and economic life, and the occurrence of accidents may lead to localized power outages, giving rise to a series of cascading events (Alhelou et al., 2019 ) In severe cases, accidents can result in casualties and substantial economic losses (Kay and Malik, 2020 ; Gholizadeh et al., 2021 ). The theory of accident causation posits that various factors contribute to the occurrence of accidents (Swuste et al., 2014 ; Ge et al., 2022 ). These factors include, but are not limited to, individual unsafe behaviors (Wang et al., 2019 ; Yang et al., 2021 ; Xiang et al., 2023 ), safety management deficiencies (Pereira et al., 2018 ; Grill and Nielsen, 2019 ), working environments (Mure' et al., 2017 ; Sobhani, 2019 ), and elevated work-related stress (Liang et al., 2021 ; Liang et al., 2022 ; Hashemian and Triantis, 2023 ). Research also indicates that the occurrence of safety accidents is not attributable to a single factor but is rather the result of the simultaneous interaction of multiple factors (Baksh et al., 2015 ; Bhattacharjee et al., 2020 ) However, there is limited research on the configurational relationships among multiple factors leading to safety incidents in electric power generation. Therefore, it is imperative to elucidate the configurational relationships of factors influencing safety incidents in electric power generation, enhance safety management in the electric power production process, and mitigate the occurrence of electric power production accidents. This paper focuses on the accident notification cases released by the China National Energy Administration, collecting a total of 161 accident reports for analysis. Initially, a grounded theory approach is employed to code and analyze the accident cases, revealing a framework of influencing factors in electric power generation safety incidents. Subsequently, a causal model of influencing factors for electric power generation safety incidents is constructed based on the grounded theory analysis. Building upon this causal model, the fsQCA method is applied to explore the inherent mechanisms of accidents and identify configurational pathways leading to electric power generation safety incidents. Recommendations are then provided for enhancing electrical safety management to reduce the occurrence of electric power generation safety incidents, safeguard the personal safety of electric power production personnel, and ensure the sustained stability of electricity supply. 2 Literature review 2.1 Causal study of safety incidents Current research widely posits that personal factors constitute a primary cause of safety incidents (González-Recio et al., 2022 ). Individual unsafe behavior is recognized as a key factor contributing to accidents, with studies indicating that approximately 80%-90% of incidents are attributed to the unsafe behaviors of operational personnel (Han et al., 2013 ; Han and Lee, 2013 ). Scholars have also delved into deeper factors contributing to accidents, such as individual characteristics, including age (Peng and Chan, 2019 ), education level (Choudhry and Fang, 2008 ; Xie and Liu, 2019 ), safety climate (Kapp, 2012 ; Pandit et al., 2019 ), safety management (Zhang et al., 2023 ), safety culture (Henriqson et al., 2014 ; Cooper et al., 2019 ; Stemn et al., 2019 ), and work environment (Swuste et al., 2010 ; Mure' et al., 2017 ), among others. The actual causes of safety incidents are complex and do not adhere to a linear cause-and-effect relationship (Dekker et al., 2011 ). For example, research indicates that human error is considered a primary cause of safety issues, but organizational management deficiencies and project environments provide conditions for these errors to occur (Love et al., 2018 ; Pickup et al., 2020 ) Scholars have also explored the collective impact of individual characteristics, work pressure, safety culture, safety climate, and safety behavior on safety incidents (Seo et al., 2015 ), as well as the mutual interactions among various accident factors (Mannering et al., 2016 ).. In the exploration of safety issues, a comprehensive consideration of these factors is crucial, necessitating a foundation in the accidents themselves to analyze the configurational pathways leading to their occurrence. 2.2 Electrical safety accident research Electric power production safety incidents represent significant issues closely associated with electricity generation, potentially resulting in substantial losses in terms of human lives, property, and the environment (Kay and Malik, 2020 ; Gholizadeh et al., 2021 ). These studies concentrate on the importance and challenges of ensuring workplace safety in the electric power industry, emphasizing the time and cost constraints hindering safety behaviors (White et al., 2016 ). Individual characteristics (such as gender, age, experience, nationality, etc.) exhibit a significant correlation with safety incidents, highlighting the need to emphasize the role of personal characteristics in safety training and strategy formulation (Castillo-Rosa et al., 2017 ). While the overall injury rates in the electrical-related industries show a declining trend, specific high-risk populations still face elevated injury risks, necessitating targeted safety measures (Volberg et al., 2017 ; Liu et al., 2023 ). Additionally, personal factors, safety climate, and health conditions significantly impact occupational accidents, with age, job roles, education, and experience playing crucial roles in safety behavior (Baby et al., 2021 ). Research indicates that both technical and human factors are key elements in enhancing safety within electric power companies (Janaćković et al., 2020 ). Scholars have proposed dynamic risk control strategies to assess the similarity between accident rankings, introducing methods utilizing case knowledge bases for case matching and reuse to address issues in accident analysis and decision-making (Hao et al., 2023 ; Zhang et al., 2023 ). These studies underscore the need for a comprehensive consideration of multiple factors in safety management within the electric power industry, suggesting diverse approaches and strategies to enhance workplace safety. Through the aforementioned literature analysis, it is evident that the primary focus of current research on accident causation revolves around studying the impact of various accident-related factors or the mutual interactions between accident causation factors. While previous research on electric power production safety incidents has made progress in exploring multiple aspects from individual to organizational levels, there remains a gap in the current literature concerning configurational causation path studies specifically in the context of electric power safety incidents. 3 Methods 3.1 Grounded theory Grounded Theory is a systematic qualitative research method designed to generate interpretative theories from empirical data (Urquhart et al., 2010 ). It has been applied in various fields such as healthcare (Sbaraini et al., 2011 ), tourism (Matteucci and Gnoth, 2017 ), and construction safety (Man et al., 2017 ; Man et al., 2021 ). Grounded Theory utilizes materials including videos, audios, interview transcripts, and text records. Currently, the most common application of Grounded Theory involves the analysis of interview or survey materials, with fewer studies employing accident cases for analysis (Kennedy and Lingard, 2006 ). However, in many instances, accident cases provide a more authentic reflection of the causes and the entire process leading to accidents (Gustin, 2002 ). This study adopts the Grounded Theory by (Anselm and Juliet, 1990 ) to analyze electric power production safety incidents. The advantage of this version of Grounded Theory lies in its higher procedural level and more systematic coding process, facilitating ease of operation (Walker and Myrick, 2006 ). The coding process comprises three steps: open coding, axial coding, and selective coding, ultimately resulting in the development of a theoretical model (Walker and Myrick, 2006 ). 3.2 Fuzzy Set Qualitative Comparative Analysis Fuzzy Set Qualitative Comparative Analysis is a configurational method based on set theory and fuzzy algebra (Ragin, 2010 ), offering particular advantages in studying complex causal relationships and multiple interactions (Fiss, 2011 ). In the field of safety science, this method has garnered widespread attention (Fan et al., 2021 ). fsQCA is outcome-oriented, allowing for the identification of specific conditions necessary for achieving certain outcomes (Misangyi et al., 2017 ; Du and Kim, 2021 ). Path analysis addresses complexity issues by identifying combinations of factors that lead to the outcome (Saba, 2013 ). Unlike crisp set QCA and multi-value set QCA, fsQCA places more emphasis on studying cases, providing a more detailed explanation of causal factors (Ganter and Hecker, 2014 ). Therefore, this study utilizes fsQCA for the configurational causation path analysis of electric power production safety incidents. 3.3 Research steps Step 1: Collect electric power production safety incident cases from the monthly accident reports released by the China National Energy Administration and the annual 'Compilation of National Electric power Accidents and Electric power Safety Incidents. Step 2: Utilize procedural Grounded Theory for open coding, axial coding, and selective coding of the accident cases to develop a causation model for electric power production safety incidents. Step 3: Based on the causation model derived from Grounded Theory, identify conditional and outcome variables and establish variable assignment rules. Step 4: After assigning values to variables based on selected cases, conduct Necessary Condition Analysis (NCA) using R language combined with QCA for univariate necessary condition analysis, followed by conditional configurational analysis. Step 5: Analyze the configurational causation paths of production safety incidents, dissecting the underlying causes of electric power production safety incidents, and propose corresponding measures and recommendations. 4 Electric power Production Safety Incident Causation Model 4.1 Case collection The accident cases in this study originate from authoritative sources, including the reports of electric power production safety incidents released by the China National Energy Administration from 2015 to 2022, as well as the annual 'Compilation of National Power Accidents and Electric power Safety Incidents' published under its auspices. After excluding some incidents with key information missing due to the passage of time (such as lack of accident causes, results, etc.), a total of 161 incident cases were collected, constituting a textual material of approximately 190,000 words. These cases provide detailed information on the time, location, causes, and consequences of the accidents. Statistical analysis of accident causes, as depicted in Figure 1, indicates that falls from height are the most prevalent type of accidents in safety incidents related to electricity production. This is attributed to the nature of high-altitude operations required in various phases of the electric power system, such as transformer installation and maintenance, erection of transmission towers, and line work. These activities contribute to the high incidence of falls from heights. Additionally, electrical shock incidents were also prevalent in electric power production safety accidents, a consequence of the unique working environment in electric power enterprises. Workers engaged in maintenance activities involving live equipment, such as transmission lines and electric power lines, are at risk of electrical shock accidents if adequate safety measures are not in place. Mechanical injuries occurred frequently in the electric power production process, often resulting from unauthorized or erroneous operations by personnel. Incidents such as collapsing structures and gas poisoning were also common and warrant attention. After the collection of cases, the 161 incident cases were randomly divided into two parts for analysis. The first part, comprising 95% of the total cases (153 cases), was allocated for Grounded Theory coding. The second part, representing 5% of the total cases (8 cases), was reserved for saturation testing. An assistant researcher conducted open coding on the incident cases, abstracting detailed descriptions from the original cases into concepts and initial categories, laying the groundwork for subsequent research. Subsequently, two experts, one with extensive experience in safety management in the electric power sector and the other an academic specializing in engineering safety behavior analysis, further refined the coding through axial coding and selective coding. Through collaborative discussions, the two experts derived a theoretical model for electric power production safety accidents from the coded incident cases. 4.2 Grounded theory coding process 1 Open coding In this study, the incident reports were systematically coded sentence by sentence, conceptualizing content related to the causes of safety incidents. To minimize subjectivity in the coding process, concepts generated during coding were based on the exact wording used in the incident reports. These concepts were directly named or abstractly named using the original terms from the incident reports. The sentence-by-sentence coding of incident reports facilitated the conceptualization of raw materials and the formation of initial categories. Examples are illustrated in Table 1, columns 2 and 3. Table 1: Open coding table for incident case reports (partial examples) Original Content Conceptualization Initial Categories Main Categories Core Categories During the climbing process, due to the improper suspension of the safety belt, a worker fell from a height of approximately 10 meters from the third section tower to the inspection platform near the bottom of that tower. Incorrect fastening of safety harness Safety harness protection Failure to follow safety protection requirements Human Unsafe Behaviors The worker had weak safety awareness and did not wear insulating gloves during the operation. Non-use of insulating gloves Insulating gloves protection In the construction of replacing the 10 kV tower conductor, one worker climbed the tower without permission, leading to electric shock and death. Unauthorized climbing of the tower for operation Unauthorized operation Operational violations A worker expanded the work scope without authorization, opening the live spare electric power source cover for work. Unauthorized expansion of work scope Unauthorized expansion of work scope A worker illegally removed the steel beam anti-tilting support, causing the beam to tilt and resulting in an object strike accident. Illegally dismantling Violation of rules Illegally entering the operating gypsum discharge conveyor belt tail frame for cleaning work. Illegal entry Non-compliance During the rural electric power grid transformation and upgrading project, a worker mistakenly entered the live interval. Mistaken entry into live compartments Mistakes Personal errors A worker was electrocuted while checking the high-pressure room's live display indicator light malfunction. Faults and defects