The Impact of Job Insecurity on Miner Safety Behavior—A Study Based on SEM and fsQCA

Abstract

The intelligent transformation of coal mines is one of the current trends in developing China’s coal mining industry. To explore the impact of miners’ insecurity on their safety behavior under this trend, miners’ psychological resilience was introduced as the mediating variable, and team safety climate was used as the moderating variable to conduct a questionnaire survey of frontline miners. The data analysis was carried out using descriptive statistics, correlation analysis, structural equation modeling (SEM), and the fsQCA method to explore the impact of job insecurity on miners’ risk behavior through psychological resilience from the dimensions of job loss insecurity, job performance insecurity, and interpersonal insecurity. The results show that the sense of insecurity of the miners has a significant negative correlation with security behavior and a significant negative correlation with psychological toughness; miners’ psychological resilience plays an intermediary role in the correlation between job loss insecurity and miners’ risk behavior. Meanwhile, team safety climate has a significant moderating effect on the relationship between job insecurity and psychological resilience, as well as the relationship between psychological resilience and safety behavior; that is, a good team safety climate can effectively reduce the negative impact of job insecurity brought about by the transformation and upgrading of coal mines.

1. Introduction

As an important energy source of the country, coal mines will still be the main source of energy for a considerable period of time [1]. With the advancement of technology, the trend of intelligent development in coal mines is becoming increasingly apparent, but this process needs to be gradually promoted [2]. Before the full implementation of intelligence, a small number of miners are still needed to monitor and command dispatch work underground [3].

In traditional mining research, the individualized nature of miners’ work and the emphasis on various factors have resulted in persistent risky behavior [4]. Currently, China is in the transitional phase of the intelligent transformation of coal mines. Miners’ sense of job insecurity has significantly increased, and this uncertainty may have an impact on their safety behavior. During the transition, miners face not only technical challenges [5] but also psychological and professional uncertainties. The gradual introduction of unmanned technology in coal mines may cause miners to worry about the stability of their jobs, thereby affecting their work attitude and safety behavior [6]. In this regard, how to ensure the security and mental health of miners in promoting intelligence and unmanned operation has become a key concern for coal mine managers and policymakers.

International researchers have examined factors such as race [7], work stress [8], and emotions [9] affecting miners in safety management studies, indicating these factors can contribute to miners’ feelings of job insecurity. In job insecurity research, studies, such as those by Probs [10] and Lawler (2006) [11], have compared data from different scenarios, demonstrating its negative impact on individuals. As individuals in China’s collectivist culture have a greater negative impact than individuals in the US’s individualistic culture, it is more important to pay attention to the relationship between job insecurity and miner safety behavior in the context of coal mine organization reform in China.

Chinese researchers have explored the impact of miners’ job insecurity on safety behavior. Using psychological measurement tools, these studies analyze the complex relationship between job insecurity and miners’ stress [12], anxiety [13], and fatigue [14] and how these factors influence safety behaviors. Findings show that high job insecurity often leads to increased psychological stress, anxiety, and fatigue, raising accident risk. Other research also examines the relationship between miners’ job insecurity and organizational factors in their work environment, particularly organizational safety culture, leadership style, and management systems. These studies conclude that a positive safety culture [15], supportive leadership [16], and robust management systems [17] can effectively alleviate job insecurity, promoting adherence to safety protocols and reducing accident rates.

Despite extensive research exploring factors influencing miner safety behavior, studies within the context of intelligent transformation remain quite limited. Current literature primarily focuses on traditional coal mining contexts, lacking in-depth exploration of miners’ insecurities and psychological aspects during intelligent transformation. This thesis enriches the theoretical framework by analyzing the impact of miners’ insecurity on safety behavior in the context of intelligent transformation and provides new perspectives for managing miners’ safety behavior in practice. With an aim to explore the impact of miners’ job insecurity on their safety behavior during the transformation period of coal mines by analyzing the psychological state and behavioral patterns of miners when facing technological changes, corresponding management strategies and measures were proposed in this study to intelligentize coal mines while ensuring the security and well-being of miners and promoting the sustainable development of the coal mining industry.

2. Research Hypothesis and Conceptual Model

2.1. Variable Definitions

To ensure the operability and measurability of the model variables, this study further elaborates on the definitions and dimension divisions of miners’ job insecurity, team safety climate, psychological resilience, and safety behavior within the coal mining enterprise context, based on previous research.

(1)

Job Insecurity

This thesis considers both subjective and objective job insecurity perspectives, aligns with the current coal industry transformation trends, and recognizes the high-risk job nature and characteristics of miners. The job insecurity of miners is divided into three sub-factors: job loss insecurity, job execution insecurity, and interpersonal insecurity. Job loss insecurity refers to the anxiety miners experience concerning the continuation of their employment contracts. Job execution insecurity is the anxiety related to the potential loss of key job characteristics. Interpersonal insecurity pertains to the anxiety miners feel about possible deterioration in relationships with other organizational members.

(2)

Team Safety Climate

Recognizing the unique nature of coal mining, this study builds on Zohar’s (1980) definition to define team safety climate as the individual’s psychological perception within the group organization and the employees’ recognition and evaluation of the organization’s emphasis on safety. Since there is no precise standard and principle for classifying the dimensions of team safety climate, this study does not categorize these dimensions further.

(3)

Psychological Resilience

Psychological resilience is known to have various connotations and definitions depending on research subjects and objectives, yet scholars generally acknowledge it as a psychological phenomenon centered on individuals, featuring behavior or emotion as its medium. The higher the level of psychological resilience, the stronger the individual’s psychological recovery ability. Therefore, this thesis starts from a psychological perspective, takes coal mine workers as the primary research object, considers the work characteristics of miners, and defines psychological resilience as a psychological phenomenon in which an individual’s emotional state or work performance quickly returns to normal levels when facing stress, threats or other negative events, such as layoffs, unfavorable transfers or staff reductions.

(4)

Miners’ Safety Behavior

Considering the real situations miners face, this thesis draws from Que Ting’s research, which investigates unsafe behavior in terms of personal safety quality, work characteristics, and safety management. Among these, personal safety quality refers to miners’ self-awareness of their safety consciousness, knowledge, and skills. Work characteristics encompass miners’ experiences concerning the nature of their work (monotony, urgency, complexity) and work environment (temperature, noise, lighting). Safety management refers to miners’ understanding level of corporate safety management regulations, safety education, and safety culture. Based on this definition and actual coal mining circumstances, a frontline miner safety behavior scale is constructed and validated.

2.2. Research Hypothesis

Miners, as the most active factor in coal mine production, are the main focus of coal mine safety behavior management, with the main goal of improving miners’ security performance [18]. The job insecurity faced by miners, such as tense interpersonal situations [19], unemployment [20], and imbalanced salary distribution [21], directly or indirectly affects their compliance with regulations and participation in safety behaviors and even hinders further improvement in coal mine safety levels. Currently, domestic and foreign scholars have conducted multi-angle and in-depth research on the impact of job insecurity on miners’ behavior [22,23,24,25,26]. Some scholars have studied the relationship between job insecurity and employees’ psychological factors, such as Hu Sanman (2017), who examined the relationship between job insecurity and emotional commitment and found that job insecurity can significantly negatively predict employees’ emotional commitment and intention to leave. Yi Tao et al. (2020) figured out that job insecurity is significantly correlated with miners’ intention to engage in unsafe behaviors, with organizational structural changes as the background. On the other hand, researchers have also focused on the negative effects of job insecurity on employees’ psychological and behavioral levels [27]. For instance, Probst and Brubaker (2001) demonstrated that job insecurity not only affects employees’ work attitudes but also influences their security motivation and security compliance behavior. They also reported that employees who perceive high levels of job insecurity exhibit higher levels of work-related injuries and accidents [28]. Guo et al. (2019) verified the direct relationship between job insecurity and security performance based on the conservation of resources theory and demonstrated that job insecurity significantly and negatively predicts the security performance of high-speed railway drivers [29]. Therefore, this article believes that miners may reduce their security contributions to the organization when dealing with job insecurity; that is, the stronger the job insecurity, the more likely it is to cause behavioral accidents. Therefore, the following hypothesis is proposed:

H1. 