Faults and defects Equipment failure Equipment Factors Equipment design and manufacturing have obvious defects. Equipment design flaws Equipment design flaws Equipment defects The main transformer manufacturer lacks strict quality control for externally purchased components. Lack of strict quality control for components Component quality issues Equipment quality issues Inadequate safety training and education for workers. Inadequate safety training Safety training Lack of safety training Corporate Safety Management Incomplete safety management system. Incomplete safety regulations Safety regulations Safety regulation issues Company has not implemented effective safety regulations. Non-implementation of safety regulations Management inadequacy Lax supervision by enterprise managers, loopholes in management, and non-standard safety disclosures. Lax supervision by enterprise managers in work permits On-site supervision issues Negligence of corporate managers Inadequate on-site supervision, failure to timely detect and stop workers' violations. Inadequate on-site supervision Hazard investigation Inadequate on-site supervision On-site Safety Management Failure to thoroughly investigate potential accidents in the furnace. Failure to investigate accident hazards Weak safety awareness Insufficient on-site hazard investigation Workers have weak safety awareness. Weak safety awareness Insufficient risk awareness Weak safety awareness Safety Competence of Production Personnel Insufficient awareness of electrical shock risks in the high-pressure room, leading to negligence during operations. Insufficient risk awareness Job skills Poor risk identification ability Lack of proficiency in job skills, unclear understanding of live equipment parts at the site. Inadequate job skills Natural environment Inadequate job skills During the prefabrication of the chimney, a sudden strong wind caused the chimney to tilt and collapse towards the north side. Strong wind Working environment Natural environment Environmental Factors Numerous holes in the underground plant, complex working environment. Complex working environment Initial Categories Working environment … … … 2 Axial Coding Building upon the results of open coding, a further analysis is conducted to identify logical connections between various categories, forming axial categories. This represents a crucial aspect of axial coding. Through iterative comparisons and inductions, as illustrated in Table 1, Column 4. 3 Selective Coding Selective coding aims to explicitly define the relationships between major categories based on axial coding and abstract core categories that can encompass all major categories. Through the process of selective coding for major categories, six core categories are identified, as presented in Table 1, Column 5. 4.3 Saturation test To ensure the reliability and completeness of theoretical development, a saturation test is conducted. If no new categories and logical relationships are found in the newly collected data, it is considered that the conceptual model is theoretically saturated (Aldiabat and Le Navenec, 2018). In this study, the remaining 8 accident reports were coded based on the grounded theory coding procedure. Compared with the previous results, no additional factors were identified, indicating that the model is theoretically saturated. Therefore, the model passes the saturation test, and case collection and analysis are concluded. 4.4 Accident causation model Utilizing grounded theory, the accident cases were systematically analyzed through three steps: open coding, axial coding, and selective coding. The analysis yielded six causative factors: human unsafe behavior, equipment factors, enterprise safety management, on-site safety management, safety competency of production personnel, and environmental factors. These factors collectively form the accident causation model for electric power production safety incidents, as illustrated in Figure 2. 5 Accident configuration causal path analysis 5.1 Case selection Based on the causative factor model derived from grounded theory, this study identified a total of six condition variables. Following the scholars' recommendations on the number of cases for fsQCA (Zhang et al. , 2017), and under the premise of determining six condition variables, 45 representative cases were selected for fsQCA analysis. As a case-oriented research method, case selection is crucial, and the QCA method adheres to the principle of theoretical sampling rather than random sampling (Du and Jia, 2017). The selection of typical cases in this study adheres to the following criteria: (1) Representativeness: The selected accident cases are widely reported in the news or have garnered significant attention on the internet, demonstrating a certain level of representativeness. (2) Diversity: The chosen accident cases exhibit diverse characteristics in terms of accident outcomes, types, and other dimensions, enhancing the explanatory electric power of the configuration path of causative factors in electric power production safety incidents. (3) Completeness: The selected accident cases have comprehensive records of the entire incident, including crucial information such as the time and location of the accident, accident type, causes, and outcomes. 5.2 Variable configuration and assignment The setup and assignment of variables in fsQCA are crucial steps, determined by researchers based on their experience and relevant theoretical judgments. This process relies on external knowledge rather than the inherent attributes of the data itself (Du and Jia, 2017). 1 Variable Configuration In this study, six factors derived from grounded theory were established as condition variables for the causative model of electric power production safety incidents. These variables include human unsafe behavior, equipment factors, enterprise safety management, on-site safety management, safety qualifications of production personnel, and environmental factors. The outcome variable in this study is the severity level of electric power production safety incidents. The classification of accident severity refers to the "Regulations on Reporting and Investigation of Production Safety Accidents" (Legislative Affairs Office of the State Council of China, 2007). 2 Variable assignment In fsQCA, membership scores range from 0, indicating complete non-membership, to values between 0 and 0.5, signifying fuzzy non-membership. The midpoint, 0.5, represents the crossover point of the result set. Values between 0.5 and 1 indicate fuzzy membership, while a membership score of 1 denotes complete membership (Zhang and Du, 2019). Considering the actual circumstances of safety incidents and the assignment rules from relevant studies (Li and Feng, 2023), this study assigns membership values for condition variables based on their occurrence in the cases. If a condition variable appears in a case, it is considered to have fuzzy membership, and a fuzzy value between 0.5 and 1 is assigned. The specific assignment rules are outlined in Table 2. Table 2 Assignment rules Unsafe Human Behavior Errors or Mistaken Operations The absence of the appeal situation in the case is assigned a value of 0; satisfaction of one condition is assigned a value of 0.6; satisfaction of two conditions is assigned a value of 0.8; satisfaction of all conditions is assigned a value of 1. Failure to Adhere to Safety Protocols Violations of Operational Procedures Enterprise Safety Management Lack of Safety Training The absence of the appeal situation in the case is assigned a value of 0; satisfaction of one condition is assigned a value of 0.6; satisfaction of two conditions is assigned a value of 0.8; satisfaction of all conditions is assigned a value of 1. Absence or Non-Implementation of Safety Regulations Negligence by Enterprise Management On-site Safety Management Insufficient On-site Safety Supervision In the case, a score of 0 is assigned when there is no appeal situation; a score of 0.7 is assigned when one condition is met, and a score of 1 is assigned when all conditions are met. Incomplete On-site Hazard Inspections Equipment Factors Equipment Malfunction The absence of the appeal situation in the case is assigned a value of 0; satisfaction of one condition is assigned a value of 0.6; satisfaction of two conditions is assigned a value of 0.8; satisfaction of all conditions is assigned a value of 1. Inherent Equipment Defects Quality Issues with Equipment Safety Competence of Production Personnel Diminished Safety Awareness The absence of the appeal situation in the case is assigned a value of 0; satisfaction of one condition is assigned a value of 0.6; satisfaction of two conditions is assigned a value of 0.8; satisfaction of all conditions is assigned a value of 1. Limited Risk Identification Capabilities Inadequate Job Skills and Competencies Environmental Factors Production Environment In the case, a score of 0 is assigned when there is no appeal situation, and a score of 1 is assigned when the condition occurs. Natural Environment Severity of Incidents Major Incidents 1 Significant Incidents 0.8 Ordinary Incidents 0.6 Potential Hazardous Incidents 0.4 5.3 Univariate necessary condition analysis The univariate necessary condition analysis in this study was conducted using the R programming language in conjunction with the fsQCA 4.1 software. NCA serves the dual purpose of identifying specific conditions that are necessary for a particular outcome and analyzing the effect size of these conditions (Dul, 2016). The effect size ranges from 0 to 1, with larger values indicating a more substantial effect, and values below 0.1 suggesting a minimal effect. NCA methodology is versatile, capable of handling both continuous and discrete variables. In cases where both x and y are continuous or discrete, and there are five or more levels, the analysis employs Ceiling Regression (CR) to generate an upper limit function. However, if x and y are binary variables or discrete variables with fewer than 5 levels, the method uses Ceiling Envelopment (CE) to generate the function (Du et al. , 2020). Given the characteristics of the data in this study, particularly the presence of discrete variables with fewer than 5 levels, CE was employed as the method for function generation. In Table 3, this study presents the results of the R language NCA. In the NCA method, for a condition to be deemed necessary, two criteria must be met: the effect size (d) should not be less than 0.1, and the Monte Carlo simulations of permutation tests should indicate that the effect size is significant (Dul et al. , 2020). From the results in Table 3, it is observed that the p-values for all conditional variables are greater than 0.01, suggesting that none of them can be considered necessary conditions for causing electric power production safety accidents. Furthermore, this study employs QCA to verify necessary conditions, as shown in Table 4. The consistency of individual condition necessity is below 0.9, aligning with the NCA results, indicating the absence of necessary conditions within the conditional variables for causing electric power production safety accidents. Table 3 R Language necessity analysis results Conditional Variables accuracy Ceiling zone Scope Effect size（d） p-value Unsafe Human Behavior 100% 0.160 0.5 0.333 0.701 Enterprise Safety Management 100% 0.320 0.6 0.533 0.049 On-site Safety Management 100% 0.000 0.6 0.000 1.000 Equipment Factors 100% 0.200 0.6 0.333 0.432 Safety Qualities of Production Personnel 100% 0.160 0.6 0.267 0.193 Environmental Factors 100% 0.000 0.6 0.000 1.000 Table 4 QCA software necessity analysis results Variables Consistency Coverage UB 0.875 0.856 ∼UB 0.551 0.872 ES 0.801 0.826 ∼ES 0.544 0.796 OS 0.287 0.907 ∼OS 0.868 0.648 EF 0.790 0.771 ∼EF 0.496 0.789 SQ 0.500 0.907 ∼SQ 0.750 0.580 EVF 0.154 0.600 ∼EVF 0.845 0.605 5.4 Configuration analysis and interpretation 1 Configuration analysis This study utilized fsQCA4.1 software to analyze the configurations leading to electrical power production safety incidents, representing different causal configurations resulting in diverse occurrences of safety incidents. The original consistency threshold was set at 0.8, the PRI (Pattern-Response-Implication) consistency threshold at 0.75, and the case frequency threshold at 1 (Frambach et al. , 2016). Given the aim of exploring the impact of conditional variables on electrical power production safety incidents and the absence of necessary conditions in the univariate necessary condition analysis, the "Present or Absent" option was chosen for counterfactual analysis. By comparing the nested relationship between intermediate and reduced solutions, the core conditions of each solution were identified: conditions appearing in both intermediate and reduced solutions were considered core conditions, while those appearing only in the intermediate solution were considered marginal conditions (Du and Jia, 2017). QCA Analysis results are presented in Table 5. The consistency of all configuration sets exceeds 0.8, indicating that all cases meet the consistency requirement. In other words, the seven configurations in Table 3 are sufficient conditions leading to electric power production safety incidents. Each configuration involves at least two variables, reaffirming that the occurrence of electric power production safety incidents is the result of the combined action of multiple conditions. The overall consistency is 0.97, exceeding 0.9, indicating that, as a whole, the configurations are sufficient conditions for the occurrence of electric power production safety incidents. The overall coverage is 0.83, indicating that the seven configurations can explain 83% of the cases of electric power production safety incidents. Following the configurational theorizing process, equivalent configurations discovered in this study are named (Fainshmidt et al. , 2020), meaning they share the same core conditions (Fiss, 2011). In Table 5, the seven path combinations are categorized into three types of incidents, namely, management deficiency, low safety competence, and unsafe behavior. Table 5 Configuration of factors affecting electric power production safety accidents 2 Configuration explanation (1) Management Deficiency Type: When there are deficiencies in enterprise safety management and on-site safety management, it is prone to causing electrical production safety accidents. The management deficiency type includes three paths, namely C1a, C1b, and C1c, all of which have core conditions of deficiencies in enterprise safety management and on-site safety management. In C1a, deficiencies in enterprise safety management and on-site safety management serve as core conditions, while unsafe behavior is a marginal condition. This indicates that during on-site operations, workers exhibited unsafe behavior or had deficiencies in unsafe behavior, but due to inadequate enterprise safety management and on-site safety management, timely intervention did not occur, ultimately resulting in a safety accident. In C1b and C1c, deficiencies in enterprise safety management and on-site safety management serve as core conditions, with equipment factors and environmental factors serving as