The sense of job insecurity among miners is directly and significantly negatively correlated with their safety behavior.

Psychological resilience, as the ability for individuals to maintain balance, maintain control, regulate adversity, and continue to act in a positive state [30], has received in-depth research and widespread attention from domestic and foreign researchers in the “stress–behavior” mechanism [31] and has drawn many valuable conclusions. The study of Rice et al. (2016) suggests that psychological resilience is not only the ability of employees to recover from negative situations but also positively influences their sense of responsibility [32]. Shatté et al. (2018) found that in the context of work difficulties, employee psychological resilience has a significantly positive impact on job performance. That is, at the same level of pressure, employees with higher levels of psychological resilience perceive and react to stress relatively less and are able to convert stress into motivation to increase job performance, thus confirming the “inverted U” effect of stress on performance [33]. The study by Crane et al. (2017) confirmed that psychological resilience plays a mediating role in the relationship between obstructive stressors such as organizational and political conflict and occupational stress, indicating that psychological resilience helps reduce the negative impact of obstructive stressors and other uncontrollable stress factors on employees. Various studies under different contexts and boundaries have proven that psychological resilience can influence employees’ behavioral performance and make individual behavior choices toward directions that are beneficial to the organization [34]. In the field of coal mine security management, employee safety behavior is the guarantee that the enterprise will produce safely and reliably. A sense of job insecurity can cause instability in the psychological resilience of miners and have an impact on their behavior. The safety behavior of miners is also influenced by psychological characteristics. The higher the level of psychological resilience of miners, the more positive their attitude toward negative pressure and negative emotions will be, and this will have a certain impact on their behavior. Therefore, based on the above analysis, it is believed in this study that psychological resilience is directly affected by job insecurity and will further affect miner safety behavior; thus, the following hypotheses were proposed:

H2. 

Psychological resilience mediates the relationship between job insecurity and miner safety behavior.

H2a. 

Psychological resilience mediates the relationship between job loss security and miner safety behavior.

H2b. 

Psychological resilience mediates the relationship between job performance insecurity and miner safety behavior.

H2c. 

Psychological resilience mediates the relationship between interpersonal insecurity and miner safety behavior.

A security culture refers to the collective understanding of an organization’s security performance formed by its members through education, training, communication, leadership guidance, and other means [35]. The behavior exhibited by miners is not only driven by individual development needs, but also by input from the surrounding environment. The security operating procedures and management system, security training system, security supporting facilities, and security communication among team members in the organization can effectively promote the psychological resilience level of miners. A good safety climate can convey the correct security concepts to employees, enhance their security awareness and proactive safety, and promote safe behavior among miners. For construction companies, enhancing security culture and creating a good safety climate are important conditions for promoting safe behavior among construction workers and reducing accident rates [36]. Although some scholars are aware of the positive role of team safety climate in reducing miners’ job insecurity and shaping a corporate safety climate in changing miners’ psychological resilience [37], few scholars have conducted an in-depth exploration of the interaction among these three factors. Based on the existing [38] analysis and observations of reality, this article takes the safety climate as a moderating variable and further analyzes the moderating role of the safety climate between the two. Therefore, the following research hypotheses are proposed:

H3. 

Team safety climate plays a positive regulatory role between job insecurity and psychological resilience.

H3a. 

Team safety climate plays a positive moderating role between job insecurity and psychological resilience.

H3b. 

Team safety climate plays a positive moderating role between job insecurity and psychological resilience in job performance.

H3c. 

Team safety climate plays a positive regulatory role between interpersonal insecurity and psychological resilience.

H4. 

The moderating role of team safety climate on the relationship between job insecurity and miner safety behavior.

H4a. 

Team safety climate plays a positive regulatory role between job insecurity and miner safety behavior.

H4b. 