marginal conditions. These two configurations indicate that due to deficiencies in enterprise safety management and on-site safety management, potential safety hazards in the working equipment or working environment on-site were not correctly identified before production operations, leading to the occurrence of safety accidents. An example of a management deficiency type accident is seen in the case of "Electrical Accident at a Wind Power Company," where due to insufficient safety education and training of employees by the company, an employee was allowed to go to work. On-site management failed to effectively supervise and did not detect Sun's erroneous behavior, leading to his accidental contact with the high-voltage side B-phase cable inside the box, causing the plug with a load to disconnect, generating an arc and resulting in fatal electrocution. (2) Low Safety Competence Type: The low safety competence type encompasses two configuration paths, C2a and C2b, both with the core condition of inadequate safety competence among production personnel. Unsafe behavior concurrently serves as the edge condition for both paths, indicating an inherent connection between safety competence and unsafe behavior. From the causation model constructed based on grounded theory, inadequate safety competence among production personnel includes factors such as a lack of safety awareness, poor risk identification capabilities, and insufficient job skills and abilities. These factors have the potential to lead production personnel to engage in unsafe behavior, consequently resulting in safety accidents. An illustrative case of the low safety competence type is evident in the "Electrical Shock Fatality Incident at a Certain Electric power Generation Limited Company," where weak safety awareness and inadequate job skills of the operators, combined with an unclear understanding of the energized components of on-site equipment, led to a fatal electric shock incident when the operator, in the presence of energized static contacts on the upper part of the generator switchgear, opened the isolating baffle inside the cabinet and came into contact with the static contacts at the generator outlet switch, resulting in an electric shock fatality. (3) Unsafe Behavior Type: The unsafe behavior type comprises two configuration paths, namely C3a and C3b, both with the core condition of unsafe behavior by production personnel. According to the causation model inferred from grounded theory for safety incidents in electric power generation, unsafe behavior in the electric power production process primarily includes errors or operational mistakes, failure to adhere to safety protection requirements, and engaging in unauthorized actions. For example, in the "Fall Accident during Maintenance Project at a certain Electric power Engineering Limited Company," on-site personnel, including Mr. Na, violated enterprise safety management regulations. High-altitude work was conducted without proper work permits, and during the operation, the safety harness was improperly removed, resulting in an accidental fall and subsequent incident. 3 Robustness test This study conducted a robustness analysis of the fsQCA results, commonly employed methods include adjusting calibration thresholds, changing PRI consistency thresholds, adding or removing cases, altering frequency thresholds, and introducing other conditions (Zhang and Du, 2019). Initially, this paper increased the PRI consistency threshold from 0.75 to 0.8, following the referenced methods, and found that the three identified accident types remained inducible. The overall consistency remained largely unchanged, while the overall coverage decreased from 0.83 to 0.77. Subsequently, 4 cases were randomly selected and removed, constituting a 10% reduction in the dataset. The solutions remained similar, indicating the robustness of the research findings. 6 Discussion Examining the accident causation model constructed from the Grounded Theory perspective, the influencing factors of safety incidents in electric power production encompass six aspects: human unsafe behavior, equipment factors, enterprise safety management, on-site safety management, safety competence of production personnel, and environmental factors. The univariate necessary condition analysis indicates that none of these six condition variables individually constitute necessary conditions for causing safety incidents in electric power production. However, their combined configuration is a contributing factor to electric power production incidents. Additionally, examining the results of configuration paths reveals inherent relationships among the condition variables. In the management defect type, enterprise safety management and on-site safety management serve as core conditions. Safety incidents of this type result from negligence in safety management. Examining the results from the Grounded Theory perspective, enterprise safety management includes deficiencies in safety training, lack of safety system implementation, and managerial dereliction; on-site safety management involves inadequate on-site safety supervision and insufficient identification of on-site hazards. Analyzing configuration path C1a, the marginal condition involves unsafe behavior, where Grounded Theory results indicate human unsafe behavior includes errors or incorrect operations, failure to comply with safety protection requirements, and violations. Therefore, the relationship between safety management and unsafe behavior is as follows: deficiencies in enterprise safety training and safety systems make on-site personnel unaware of necessary safety preparations and standard safety procedures, leading to a tendency to engage in unsafe behavior. Simultaneously, due to the absence of on-site safety supervision, unsafe behaviors of on-site personnel go undetected or are not promptly and effectively stopped, ultimately resulting in safety incidents. Discussing configuration paths C1b and C1c, where equipment factors and environmental factors are marginal conditions, Grounded Theory results indicate that equipment factors include equipment failures, inherent equipment defects, and equipment quality issues, while environmental factors encompass natural and working environment conditions. Thus, the relationship between safety management and equipment factors, as well as environmental factors, is as follows: due to the absence of enterprise safety management, on-site managers fail to timely identify safety hazards associated with equipment and potential hazards in the working environment, leading to undiscovered safety hazards directly causing safety incidents. To address incidents of the management defect type, emphasis should be placed on enhancing safety management. Pereira et al. ( 2018 ) suggest that to achieve proactive safety management in production, it is essential to identify, prioritize, monitor, and control factors influencing incident occurrences, particularly recognizing precursor factors before accidents or injuries occur (Pereira et al., 2018 ). Enhancing positive safety leadership can be achieved by training and guiding on-site managers to assume leadership responsibilities, conduct individual and collective risk assessments, thereby ensuring safety in on-site production (Grill and Nielsen, 2019 ). From the configuration paths identified in the low safety competence type, namely C2a and C2b, it is evident that the core conditions for both paths are associated with the safety competence of production personnel. In both paths, unsafe behavior serves as a marginal condition. The coding results from the Grounded Theory indicate that the low safety competence of production personnel is primarily manifested in three aspects: weak safety awareness, poor risk identification capabilities, and inadequate job skills. These factors constitute the major causes of incidents characterized by low safety competence. Examining the relationship between the safety competence of production personnel and unsafe behavior from the perspective of the identified configuration paths and Grounded Theory results, it is evident that the connection lies in the following aspects: due to issues such as weak safety awareness and poor risk identification capabilities among production personnel, these individuals often engage in unsafe behaviors during electric power production activities. Negligence and an inability to accurately identify potential risks lead to behaviors such as not adhering to safety requirements, failing to implement safety measures, and disregarding safety instructions, ultimately resulting in accidents. Additionally, insufficient job skills among production personnel, preventing them from proficiently completing or handling relevant tasks, may also lead to unsafe behavior. For instance, production personnel with inadequate technical skills may lack awareness of live parts of on-site equipment, potentially engaging in hazardous actions like touching live components. Therefore, scholars suggest that enhancing the safety awareness of production personnel, fostering a correct safety attitude, and establishing positive safety role models can normalize attitudes and behavioral patterns related to safety. This approach allows production personnel to acquire necessary safety knowledge and job skills, consequently reducing the occurrence of safety incidents (Wang et al., 2018 ). Configuration paths C3a and C3b both fall under the category of unsafe behavior type, with the core condition being the occurrence of unsafe behavior. Throughout the process of Grounded Theory coding, unsafe behavior emerged as a frequently occurring factor. In many instances, the occurrence of safety incidents can be attributed to the presence of unsafe behavior, even though, in some cases, unsafe behavior may not be the most fundamental factor leading to the incident. Examining the configuration paths, it is evident that in 5 out of the 7 paths, unsafe behavior appears either as a core condition or a marginal condition in the configuration paths. Many scholars also conclude that individual unsafe behavior is a significant factor contributing to safety incidents (Wang et al., 2019 ; Yang et al., 2021 ; Xiang et al., 2023 ). Consequently, minimizing the occurrence of unsafe behavior among production personnel is considered a crucial avenue for reducing safety incidents in electric power production. As discussed earlier, there is a certain correlation among the causes of safety incidents in electric power production. Therefore, addressing and reducing unsafe behavior among production personnel requires more in-depth consideration. Electric power companies can enhance safety training, establish and implement effective regulations and procedures to elevate the safety awareness, risk identification capabilities, and job skills of production personnel, thereby reducing instances of unsafe behavior. Additionally, timely detection, intervention, and correction by on-site supervision when production personnel engage in unsafe behavior constitute an effective approach to prevent safety incidents. Furthermore, the prompt identification and elimination of safety hazards in the work environment or mechanical equipment by supervisory personnel can also significantly reduce the occurrence of safety incidents. 7 Conclusion This study analyzed 161 cases of safety incidents in electric power production, and the results are summarized as follows: (1) A causal model of safety incidents in electric power production was constructed using Grounded Theory encoding, encompassing six causative factors: human unsafe behavior, equipment factors, enterprise safety management, on-site safety management, safety competence of production personnel, and environmental factors. (2) The Grounded Theory-based causal model was employed as the conditional variable for fsQCA analysis, with the accident severity level as the outcome variable. A set of 45 representative cases were selected and assigned values based on predefined coding rules. Initially, a single-variable necessary condition analysis was conducted using R language and fsQCA software, revealing that none of the six conditional variables were necessary conditions for causing safety incidents in electric power production. Subsequently, a configurational analysis was performed, resulting in seven configurational paths that were further categorized into three types: management deficiency type, low safety competence type, and unsafe behavior type. Configurational explanations were provided for each type. (3) The composition of conditional variables in the configurational paths was discussed to explore the inherent relationships among them. Recommendations were then formulated for each type of causative factor configuration. However, this study has certain limitations. Firstly, the accident cases analyzed in this paper are sourced from a single country. Secondly, after collecting the accident cases, the study excluded some cases with missing key information, such as lacking accident causes or the consequences of the accidents. These cases might contain accident causes that were not identified by Grounded Theory in this study. In future research, efforts will be made to collect cases more extensively, and collaboration with international scholars will be strengthened to enrich the case materials, ensuring that the results obtained can be widely applicable. Declarations Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article, National Office for Philosophy and Social Sciences Fund Major Project(22&ZD105), the scientific research starting project of SWPU(202111103), SWPU humanities special fund project(2021RW041), China Construction and Labor Society Research Project(CCLI2023R002), Sichuan University's key research base project for social sciences(CYCX2023ZC06). Competing Interests The authors declare no competing interests. Author Contributions Lin Zhu: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision Writing – review & editing. Ke Xiong: Writing – review & editing, Writing – original draft, Software, Investigation, Formal analysis, Data curation. Min Pang: Data curation, Resources, Supervision. Ethical Approval Not applicable. Consent to Participate Not applicable. 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(2021), "Insights into the mechanism of construction workers’ unsafe behaviors from an individual perspective", Safety Science, (133)2021, pp. 105004.http://doi.org/10.1016/j.ssci.2020.105004 Zhang, C., Zheng, X. & Wang, F. (2017), "Application of Qualitative Comparative Analysis Method in Management Configuration Research: Review and Outlook", Foreign Economics & Management, (39)2017, pp. 68-83.http://doi.org/10.16538/j.cnki.fem.2017.04.006 Zhang, H., Geng, H., Zeng, H. & Jiang, L. (2023), "Dynamic risk evaluation and control of electrical personal accidents", Reliability Engineering & System Safety, (237)2023, pp. 109353.http://doi.org/10.1016/j.ress.2023.109353 Zhang, M. & Du, Y. (2019), "Application of QCA method in organization and management research: positioning, strategy and direction", Chinese Journal of Management, 2019, pp. 1312-1323 Zhang, Z., Guo, H., Gao, P., Wang, Y. & Fang, Y. (2023), "Impact of owners' safety management behavior on construction workers' unsafe behavior.", Safety Science, (158)2023, pp. 105944.http://doi.org/10.1016/j.ssci.2022.105944 Cite Share Download PDF Status: Published Journal Publication published 17 Aug, 2024 Read the published version in Environmental Science and Pollution Research → Version 1 posted Editorial decision: Major Revision 15 Jun, 2024 Reviewers agreed at journal 05 Apr, 2024 Reviewers invited by journal 05 Apr, 2024 Editor invited by journal 04 Apr, 2024 Editor assigned by journal 19 Mar, 2024 First submitted to journal 13 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4073769","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":287802754,"identity":"edd7480d-ff97-4c51-a353-d163e216d5bf","order_by":0,"name":"Lin Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACPmYGBiBiYGxgbwCRRGhhg2vhOQDVwkZICwNMi0QCsVrYeQ9/Lmyzkd1w843hg587GPL45Qm4jo2ZL8F4Zlua8YbbOcaGvWcYiiXbCPqFxyCZt+1wIlCLmQRvG0PihmNEaDnM2/Y/ccPNM+Y//wK17CdCi2Ezb9uBxA03eMyYwbYQ9D4zjzEzz7lk45ln0oqlZdskEmccS8CvhZ//jPFnnjI72b7jhzd+fNtmk9jffICANQjAYQAkJIhWDgLsD0hSPgpGwSgYBSMHAADnXD87w5EeJAAAAABJRU5ErkJggg==","orcid":"","institution":"SWPU: Southwest Petroleum University","correspondingAuthor":true,"prefix":"","firstName":"Lin","middleName":"","lastName":"Zhu","suffix":""},{"id":287802755,"identity":"8e69974b-138e-4021-8bed-2255b116222a","order_by":1,"name":"Ke Xiong","email":"","orcid":"","institution":"SWPU: Southwest Petroleum University","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Xiong","suffix":""},{"id":287802756,"identity":"925b0a1b-718d-4d41-a601-a87294d3f1f1","order_by":2,"name":"Min Pang","email":"","orcid":"","institution":"SWPU: Southwest Petroleum University","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Pang","suffix":""}],"badges":[],"createdAt":"2024-03-11 12:17:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4073769/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4073769/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11356-024-34702-y","type":"published","date":"2024-08-17T15:57:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54372329,"identity":"1b57bef6-2062-4419-8533-68a34fc25f08","added_by":"auto","created_at":"2024-04-09 13:19:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":137011,"visible":true,"origin":"","legend":"\u003cp\u003eStatistics of accident types\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4073769/v1/d1f7332171da5d40d48b3d8b.png"},{"id":54372277,"identity":"2334d666-6306-4e99-a7ab-005a4bee9431","added_by":"auto","created_at":"2024-04-09 13:19:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":147366,"visible":true,"origin":"","legend":"\u003cp\u003eModel of causative factors in electric power production safety incidents\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4073769/v1/e6c877b52290756b2d0ebad5.png"},{"id":63071473,"identity":"b592d484-f34a-4403-96ba-ac8e5c937ad2","added_by":"auto","created_at":"2024-08-22 20:07:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1035009,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4073769/v1/a550cdd3-d1b6-4ddc-a2a3-15c9800a9dd6.pdf"}],"financialInterests":"","formattedTitle":"Study on the Configuration Causal Factors of Electric power Generation Safety Incidents Based on Grounded Theory and fsQCA","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eSustainable and reliable energy supply plays a crucial role in the stable development of a nation (Awan and Khan, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; R and Sushil, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Electricity, as a vital component of energy supply, is fundamental to various sectors such as basic household electricity consumption, industrial and agricultural production, and transportation (Guerra et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Novosel, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Hydropower accounts for approximately 16% of China's total electricity generation, making it the second-largest electric power generation method after thermal electric power (Li et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Feng et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, due to the influence of recent El Ni\u0026ntilde;o events (Witze, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), there has been a sharp decrease in river flow in China, leading to the drying up of certain river sections. Significant fluctuations in river flow have rendered hydropower generation unstable, posing a considerable challenge to China's hydropower sector and adding pressure to electricity production (Gu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe field of electric power production is characterized by a considerable level of complexity and uncertainty (Bistline, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Encompassing various stages such as generation, transmission, distribution, substations, and delivery (Liserre et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), electric power production involves diverse personnel, including maintenance personnel, operators, and construction workers (Pourbeik, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).The diversity and complexity of tasks within electric power production result in the existence of numerous potential hazards in practical work environments, such as the prevalence of high-temperature and high-pressure equipment, as well as the handling of hazardous chemicals and waste materials. This reflects the stark reality that the electric power production sector is prone to occurrences of personal injury accidents (Alhelou et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). According to the annual compilation of 'National Electricity Accidents and power Safety Events' released (China National Energy Administration, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), in the year 2021, there were 35 reported cases of electricity-related personal injury accidents nationwide, resulting in 38 fatalities. Within the field of electric power production, diverse types of personal injury accidents exist, and the frequency of such incidents is relatively high (Marhavilas et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Common occurrences during the electric power production process include electrical shocks, falls from heights, pole and tower collapses, and object impacts (Gholizadeh et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) Electricity plays a central role in social and economic life, and the occurrence of accidents may lead to localized power outages, giving rise to a series of cascading events (Alhelou et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) In severe cases, accidents can result in casualties and substantial economic losses (Kay and Malik, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gholizadeh et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe theory of accident causation posits that various factors contribute to the occurrence of accidents (Swuste et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ge et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These factors include, but are not limited to, individual unsafe behaviors (Wang et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xiang et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), safety management deficiencies (Pereira et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Grill and Nielsen, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), working environments (Mure' et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sobhani, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and elevated work-related stress (Liang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hashemian and Triantis, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Research also indicates that the occurrence of safety accidents is not attributable to a single factor but is rather the result of the simultaneous interaction of multiple factors (Baksh et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bhattacharjee et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) However, there is limited research on the configurational relationships among multiple factors leading to safety incidents in electric power generation. Therefore, it is imperative to elucidate the configurational relationships of factors influencing safety incidents in electric power generation, enhance safety management in the electric power production process, and mitigate the occurrence of electric power production accidents.\u003c/p\u003e \u003cp\u003eThis paper focuses on the accident notification cases released by the China National Energy Administration, collecting a total of 161 accident reports for analysis. Initially, a grounded theory approach is employed to code and analyze the accident cases, revealing a framework of influencing factors in electric power generation safety incidents. Subsequently, a causal model of influencing factors for electric power generation safety incidents is constructed based on the grounded theory analysis. Building upon this causal model, the fsQCA method is applied to explore the inherent mechanisms of accidents and identify configurational pathways leading to electric power generation safety incidents. Recommendations are then provided for enhancing electrical safety management to reduce the occurrence of electric power generation safety incidents, safeguard the personal safety of electric power production personnel, and ensure the sustained stability of electricity supply.\u003c/p\u003e"},{"header":"2 Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Causal study of safety incidents\u003c/h2\u003e \u003cp\u003eCurrent research widely posits that personal factors constitute a primary cause of safety incidents (Gonz\u0026aacute;lez-Recio et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Individual unsafe behavior is recognized as a key factor contributing to accidents, with studies indicating that approximately 80%-90% of incidents are attributed to the unsafe behaviors of operational personnel (Han et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Han and Lee, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Scholars have also delved into deeper factors contributing to accidents, such as individual characteristics, including age (Peng and Chan, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), education level (Choudhry and Fang, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Xie and Liu, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), safety climate (Kapp, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pandit et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), safety management (Zhang et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), safety culture (Henriqson et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cooper et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Stemn et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and work environment (Swuste et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Mure' et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), among others.\u003c/p\u003e \u003cp\u003eThe actual causes of safety incidents are complex and do not adhere to a linear cause-and-effect relationship (Dekker et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). For example, research indicates that human error is considered a primary cause of safety issues, but organizational management deficiencies and project environments provide conditions for these errors to occur (Love et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pickup et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) Scholars have also explored the collective impact of individual characteristics, work pressure, safety culture, safety climate, and safety behavior on safety incidents (Seo et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), as well as the mutual interactions among various accident factors (Mannering et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).. In the exploration of safety issues, a comprehensive consideration of these factors is crucial, necessitating a foundation in the accidents themselves to analyze the configurational pathways leading to their occurrence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Electrical safety accident research\u003c/h2\u003e \u003cp\u003eElectric power production safety incidents represent significant issues closely associated with electricity generation, potentially resulting in substantial losses in terms of human lives, property, and the environment (Kay and Malik, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gholizadeh et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These studies concentrate on the importance and challenges of ensuring workplace safety in the electric power industry, emphasizing the time and cost constraints hindering safety behaviors (White et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIndividual characteristics (such as gender, age, experience, nationality, etc.) exhibit a significant correlation with safety incidents, highlighting the need to emphasize the role of personal characteristics in safety training and strategy formulation (Castillo-Rosa et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). While the overall injury rates in the electrical-related industries show a declining trend, specific high-risk populations still face elevated injury risks, necessitating targeted safety measures (Volberg et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, personal factors, safety climate, and health conditions significantly impact occupational accidents, with age, job roles, education, and experience playing crucial roles in safety behavior (Baby et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearch indicates that both technical and human factors are key elements in enhancing safety within electric power companies (Janaćković et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Scholars have proposed dynamic risk control strategies to assess the similarity between accident rankings, introducing methods utilizing case knowledge bases for case matching and reuse to address issues in accident analysis and decision-making (Hao et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These studies underscore the need for a comprehensive consideration of multiple factors in safety management within the electric power industry, suggesting diverse approaches and strategies to enhance workplace safety.\u003c/p\u003e \u003cp\u003eThrough the aforementioned literature analysis, it is evident that the primary focus of current research on accident causation revolves around studying the impact of various accident-related factors or the mutual interactions between accident causation factors. While previous research on electric power production safety incidents has made progress in exploring multiple aspects from individual to organizational levels, there remains a gap in the current literature concerning configurational causation path studies specifically in the context of electric power safety incidents.