Team safety climate plays a positive regulatory role between job insecurity and miner safety behavior in work performance.

H4c. 

Team safety climate plays a positive moderating role between interpersonal insecurity and miner safety behavior.

H5. 

Team safety climate plays a moderating role in the mediating effect of psychological resilience between job insecurity and miner safety behavior.

H5a. 

The team safety climate plays a moderating role in the mediating effect of psychological resilience on the relationship between job insecurity and miner safety behavior.

H5b. 

Team safety climate plays a moderating role in the mediating effect of psychological resilience on the relationship between job insecurity and miner safety behavior.

H5c. 

Team safety climate plays a moderating role in the mediating effect of psychological resilience on the relationship between interpersonal insecurity and miner safety behavior.

In summary, this study proposes a hypothesis model for the job insecurity, team safety atmosphere, psychological resilience, and safety behavior of front-line miners in coal mining enterprises. The hypothesis model is shown in Figure 1.

3. Research Objects and Questionnaire

3.1. Research Objects

The intelligent transformation and functional changes in coal mines have caused significant changes in the number and scope of responsibilities of workers on the mining surface, so some workers on the mining surface were selected as the research objects. The data mainly comes from male miners working in a mine in Lvliang City, Shanxi Province, mainly engaged in underground machine mining, prospecting, blasting, support, ventilation, tile inspection, and other work, and does not involve functional departments. To ensure the reliability of the survey data, multi-stage sampling and anonymous answering were adopted in the sample selection of this questionnaire survey. The researchers explained the meaning of job insecurity, team safety atmosphere, psychological resilience, safety behavior, etc., in advance, and then the miners answered the questions without contact. The time for filling in the questions was chosen to be during the regular meeting before the shift (after filling in the questions, the envelope was sealed and delivered to the collection box). At the same time, in order to eliminate the possible sensitive psychology of the miners and avoid the common method variation caused by the single questionnaire survey method, this survey used a two-stage survey method. To ensure the reliability of the survey data, all questionnaires were filled out anonymously. The questionnaires were mainly distributed and collected through field research, and no intervention was made during the scale-filling process (except for guidance). A total of 600 questionnaires were distributed. After excluding invalid questionnaires, 481 valid questionnaires were collected, with an effective rate of 80.2%.

As shown in

Table 1. Basic characteristics of survey subjects.

Variables	Category	Frequency	Percentage
Age	≤30 years	118	24.5
	31–40 years	164	34.1
	41~50 years	168	34.9
	≥51 years	31	6.6
Years of Service	≤5 years	103	21.4
	6~10 years	166	34.5
	11~20 years	186	38.6
	≥21 years	26	5.5
Educational level	Junior high school and below	48	9.8
	High school or vocational school	151	31.4
	Associate Degree	154	32.1
	Bachelor’s degree and above	128	26.6
Job Type	Excavation worker	54	11.4
	Maintenance worker	82	17
	Support worker	107	22.3
	Safety officer	200	41.5
	Other	38	7.9
Marital Status	Unmarried	312	64.8
	Married	169	35.2

, demographic characteristics showed that 58.6% of miners were under 40 years old, with those aged 30–50 accounting for 69%, indicating that the coal mining workforce is getting younger. However, due to the nature of the work, the majority of miners are still older miners. Employees with college or bachelor’s degrees account for more than 50%, and the proportion of high-quality talent in coal mining enterprises has increased. The data on work types showed that tunneling workers, support workers, and other frontline labor types comprised 11.4%, 17%, and 22.3%, while safety personnel and other roles exceeded 49%, mainly due to the introduction of intelligent equipment and integrated large-scale mining equipment, reducing hazardous positions and increasing safety inspection, monitoring, and management roles. Other roles included tunnel monitoring staff and automation equipment supervisors. With the ongoing intelligent transformation, coal mining companies are increasingly recruiting highly educated and skilled personnel. The demographic data of the research sample aligns with the early-stage characteristics of intelligent transformation, offering representativeness in this context.