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Grounded theory\u003c/h2\u003e \u003cp\u003eGrounded Theory is a systematic qualitative research method designed to generate interpretative theories from empirical data (Urquhart et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). It has been applied in various fields such as healthcare (Sbaraini et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), tourism (Matteucci and Gnoth, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and construction safety (Man et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Man et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Grounded Theory utilizes materials including videos, audios, interview transcripts, and text records. Currently, the most common application of Grounded Theory involves the analysis of interview or survey materials, with fewer studies employing accident cases for analysis (Kennedy and Lingard, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, in many instances, accident cases provide a more authentic reflection of the causes and the entire process leading to accidents (Gustin, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study adopts the Grounded Theory by (Anselm and Juliet, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) to analyze electric power production safety incidents. The advantage of this version of Grounded Theory lies in its higher procedural level and more systematic coding process, facilitating ease of operation (Walker and Myrick, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The coding process comprises three steps: open coding, axial coding, and selective coding, ultimately resulting in the development of a theoretical model (Walker and Myrick, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Fuzzy Set Qualitative Comparative Analysis\u003c/h2\u003e \u003cp\u003eFuzzy Set Qualitative Comparative Analysis is a configurational method based on set theory and fuzzy algebra (Ragin, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), offering particular advantages in studying complex causal relationships and multiple interactions (Fiss, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In the field of safety science, this method has garnered widespread attention (Fan et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). fsQCA is outcome-oriented, allowing for the identification of specific conditions necessary for achieving certain outcomes (Misangyi et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Du and Kim, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Path analysis addresses complexity issues by identifying combinations of factors that lead to the outcome (Saba, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Unlike crisp set QCA and multi-value set QCA, fsQCA places more emphasis on studying cases, providing a more detailed explanation of causal factors (Ganter and Hecker, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, this study utilizes fsQCA for the configurational causation path analysis of electric power production safety incidents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Research steps\u003c/h2\u003e \u003cp\u003eStep 1: Collect electric power production safety incident cases from the monthly accident reports released by the China National Energy Administration and the annual 'Compilation of National Electric power Accidents and Electric power Safety Incidents.\u003c/p\u003e \u003cp\u003eStep 2: Utilize procedural Grounded Theory for open coding, axial coding, and selective coding of the accident cases to develop a causation model for electric power production safety incidents.\u003c/p\u003e \u003cp\u003eStep 3: Based on the causation model derived from Grounded Theory, identify conditional and outcome variables and establish variable assignment rules.\u003c/p\u003e \u003cp\u003eStep 4: After assigning values to variables based on selected cases, conduct Necessary Condition Analysis (NCA) using R language combined with QCA for univariate necessary condition analysis, followed by conditional configurational analysis.\u003c/p\u003e \u003cp\u003eStep 5: Analyze the configurational causation paths of production safety incidents, dissecting the underlying causes of electric power production safety incidents, and propose corresponding measures and recommendations.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Electric power Production Safety Incident Causation Model","content":"\u003ch2\u003e4.1 Case collection\u003c/h2\u003e\n\u003cp\u003eThe accident cases in this study originate from authoritative sources, including the reports of electric power production safety incidents released by the China National Energy Administration from 2015 to 2022, as well as the annual \u0026apos;Compilation of National Power Accidents and Electric power Safety Incidents\u0026apos; published under its auspices. After excluding some incidents with key information missing due to the passage of time (such as lack of accident causes, results, etc.), a total of 161 incident cases were collected, constituting a textual material of approximately 190,000 words. These cases provide detailed information on the time, location, causes, and consequences of the accidents.\u003c/p\u003e\n\u003cp\u003eStatistical analysis of accident causes, as depicted in Figure 1, indicates that falls from height are the most prevalent type of accidents in safety incidents related to electricity production. This is attributed to the nature of high-altitude operations required in various phases of the electric power system, such as transformer installation and maintenance, erection of transmission towers, and line work. These activities contribute to the high incidence of falls from heights. Additionally, electrical shock incidents were also prevalent in electric power production safety accidents, a consequence of the unique working environment in electric power enterprises. Workers engaged in maintenance activities involving live equipment, such as transmission lines and electric power lines, are at risk of electrical shock accidents if adequate safety measures are not in place.\u003c/p\u003e\n\u003cp\u003eMechanical injuries occurred frequently in the electric power production process, often resulting from unauthorized or erroneous operations by personnel. Incidents such as collapsing structures and gas poisoning were also common and warrant attention.\u003c/p\u003e\n\u003cp\u003eAfter the collection of cases, the 161 incident cases were randomly divided into two parts for analysis. The first part, comprising 95% of the total cases (153 cases), was allocated for Grounded Theory coding. The second part, representing 5% of the total cases (8 cases), was reserved for saturation testing. An assistant researcher conducted open coding on the incident cases, abstracting detailed descriptions from the original cases into concepts and initial categories, laying the groundwork for subsequent research. Subsequently, two experts, one with extensive experience in safety management in the electric power sector and the other an academic specializing in engineering safety behavior analysis, further refined the coding through axial coding and selective coding. Through collaborative discussions, the two experts derived a theoretical model for electric power production safety accidents from the coded incident cases.\u003c/p\u003e\n\u003ch2\u003e4.2 Grounded theory coding process\u003c/h2\u003e\n\u003cp\u003e1 Open coding\u003c/p\u003e\n\u003cp\u003eIn this study, the incident reports were systematically coded sentence by sentence, conceptualizing content related to the causes of safety incidents. To minimize subjectivity in the coding process, concepts generated during coding were based on the exact wording used in the incident reports. These concepts were directly named or abstractly named using the original terms from the incident reports. The sentence-by-sentence coding of incident reports facilitated the conceptualization of raw materials and the formation of initial categories. Examples are illustrated in Table 1, columns 2 and 3.\u003c/p\u003e\n\u003cp\u003eTable 1: Open coding table for incident case reports (partial examples)\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"679\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.43151693667158%\"\u003e\n \u003cp\u003eOriginal Content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.96759941089838%\"\u003e\n \u003cp\u003eConceptualization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003eInitial Categories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.642120765832107%\"\u003e\n \u003cp\u003eMain Categories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003eCore Categories\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.43151693667158%\"\u003e\n \u003cp\u003eDuring the climbing process, due to the improper suspension of the safety belt, a worker fell from a height of approximately 10 meters from the third section tower to the inspection platform near the bottom of that tower.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.96759941089838%\"\u003e\n \u003cp\u003eIncorrect fastening of safety harness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003eSafety harness protection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.642120765832107%\" rowspan=\"2\"\u003e\n \u003cp\u003eFailure to follow safety protection requirements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" rowspan=\"7\"\u003e\n \u003cp\u003eHuman Unsafe Behaviors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eThe worker had weak safety awareness and did not wear insulating gloves during the operation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.87224669603524%\"\u003e\n \u003cp\u003eNon-use of insulating gloves\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.12775330396476%\"\u003e\n \u003cp\u003eInsulating gloves protection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\"\u003e\n \u003cp\u003eIn the construction of replacing the 10 kV tower conductor, one worker climbed the tower without permission, leading to electric shock and death.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.516754850088184%\"\u003e\n \u003cp\u003eUnauthorized climbing of the tower for operation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eUnauthorized operation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\" rowspan=\"4\"\u003e\n \u003cp\u003eOperational violations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eA worker expanded the work scope without authorization, opening the live spare electric power source cover for work.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.87224669603524%\"\u003e\n \u003cp\u003eUnauthorized expansion of work scope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.12775330396476%\"\u003e\n \u003cp\u003eUnauthorized expansion of work scope\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eA worker illegally removed the steel beam anti-tilting support, causing the beam to tilt and resulting in an object strike accident.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.87224669603524%\"\u003e\n \u003cp\u003eIllegally dismantling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.12775330396476%\"\u003e\n \u003cp\u003eViolation of rules\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eIllegally entering the operating gypsum discharge conveyor belt tail frame for cleaning work.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.87224669603524%\"\u003e\n \u003cp\u003eIllegal entry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.12775330396476%\"\u003e\n \u003cp\u003eNon-compliance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\"\u003e\n \u003cp\u003eDuring the rural electric power grid transformation and upgrading project, a worker mistakenly entered the live interval.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.516754850088184%\"\u003e\n \u003cp\u003eMistaken entry into live compartments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eMistakes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\"\u003e\n \u003cp\u003ePersonal errors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.43151693667158%\"\u003e\n \u003cp\u003eA worker was electrocuted while checking the high-pressure room\u0026apos;s live display indicator light malfunction.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.96759941089838%\"\u003e\n \u003cp\u003eFaults and defects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003eFaults and defects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.642120765832107%\"\u003e\n \u003cp\u003eEquipment failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" rowspan=\"3\"\u003e\n \u003cp\u003eEquipment Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\"\u003e\n \u003cp\u003eEquipment design and manufacturing have obvious defects.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.516754850088184%\"\u003e\n \u003cp\u003eEquipment design flaws\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eEquipment design flaws\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\"\u003e\n \u003cp\u003eEquipment defects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\"\u003e\n \u003cp\u003eThe main transformer manufacturer lacks strict quality control for externally purchased components.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.516754850088184%\"\u003e\n \u003cp\u003eLack of strict quality control for components\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eComponent quality issues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\"\u003e\n \u003cp\u003eEquipment quality issues\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.43151693667158%\"\u003e\n \u003cp\u003eInadequate safety training and education for workers.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.96759941089838%\"\u003e\n \u003cp\u003eInadequate safety training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003eSafety training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.642120765832107%\"\u003e\n \u003cp\u003eLack of safety training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" rowspan=\"4\"\u003e\n \u003cp\u003eCorporate Safety Management\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\"\u003e\n \u003cp\u003eIncomplete safety management system.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.516754850088184%\"\u003e\n \u003cp\u003eIncomplete safety regulations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eSafety regulations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\" rowspan=\"2\"\u003e\n \u003cp\u003eSafety regulation issues\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eCompany has not implemented effective safety regulations.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.87224669603524%\"\u003e\n \u003cp\u003eNon-implementation of safety regulations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.12775330396476%\"\u003e\n \u003cp\u003eManagement inadequacy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\"\u003e\n \u003cp\u003eLax supervision by enterprise managers, loopholes in management, and non-standard safety disclosures.