3.2. Scale Design

The main scales used in this study include the job insecurity scale, psychological resilience scale, team safety climate scale, and safety behavior scale. Excluding part one of the questionnaire, all scales were scored using the Likert five-point scoring method, with options ranging from 1 to 5, representing completely inconsistent, somewhat inconsistent, uncertain, basically consistent, and completely consistent, respectively.

The first part included the research objectives, specific requirements for the questionnaire (requiring answers to each question), gratitude to the respondents, confidentiality commitment from the researchers to the respondents, and contact information of the researchers. Meanwhile, a demographic survey questionnaire was set up, which included basic individual characteristics of miners such as gender, age, length of service, occupation, education level, marital status, etc.

The second part was the questionnaire for measuring miners’ job insecurity. Based on the reference to the relevant work insecurity scale and combined with the actual situation of the coal mining industry, this scale was modified [39]. The scale consisted of three dimensions, including job loss insecurity, job performance insecurity, and interpersonal insecurity, with a total of 12 items. For example, “I am worried that my work ability does not meet the development requirements of the coal mine”, “I worry that I can be easily replaced by others in my position at the coal mine”, etc. The higher the score, the stronger the miner’s perception of unsafe work.

The third part was the psychological resilience scale. Using the Conner–Davison scale [40], Pang Xiaohua and others [41] found that the application of this scale in the Chinese coal mine worker population has good reliability and validity. Six questions were selected based on the current situation of the miners. Such as “After encountering setbacks, I can recover quickly”. The higher the score, the higher the miner’s level of psychological resilience.

The fourth part was the miner safety behavior scale. The miner safety behavior scale mainly refers to Qu Ting’s [42,43,44] Risk Behavior Scale, selecting three dimensions including personal security quality, job characteristics, and safe management, with a total of 11 questions. Such as “During the work process, I do not consider safety as the top priority”, “I have not mastered all safety operation procedures”. The higher the score, the more likely risky behavior was to occur.

The fifth part was the team safety climate scale. The main definition of safety climate is based on Zohar (1980): safety climate is the individual psychological perception of employees in the organization, and it is the employees’ cognitive evaluation of the organization’s emphasis on security. Based on Zohar’s (1980) research, a scale with 10 items was selected, combining the basic characteristics of miners and their actual working conditions. There are a total of 36 questions in the scale, excluding basic information. Such as “My direct supervisor ensures that we have safety equipment while working”, “My direct supervisor often checks whether we comply with safety regulations”. The higher the score, the better the team safety atmosphere. After consulting with experts in the coal mining industry and making repeated revisions, it was put into use while control variables such as miner’s age, years of experience, and education level were included.

4. Data Analysis and Results

4.1. Data Analysis Based on Structural Equation Modeling

This research method is based on the views of Hair et al. [45]. PLS-SEM (partial least squares structural equation modeling) is more suitable for complex models than covariance SEM, as it can estimate the explanatory power within samples and predictive ability outside samples. It is also suitable for exploring or extending theoretical models; therefore, this study uses SmartPLS4 to construct PLS-SEM to validate hypotheses.

• Common Method Bias

Considering that it may not be feasible to completely eliminate common method bias, for the data analysis stage, two methods, namely the Harman single factor test (Podsakoff, 2003) and the full collinearity assessment approach by Kock (2017), were used to assess whether there was a common method problem in the questionnaire survey data.

(1)

Harman’s Single-Factor method

Harman’s single-factor method was used for testing. All items involved in the scale were included in the factor analysis. Through principal component analysis, a total of eight common factors with eigenvalues greater than 1 were obtained, and the cumulative variance explained was 65.713%. Among them, the contribution rate of the first unrotated factor variance was 28.664%, which was less than 50%, indicating that this study did not have a serious common method bias.

(2)

Full Collinearity Assessment Approach

Referring to the method of Kock (2017), a method based on collinearity tests was used to identify common method biases, namely when the variance inflation factor (VIF) of endogenous variables was less than 3.3, there was no serious common method bias in the study. The maximum VIF value of the endogenous variables (Inner Model) in this study was 1.564, indicating that there was no serious common method bias in this study.