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.516754850088184%\"\u003e\n \u003cp\u003eLax supervision by enterprise managers in work permits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eOn-site supervision issues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\"\u003e\n \u003cp\u003eNegligence of corporate managers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.43151693667158%\"\u003e\n \u003cp\u003eInadequate on-site supervision, failure to timely detect and stop workers\u0026apos; violations.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.96759941089838%\"\u003e\n \u003cp\u003eInadequate on-site supervision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003eHazard investigation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.642120765832107%\"\u003e\n \u003cp\u003eInadequate on-site supervision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" rowspan=\"2\"\u003e\n \u003cp\u003eOn-site Safety Management\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\"\u003e\n \u003cp\u003eFailure to thoroughly investigate potential accidents in the furnace.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.516754850088184%\"\u003e\n \u003cp\u003eFailure to investigate accident hazards\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eWeak safety awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\"\u003e\n \u003cp\u003eInsufficient on-site hazard investigation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.43151693667158%\"\u003e\n \u003cp\u003eWorkers have weak safety awareness.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.96759941089838%\"\u003e\n \u003cp\u003eWeak safety awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003eInsufficient risk awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.642120765832107%\"\u003e\n \u003cp\u003eWeak safety awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" rowspan=\"3\"\u003e\n \u003cp\u003eSafety Competence of Production Personnel\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\"\u003e\n \u003cp\u003eInsufficient awareness of electrical shock risks in the high-pressure room, leading to negligence during operations.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.516754850088184%\"\u003e\n \u003cp\u003eInsufficient risk awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eJob skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\"\u003e\n \u003cp\u003ePoor risk identification ability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\"\u003e\n \u003cp\u003eLack of proficiency in job skills, unclear understanding of live equipment parts at the site.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.516754850088184%\"\u003e\n \u003cp\u003eInadequate job skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eNatural environment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\"\u003e\n \u003cp\u003eInadequate job skills\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.43151693667158%\"\u003e\n \u003cp\u003eDuring the prefabrication of the chimney, a sudden strong wind caused the chimney to tilt and collapse towards the north side.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.96759941089838%\"\u003e\n \u003cp\u003eStrong wind\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003eWorking environment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.642120765832107%\"\u003e\n \u003cp\u003eNatural environment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" rowspan=\"2\"\u003e\n \u003cp\u003eEnvironmental Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.0352733686067%\"\u003e\n \u003cp\u003eNumerous holes in the underground plant, complex working environment.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.516754850088184%\"\u003e\n \u003cp\u003eComplex working environment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.51851851851852%\"\u003e\n \u003cp\u003eInitial Categories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\"\u003e\n \u003cp\u003eWorking environment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.43151693667158%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026hellip;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.96759941089838%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026hellip;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026hellip;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.642120765832107%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e2 Axial Coding\u003c/p\u003e\n\u003cp\u003eBuilding upon the results of open coding, a further analysis is conducted to identify logical connections between various categories, forming axial categories. This represents a crucial aspect of axial coding. Through iterative comparisons and inductions, as illustrated in Table 1, Column 4.\u003c/p\u003e\n\u003cp\u003e3 Selective Coding\u003c/p\u003e\n\u003cp\u003eSelective coding aims to explicitly define the relationships between major categories based on axial coding and abstract core categories that can encompass all major categories. Through the process of selective coding for major categories, six core categories are identified, as presented in Table 1, Column 5.\u003c/p\u003e\n\u003ch2\u003e4.3 Saturation test\u003c/h2\u003e\n\u003cp\u003eTo ensure the reliability and completeness of theoretical development, a saturation test is conducted. If no new categories and logical relationships are found in the newly collected data, it is considered that the conceptual model is theoretically saturated\u0026nbsp;(Aldiabat and Le Navenec, 2018). In this study, the remaining 8 accident reports were coded based on the grounded theory coding procedure. Compared with the previous results, no additional factors were identified, indicating that the model is theoretically saturated. Therefore, the model passes the saturation test, and case collection and analysis are concluded.\u003c/p\u003e\n\u003ch2\u003e4.4 Accident causation model\u003c/h2\u003e\n\u003cp\u003eUtilizing grounded theory, the accident cases were systematically analyzed through three steps: open coding, axial coding, and selective coding. The analysis yielded six causative factors: human unsafe behavior, equipment factors, enterprise safety management, on-site safety management, safety competency of production personnel, and environmental factors. These factors collectively form the accident causation model for electric power production safety incidents, as illustrated in Figure 2.\u003c/p\u003e"},{"header":"5 Accident configuration causal path analysis","content":"\u003ch2\u003e5.1 Case selection\u003c/h2\u003e\n\u003cp\u003eBased on the causative factor model derived from grounded theory, this study identified a total of six condition variables. Following the scholars\u0026apos; recommendations on the number of cases for fsQCA\u0026nbsp;(Zhang\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e, 2017), and under the premise of determining six condition variables, 45 representative cases were selected for fsQCA analysis. As a case-oriented research method, case selection is crucial, and the QCA method adheres to the principle of theoretical sampling rather than random sampling\u0026nbsp;(Du and Jia, 2017).\u0026nbsp;The selection of typical cases in this study adheres to the following criteria:\u003c/p\u003e\n\u003cp\u003e(1) Representativeness: The selected accident cases are widely reported in the news or have garnered significant attention on the internet, demonstrating a certain level of representativeness.\u003c/p\u003e\n\u003cp\u003e(2) Diversity: The chosen accident cases exhibit diverse characteristics in terms of accident outcomes, types, and other dimensions, enhancing the explanatory electric power of the configuration path of causative factors in electric power production safety incidents.\u003c/p\u003e\n\u003cp\u003e(3) Completeness: The selected accident cases have comprehensive records of the entire incident, including crucial information such as the time and location of the accident, accident type, causes, and outcomes.\u003c/p\u003e\n\u003ch2\u003e5.2 Variable configuration and assignment\u003c/h2\u003e\n\u003cp\u003eThe setup and assignment of variables in fsQCA are crucial steps, determined by researchers based on their experience and relevant theoretical judgments. This process relies on external knowledge rather than the inherent attributes of the data itself\u0026nbsp;(Du and Jia, 2017).\u003c/p\u003e\n\u003cp\u003e1 Variable Configuration\u003c/p\u003e\n\u003cp\u003eIn this study, six factors derived from grounded theory were established as condition variables for the causative model of electric power production safety incidents. These variables include human unsafe behavior, equipment factors, enterprise safety management, on-site safety management, safety qualifications of production personnel, and environmental factors. The outcome variable in this study is the severity level of electric power production safety incidents. The classification of accident severity refers to the \u0026quot;Regulations on Reporting and Investigation of Production Safety Accidents\u0026quot;\u0026nbsp;(Legislative Affairs Office of the State Council of China, 2007).\u003c/p\u003e\n\u003cp\u003e2 Variable assignment\u003c/p\u003e\n\u003cp\u003eIn fsQCA, membership scores range from 0, indicating complete non-membership, to values between 0 and 0.5, signifying fuzzy non-membership. The midpoint, 0.5, represents the crossover point of the result set. Values between 0.5 and 1 indicate fuzzy membership, while a membership score of 1 denotes complete membership\u0026nbsp;(Zhang and Du, 2019). Considering the actual circumstances of safety incidents and the assignment rules from relevant studies\u0026nbsp;(Li and Feng, 2023), this study assigns membership values for condition variables based on their occurrence in the cases. If a condition variable appears in a case, it is considered to have fuzzy membership, and a fuzzy value between 0.5 and 1 is assigned. The specific assignment rules are outlined in Table 2.\u003c/p\u003e\n\u003cp\u003eTable 2 Assignment rules\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"679\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" rowspan=\"3\"\u003e\n \u003cp\u003eUnsafe Human Behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.889543446244478%\"\u003e\n \u003cp\u003eErrors or Mistaken Operations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.61561119293078%\" rowspan=\"3\"\u003e\n \u003cp\u003eThe absence of the appeal situation in the case is assigned a value of 0; satisfaction of one condition is assigned a value of 0.6; satisfaction of two conditions is assigned a value of 0.8; satisfaction of all conditions is assigned a value of 1.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eFailure to Adhere to Safety Protocols\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eViolations of Operational Procedures\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" rowspan=\"3\"\u003e\n \u003cp\u003eEnterprise Safety Management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.889543446244478%\"\u003e\n \u003cp\u003eLack of Safety Training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.61561119293078%\" rowspan=\"3\"\u003e\n \u003cp\u003eThe absence of the appeal situation in the case is assigned a value of 0; satisfaction of one condition is assigned a value of 0.6; satisfaction of two conditions is assigned a value of 0.8; satisfaction of all conditions is assigned a value of 1.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eAbsence or Non-Implementation of Safety Regulations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eNegligence by Enterprise Management\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" rowspan=\"2\"\u003e\n \u003cp\u003eOn-site Safety Management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.889543446244478%\"\u003e\n \u003cp\u003eInsufficient On-site Safety Supervision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.61561119293078%\" rowspan=\"2\"\u003e\n \u003cp\u003eIn the case, a score of 0 is assigned when there is no appeal situation; a score of 0.7 is assigned when one condition is met, and a score of 1 is assigned when all conditions are met.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eIncomplete On-site Hazard Inspections\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" rowspan=\"3\"\u003e\n \u003cp\u003eEquipment Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.889543446244478%\"\u003e\n \u003cp\u003eEquipment Malfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.61561119293078%\" rowspan=\"3\"\u003e\n \u003cp\u003eThe absence of the appeal situation in the case is assigned a value of 0; satisfaction of one condition is assigned a value of 0.6; satisfaction of two conditions is assigned a value of 0.8; satisfaction of all conditions is assigned a value of 1.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eInherent Equipment Defects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eQuality Issues with Equipment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" rowspan=\"3\"\u003e\n \u003cp\u003eSafety Competence of Production Personnel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.889543446244478%\"\u003e\n \u003cp\u003eDiminished Safety Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.61561119293078%\" rowspan=\"3\"\u003e\n \u003cp\u003eThe absence of the appeal situation in the case is assigned a value of 0; satisfaction of one condition is assigned a value of 0.6; satisfaction of two conditions is assigned a value of 0.8; satisfaction of all conditions is assigned a value of 1.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eLimited Risk Identification Capabilities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eInadequate Job Skills and Competencies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" rowspan=\"2\"\u003e\n \u003cp\u003eEnvironmental Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.889543446244478%\"\u003e\n \u003cp\u003eProduction Environment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.61561119293078%\" rowspan=\"2\"\u003e\n \u003cp\u003eIn the case, a score of 0 is assigned when there is no appeal situation, and a score of 1 is assigned when the condition occurs.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eNatural Environment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" rowspan=\"4\"\u003e\n \u003cp\u003eSeverity of Incidents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.889543446244478%\"\u003e\n \u003cp\u003eMajor Incidents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.61561119293078%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.80599647266314%\"\u003e\n \u003cp\u003eSignificant Incidents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.19400352733686%\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.80599647266314%\"\u003e\n \u003cp\u003eOrdinary Incidents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.19400352733686%\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.80599647266314%\"\u003e\n \u003cp\u003ePotential Hazardous Incidents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.19400352733686%\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e5.3 Univariate necessary condition analysis\u003c/h2\u003e\n\u003cp\u003eThe univariate necessary condition analysis in this study was conducted using the R programming language in conjunction with the fsQCA 4.1 software. NCA serves the dual purpose of identifying specific conditions that are necessary for a particular outcome and analyzing the effect size of these conditions\u0026nbsp;(Dul, 2016). The effect size ranges from 0 to 1, with larger values indicating a more substantial effect, and values below 0.1 suggesting a minimal effect. NCA methodology is versatile, capable of handling both continuous and discrete variables. In cases where both x and y are continuous or discrete, and there are five or more levels, the analysis employs Ceiling Regression (CR) to generate an upper limit function. However, if x and y are binary variables or discrete variables with fewer than 5 levels, the method uses Ceiling Envelopment (CE) to generate the function\u0026nbsp;(Du\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e, 2020).\u0026nbsp;Given the characteristics of the data in this study, particularly the presence of discrete variables with fewer than 5 levels, CE was employed as the method for function generation.\u003c/p\u003e\n\u003cp\u003eIn Table 3, this study presents the results of the R language NCA. In the NCA method, for a condition to be deemed necessary, two criteria must be met: the effect size (d) should not be less than 0.1, and the Monte Carlo simulations of permutation tests should indicate that the effect size is significant\u0026nbsp;(Dul\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e, 2020).\u0026nbsp;From the results in Table 3, it is observed that the p-values for all conditional variables are greater than 0.01, suggesting that none of them can be considered necessary conditions for causing electric power production safety accidents. Furthermore, this study employs QCA to verify necessary conditions, as shown in Table 4. The consistency of individual condition necessity is below 0.9, aligning with the NCA results, indicating the absence of necessary conditions within the conditional variables for causing electric power production safety accidents.\u003c/p\u003e\n\u003cp\u003eTable 3 R Language necessity analysis results\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"654\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.36600306278714%\"\u003e\n \u003cp\u003eConditional Variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.332312404287903%\"\u003e\n \u003cp\u003eaccuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.39509954058193%\"\u003e\n \u003cp\u003eCeiling zone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\"\u003e\n \u003cp\u003eScope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.76416539050536%\"\u003e\n \u003cp\u003eEffect size（d）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.36600306278714%\"\u003e\n \u003cp\u003eUnsafe Human Behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.332312404287903%\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.39509954058193%\"\u003e\n \u003cp\u003e0.160\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.76416539050536%\"\u003e\n \u003cp\u003e0.333\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\"\u003e\n \u003cp\u003e0.701\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.36600306278714%\"\u003e\n \u003cp\u003eEnterprise Safety Management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.332312404287903%\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.39509954058193%\"\u003e\n \u003cp\u003e0.320\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.76416539050536%\"\u003e\n \u003cp\u003e0.533\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\"\u003e\n \u003cp\u003e0.049\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.36600306278714%\"\u003e\n \u003cp\u003eOn-site Safety Management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.332312404287903%\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.39509954058193%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.76416539050536%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.36600306278714%\"\u003e\n \u003cp\u003eEquipment Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.332312404287903%\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.39509954058193%\"\u003e\n \u003cp\u003e0.200\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.76416539050536%\"\u003e\n \u003cp\u003e0.333\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\"\u003e\n \u003cp\u003e0.432\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.36600306278714%\"\u003e\n \u003cp\u003eSafety Qualities of Production Personnel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.332312404287903%\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.39509954058193%\"\u003e\n \u003cp\u003e0.160\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.76416539050536%\"\u003e\n \u003cp\u003e0.267\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\"\u003e\n \u003cp\u003e0.193\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.36600306278714%\"\u003e\n \u003cp\u003eEnvironmental Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.332312404287903%\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.39509954058193%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.76416539050536%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4 QCA software necessity analysis results\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"243\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"bottom\"\u003e\n \u003cp\u003eConsistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.75206611570248%\" valign=\"bottom\"\u003e\n \u003cp\u003eCoverage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"top\"\u003e\n \u003cp\u003eUB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.875\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.75206611570248%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.856\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026sim;UB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.551\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.75206611570248%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.872\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"top\"\u003e\n \u003cp\u003eES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.801\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.75206611570248%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.826\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026sim;ES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.544\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.75206611570248%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.796\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"top\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.287\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.75206611570248%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.907\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026sim;OS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.868\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.75206611570248%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.648\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"top\"\u003e\n \u003cp\u003eEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.790\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.75206611570248%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.771\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026sim;EF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.496\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.75206611570248%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.789\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"top\"\u003e\n \u003cp\u003eSQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.500\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.75206611570248%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.907\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026sim;SQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.750\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.75206611570248%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.580\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"top\"\u003e\n \u003cp\u003eEVF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.154\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.75206611570248%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026sim;EVF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.12396694214876%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.845\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.75206611570248%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e5.4 Configuration analysis and interpretation\u003c/h2\u003e\n\u003cp\u003e1 Configuration analysis\u003c/p\u003e\n\u003cp\u003eThis study utilized fsQCA4.1 software to analyze the configurations leading to electrical power production safety incidents, representing different causal configurations resulting in diverse occurrences of safety incidents. The original consistency threshold was set at 0.8, the PRI (Pattern-Response-Implication) consistency threshold at 0.75, and the case frequency threshold at 1\u0026nbsp;(Frambach\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e, 2016).\u0026nbsp;Given the aim of exploring the impact of conditional variables on electrical power production safety incidents and the absence of necessary conditions in the univariate necessary condition analysis, the \u0026quot;Present or Absent\u0026quot; option was chosen for counterfactual analysis. By comparing the nested relationship between intermediate and reduced solutions, the core conditions of each solution were identified: conditions appearing in both intermediate and reduced solutions were considered core conditions, while those appearing only in the intermediate solution were considered marginal conditions\u0026nbsp;(Du and Jia, 2017).\u003c/p\u003e\n\u003cp\u003eQCA Analysis results are presented in Table 5. The consistency of all configuration sets exceeds 0.8, indicating that all cases meet the consistency requirement. In other words, the seven configurations in Table 3 are sufficient conditions leading to electric power production safety incidents. Each configuration involves at least two variables, reaffirming that the occurrence of electric power production safety incidents is the result of the combined action of multiple conditions. The overall consistency is 0.97, exceeding 0.9, indicating that, as a whole, the configurations are sufficient conditions for the occurrence of electric power production safety incidents. The overall coverage is 0.83, indicating that the seven configurations can explain 83% of the cases of electric power production safety incidents. Following the configurational theorizing process, equivalent configurations discovered in this study are named\u0026nbsp;(Fainshmidt\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e, 2020), meaning they share the same core conditions\u0026nbsp;(Fiss, 2011).\u0026nbsp;In Table 5, the seven path combinations are categorized into three types of incidents, namely, management deficiency, low safety competence, and unsafe behavior.\u003c/p\u003e\n\u003cp\u003eTable 5 Configuration of factors affecting electric power production safety accidents\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\u003cimg 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\" style=\"width: 1173px; height: 679.263px;\" width=\"1173\" height=\"679.263\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"687\"\u003e\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e2 Configuration explanation\u003c/p\u003e\n\u003cp\u003e(1) Management Deficiency Type: When there are deficiencies in enterprise safety management and on-site safety management, it is prone to causing electrical production safety accidents. The management deficiency type includes three paths, namely C1a, C1b, and C1c, all of which have core conditions of deficiencies in enterprise safety management and on-site safety management. In C1a, deficiencies in enterprise safety management and on-site safety management serve as core conditions, while unsafe behavior is a marginal condition. This indicates that during on-site operations, workers exhibited unsafe behavior or had deficiencies in unsafe behavior, but due to inadequate enterprise safety management and on-site safety management, timely intervention did not occur, ultimately resulting in a safety accident. In C1b and C1c, deficiencies in enterprise safety management and on-site safety management serve as core conditions, with equipment factors and environmental factors serving as marginal conditions. These two configurations indicate that due to deficiencies in enterprise safety management and on-site safety management, potential safety hazards in the working equipment or working environment on-site were not correctly identified before production operations, leading to the occurrence of safety accidents. An example of a management deficiency type accident is seen in the case of \u0026quot;Electrical Accident at a Wind Power Company,\u0026quot; where due to insufficient safety education and training of employees by the company, an employee was allowed to go to work. On-site management failed to effectively supervise and did not detect Sun\u0026apos;s erroneous behavior, leading to his accidental contact with the high-voltage side B-phase cable inside the box, causing the plug with a load to disconnect, generating an arc and resulting in fatal electrocution.\u003c/p\u003e\n\u003cp\u003e(2) Low Safety Competence Type: The low safety competence type encompasses two configuration paths, C2a and C2b, both with the core condition of inadequate safety competence among production personnel. Unsafe behavior concurrently serves as the edge condition for both paths, indicating an inherent connection between safety competence and unsafe behavior. From the causation model constructed based on grounded theory, inadequate safety competence among production personnel includes factors such as a lack of safety awareness, poor risk identification capabilities, and insufficient job skills and abilities. These factors have the potential to lead production personnel to engage in unsafe behavior, consequently resulting in safety accidents. An illustrative case of the low safety competence type is evident in the \u0026quot;Electrical Shock Fatality Incident at a Certain Electric power Generation Limited Company,\u0026quot; where weak safety awareness and inadequate job skills of the operators, combined with an unclear understanding of the energized components of on-site equipment, led to a fatal electric shock incident when the operator, in the presence of energized static contacts on the upper part of the generator switchgear, opened the isolating baffle inside the cabinet and came into contact with the static contacts at the generator outlet switch, resulting in an electric shock fatality.\u003c/p\u003e\n\u003cp\u003e(3) Unsafe Behavior Type: The unsafe behavior type comprises two configuration paths, namely C3a and C3b, both with the core condition of unsafe behavior by production personnel. According to the causation model inferred from grounded theory for safety incidents in electric power generation, unsafe behavior in the electric power production process primarily includes errors or operational mistakes, failure to adhere to safety protection requirements, and engaging in unauthorized actions. For example, in the \u0026quot;Fall Accident during Maintenance Project at a certain Electric power Engineering Limited Company,\u0026quot; on-site personnel, including Mr. Na, violated enterprise safety management regulations. High-altitude work was conducted without proper work permits, and during the operation, the safety harness was improperly removed, resulting in an accidental fall and subsequent incident.