2.

Reliability and validity analysis

The questionnaire data were input into SPSS 24.0 for reliability analysis, obtaining Cronbach’s α coefficients of each variable, as shown in

Table 2. Questionnaire mean, standard deviation, and Cronbach’s α coefficient.

Variable	Cr	AVE	Cronbach’s Alpha Coefficient
JLI	0.897	0.684	0.846
JPI	0.865	0.617	0.792
II	0.901	0.695	0.854
PR	0.895	0.586	0.858
TSC	0.953	0.668	0.945
PSQ	0.880	0.647	0.818
JC	0.857	0.667	0.751
SM	0.878	0.644	0.815
MSB	0.859	0.637	0.859

Note: Job loss insecurity (JLI), job performance insecurity (JPI), interpersonal insecurity (II), personal security quality (PSQ), job characteristics (JC), safe management (SM), psychological resilience (PR), miner safety behavior (MSB), team safety climate (TSC).

. Cronbach’s alpha coefficients for each first-order variable were all above 0.7 (minimum value = 0.751), indicating good internal consistency and stability of the designed scale, as well as high reliability. The composite reliability (CR) indicators were all above 0.7 (minimum value = 0.857), indicating good reliability. The average variance extracted (AVE) for the construct was above 0.5 for all variables (minimum value = 0.586), indicating good convergent validity. In addition, Cronbach’s alpha coefficient for the second-order variable of miner safety behavior was 0.859, the CR was 0.840, and the AVE was 0.637, all meeting the requirements, indicating good reliability and validity of the second-order variable in this study.

In addition, the Heterotrait–Monotrait Ratio (HTMT) was employed to test discriminant validity, with results shown in

Table 3. Discriminant validity test of first-order constructs.

	1	2	3	4	5	6	7	8
1JLI	0.827	0.613	0.479	0.574	0.326	0.479	0.545	0.418
2JPI	0.501	0.785	0.431	0.520	0.208	0.420	0.503	0.363
3II	0.410	0.355	0.834	0.479	0.271	0.379	0.458	6.21
4PR	−0.490	−0.430	−0.412	6.21	0.405	0.559	0.520	0.403
5TSC	−0.292	−0.181	−0.244	0.366	0.817	0.234	0.358	0.290
6PSQ	−0.399	−0.338	−0.318	0.469	0.207	0.804	0.570	0.545
7JC	−0.434	−0.388	−0.369	0.417	0.304	0.453	0.817	0.597
8SM	−0.348	−0.293	−0.283	6.21	0.258	0.447	0.472	0.802

: the HTMT ratios between each construct were less than 0.85, and the confidence intervals of the HTMT statistics, tested by the Bootstrap method, did not include 1, indicating that the variables in this study can be well distinguished. The Fornell–Larcker criterion was also used to test the discriminant validity between variables. By comparing the square root of the average variance extracted (AVE) of each variable in this study, it was found that they were all greater than the correlation coefficients between variables, indicating that there was good discriminant validity among the variables in this study.

3.

Hypothesis testing

AMOS 24.0 was used to build an SEM model of miners’ sense of job insecurity and safety behavior during the transformation period of coal mines, as shown in Figure 2. LJI1~LJI4, JPI1~JPI4, and II1~II4 were observed variables for job loss insecurity, job performance insecurity, and interpersonal insecurity. PSQ1~PSQ4, JC1~JC3, and SM1~SM4 were observed variables for personal security quality, job characteristics, and safe management. A test for collinearity of the model was conducted, and the analysis results are shown in

Table 4. Results of structural equation modeling study.