\u003c/p\u003e\n\u003cp\u003e3 Robustness test\u003c/p\u003e\n\u003cp\u003eThis study conducted a robustness analysis of the fsQCA results, commonly employed methods include adjusting calibration thresholds, changing PRI consistency thresholds, adding or removing cases, altering frequency thresholds, and introducing other conditions (Zhang and Du, 2019). Initially, this paper increased the PRI consistency threshold from 0.75 to 0.8, following the referenced methods, and found that the three identified accident types remained inducible. The overall consistency remained largely unchanged, while the overall coverage decreased from 0.83 to 0.77. Subsequently, 4 cases were randomly selected and removed, constituting a 10% reduction in the dataset. The solutions remained similar, indicating the robustness of the research findings.\u003c/p\u003e"},{"header":"6 Discussion","content":"\u003cp\u003eExamining the accident causation model constructed from the Grounded Theory perspective, the influencing factors of safety incidents in electric power production encompass six aspects: human unsafe behavior, equipment factors, enterprise safety management, on-site safety management, safety competence of production personnel, and environmental factors. The univariate necessary condition analysis indicates that none of these six condition variables individually constitute necessary conditions for causing safety incidents in electric power production. However, their combined configuration is a contributing factor to electric power production incidents. Additionally, examining the results of configuration paths reveals inherent relationships among the condition variables.\u003c/p\u003e \u003cp\u003eIn the management defect type, enterprise safety management and on-site safety management serve as core conditions. Safety incidents of this type result from negligence in safety management. Examining the results from the Grounded Theory perspective, enterprise safety management includes deficiencies in safety training, lack of safety system implementation, and managerial dereliction; on-site safety management involves inadequate on-site safety supervision and insufficient identification of on-site hazards. Analyzing configuration path C1a, the marginal condition involves unsafe behavior, where Grounded Theory results indicate human unsafe behavior includes errors or incorrect operations, failure to comply with safety protection requirements, and violations. Therefore, the relationship between safety management and unsafe behavior is as follows: deficiencies in enterprise safety training and safety systems make on-site personnel unaware of necessary safety preparations and standard safety procedures, leading to a tendency to engage in unsafe behavior. Simultaneously, due to the absence of on-site safety supervision, unsafe behaviors of on-site personnel go undetected or are not promptly and effectively stopped, ultimately resulting in safety incidents.\u003c/p\u003e \u003cp\u003eDiscussing configuration paths C1b and C1c, where equipment factors and environmental factors are marginal conditions, Grounded Theory results indicate that equipment factors include equipment failures, inherent equipment defects, and equipment quality issues, while environmental factors encompass natural and working environment conditions. Thus, the relationship between safety management and equipment factors, as well as environmental factors, is as follows: due to the absence of enterprise safety management, on-site managers fail to timely identify safety hazards associated with equipment and potential hazards in the working environment, leading to undiscovered safety hazards directly causing safety incidents. To address incidents of the management defect type, emphasis should be placed on enhancing safety management. Pereira et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) suggest that to achieve proactive safety management in production, it is essential to identify, prioritize, monitor, and control factors influencing incident occurrences, particularly recognizing precursor factors before accidents or injuries occur (Pereira et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Enhancing positive safety leadership can be achieved by training and guiding on-site managers to assume leadership responsibilities, conduct individual and collective risk assessments, thereby ensuring safety in on-site production (Grill and Nielsen, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom the configuration paths identified in the low safety competence type, namely C2a and C2b, it is evident that the core conditions for both paths are associated with the safety competence of production personnel. In both paths, unsafe behavior serves as a marginal condition. The coding results from the Grounded Theory indicate that the low safety competence of production personnel is primarily manifested in three aspects: weak safety awareness, poor risk identification capabilities, and inadequate job skills. These factors constitute the major causes of incidents characterized by low safety competence.\u003c/p\u003e \u003cp\u003eExamining the relationship between the safety competence of production personnel and unsafe behavior from the perspective of the identified configuration paths and Grounded Theory results, it is evident that the connection lies in the following aspects: due to issues such as weak safety awareness and poor risk identification capabilities among production personnel, these individuals often engage in unsafe behaviors during electric power production activities. Negligence and an inability to accurately identify potential risks lead to behaviors such as not adhering to safety requirements, failing to implement safety measures, and disregarding safety instructions, ultimately resulting in accidents.\u003c/p\u003e \u003cp\u003eAdditionally, insufficient job skills among production personnel, preventing them from proficiently completing or handling relevant tasks, may also lead to unsafe behavior. For instance, production personnel with inadequate technical skills may lack awareness of live parts of on-site equipment, potentially engaging in hazardous actions like touching live components. Therefore, scholars suggest that enhancing the safety awareness of production personnel, fostering a correct safety attitude, and establishing positive safety role models can normalize attitudes and behavioral patterns related to safety. This approach allows production personnel to acquire necessary safety knowledge and job skills, consequently reducing the occurrence of safety incidents (Wang et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConfiguration paths C3a and C3b both fall under the category of unsafe behavior type, with the core condition being the occurrence of unsafe behavior. Throughout the process of Grounded Theory coding, unsafe behavior emerged as a frequently occurring factor. In many instances, the occurrence of safety incidents can be attributed to the presence of unsafe behavior, even though, in some cases, unsafe behavior may not be the most fundamental factor leading to the incident. Examining the configuration paths, it is evident that in 5 out of the 7 paths, unsafe behavior appears either as a core condition or a marginal condition in the configuration paths. Many scholars also conclude that individual unsafe behavior is a significant factor contributing to safety incidents (Wang et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xiang et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, minimizing the occurrence of unsafe behavior among production personnel is considered a crucial avenue for reducing safety incidents in electric power production.\u003c/p\u003e \u003cp\u003eAs discussed earlier, there is a certain correlation among the causes of safety incidents in electric power production. Therefore, addressing and reducing unsafe behavior among production personnel requires more in-depth consideration. Electric power companies can enhance safety training, establish and implement effective regulations and procedures to elevate the safety awareness, risk identification capabilities, and job skills of production personnel, thereby reducing instances of unsafe behavior. Additionally, timely detection, intervention, and correction by on-site supervision when production personnel engage in unsafe behavior constitute an effective approach to prevent safety incidents. Furthermore, the prompt identification and elimination of safety hazards in the work environment or mechanical equipment by supervisory personnel can also significantly reduce the occurrence of safety incidents.\u003c/p\u003e"},{"header":"7 Conclusion","content":"\u003cp\u003eThis study analyzed 161 cases of safety incidents in electric power production, and the results are summarized as follows: (1) A causal model of safety incidents in electric power production was constructed using Grounded Theory encoding, encompassing six causative factors: human unsafe behavior, equipment factors, enterprise safety management, on-site safety management, safety competence of production personnel, and environmental factors. (2) The Grounded Theory-based causal model was employed as the conditional variable for fsQCA analysis, with the accident severity level as the outcome variable. A set of 45 representative cases were selected and assigned values based on predefined coding rules. Initially, a single-variable necessary condition analysis was conducted using R language and fsQCA software, revealing that none of the six conditional variables were necessary conditions for causing safety incidents in electric power production. Subsequently, a configurational analysis was performed, resulting in seven configurational paths that were further categorized into three types: management deficiency type, low safety competence type, and unsafe behavior type. Configurational explanations were provided for each type. (3) The composition of conditional variables in the configurational paths was discussed to explore the inherent relationships among them. Recommendations were then formulated for each type of causative factor configuration.\u003c/p\u003e \u003cp\u003eHowever, this study has certain limitations. Firstly, the accident cases analyzed in this paper are sourced from a single country. Secondly, after collecting the accident cases, the study excluded some cases with missing key information, such as lacking accident causes or the consequences of the accidents. These cases might contain accident causes that were not identified by Grounded Theory in this study. In future research, efforts will be made to collect cases more extensively, and collaboration with international scholars will be strengthened to enrich the case materials, ensuring that the results obtained can be widely applicable.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article, National Office for Philosophy and Social Sciences Fund Major Project(22\u0026amp;ZD105), the scientific research starting project of SWPU(202111103), SWPU humanities special fund project(2021RW041), China Construction and Labor Society Research Project(CCLI2023R002), Sichuan University\u0026apos;s key research base project for social sciences(CYCX2023ZC06).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLin Zhu: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision Writing \u0026ndash; review \u0026amp; editing. Ke Xiong: Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Software, Investigation, Formal analysis, Data curation. Min Pang: Data curation, Resources, Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors confirm the publication has been approved.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAldiabat, K. M. \u0026amp; Le Navenec, C. L. 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Previous research has mainly focused on the mechanisms and causal relationships of accidents. However, these incidents result from multiple factors working together, lacking systematic analysis. This study examines 161 electric power generation safety incidents from 2015 to 2022, utilizing grounded theory for coding to construct a causal model. The derived model is used as a conditional variable for fuzzy set qualitative comparative analysis (fsQCA), with accident severity as the outcome variable. Forty-five cases are selected for assigning values, and R language and fsQCA software are integrated for univariate necessary condition analysis, followed by configurational analysis. Results show the grounded theory-derived causal model includes six factors: human unsafe behavior, equipment factors, enterprise safety management, on-site safety management, safety qualifications of personnel, and environmental factors. Necessary condition analysis indicates incidents result from multiple conditions. Configurational analysis identifies seven paths condensed into three types: management deficiency, low safety qualifications, and unsafe behavior. Recommendations are proposed for each type, discussing intrinsic connections between variables based on conditional variables in configurational paths. The aim is to reduce electric power generation safety incidents, ensure personnel safety, and guarantee continuous electricity supply.\u003c/p\u003e","manuscriptTitle":"Study on the Configuration Causal Factors of Electric power Generation Safety Incidents Based on Grounded Theory and fsQCA","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-09 13:17:38","doi":"10.21203/rs.3.rs-4073769/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2024-06-16T01:44:54+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-04-06T00:05:58+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-05T07:32:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2024-04-04T16:47:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-19T04:04:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2024-03-13T21:45:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"93a268c2-bfb6-4ded-b672-8e061e58b020","owner":[],"postedDate":"April 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-22T19:38:46+00:00","versionOfRecord":{"articleIdentity":"rs-4073769","link":"https://doi.org/10.1007/s11356-024-34702-y","journal":{"identity":"environmental-science-and-pollution-research","isVorOnly":false,"title":"Environmental Science and Pollution Research"},"publishedOn":"2024-08-17 15:57:15","publishedOnDateReadable":"August 17th, 2024"},"versionCreatedAt":"2024-04-09 13:17:38","video":"","vorDoi":"10.1007/s11356-024-34702-y","vorDoiUrl":"https://doi.org/10.1007/s11356-024-34702-y","workflowStages":[]},"version":"v1","identity":"rs-4073769","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4073769","identity":"rs-4073769","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}