Path	Std. Estimate	S.E.	t	p	95%CI		f2	VIF	H	Results
					Lower	Upper
PR: R2 = 0.469, AdjR2 = 0.461; Q2 = 0.265
JLI → PR	−0.173	0.049	3.527	<0.001	−0.262	−0.074	0.036	1.582
JPI → PR	−0.168	0.043	3.898	<0.001	−0.247	−0.079	0.038	1.411
II → PR	−0.122	0.039	3.135	0.002	−0.195	−0.043	0.021	1.311
TSC → PR	0.186	0.044	4.220	<0.001	0.094	0.268	0.057	1.138
TSC × JLI → PR	0.129	0.042	3.085	0.002	0.046	0.209	0.023	2.382	H3a	Support
TSC × JPI → PR	0.157	0.045	3.527	<0.001	0.070	0.245	0.036	1.723	H3b	Support
TSC × II → PR	0.046	0.040	1.166	0.244	−0.034	0.121	0.004	1.941	H3c	Not support
MSB: R2 = 0.394, AdjR2 = 0.384; Q2 = 0.157
JLI → MSB	−0.188	0.058	3.237	0.001	−0.307	−0.079	0.036	1.638	H1a	Support
JPI → MSB	−0.152	0.057	2.689	0.007	−0.263	−0.042	0.026	1.464	H1b	Support
II → MSB	−0.124	0.048	2.558	0.011	−0.213	−0.022	0.019	1.339	H1c	Support
PR → MSB	0.203	0.083	2.443	0.015	0.042	0.367	0.036	1.883
TSC → MSB	0.118	0.052	2.283	0.022	0.017	0.219	0.019	1.203
TSC × JLI → MSB	−0.022	0.059	0.379	0.705	−0.138	0.094	0.001	2.437	H4a	Not support
TSC × JPI → MSB	0.141	0.061	2.330	0.020	0.016	0.253	0.024	1.784	H4b	Support
TSC × II → MSB	−0.007	0.053	0.130	0.897	−0.110	0.099	<0.001	1.948	H4c	Not support

Note: Job loss insecurity (JLI), job performance insecurity (JPI), interpersonal insecurity (II), psychological resilience (PR), miner safety behavior (MSB), team safety climate (TSC).

. The maximum variance inflation factor (VIF) for the predictor variables in the model was 2.437, which was less than 5, indicating no significant collinearity among the endogenous variables. From this, it can be seen that the model fits the data well and has a certain degree of adaptability.

After resampling the model using the Bootstrap method 5000 times, the results of the path significance test are shown in the table above: among them, the job loss insecurity, job performance insecurity, and interpersonal insecurity all had a significant negative impact on miners safety behavior (p < 0.05), with standardized coefficients β of −0.188, −0.152, and −0.124, respectively, thus supporting hypotheses H1, H1a, H1b, and H1c.

The results of the inspection showed that job loss insecurity, job performance insecurity, and interpersonal insecurity all had a significant negative impact on psychological resilience (p < 0.05), with standardized coefficients β of −0.173, −0.168, and −0.122, respectively. Psychological resilience had a significant positive impact on miner safety behavior (p = 0.022, β = 0.203).

4.

Mediation effect test

Using the bias-corrected nonparametric percentile bootstrap method to estimate the 95% confidence interval of the mediated effect obtained by the coefficient product method, if the 95% confidence interval did not include 0, it indicated that the mediated effect was significant. As shown in

Table 5. Mediation effect testing.

Path		Std. Estimate	S.E.	95% Confidence Interval		H	Results
				Lower	Upper
JLI → MSB	Total Effect	−0.223	0.058	−0.338	−0.115
	Direct Effect	−0.188	0.058	−0.307	−0.079
	Indirect Effect	−0.035	0.018	−0.080	−0.007	H2a	support
JPI → MSB	Total Effect	−0.186	0.052	−0.285	−0.086
	Direct Effect	−0.152	0.057	−0.263	−0.042
	Indirect Effect	−0.034	0.016	−0.075	−0.008	H2b	support
II to MSB	Total Effect	−0.148	0.045	−0.232	−0.055
	Direct Effect	−0.124	0.048	−0.213	−0.022
	Indirect Effect	−0.025	0.013	−0.058	−0.005	H2c	support

Note: Job loss insecurity (JLI), job performance insecurity (JPI), interpersonal insecurity (II), miner safety behavior (MSB).

, the analysis results indicated that for the mediating effect of psychological resilience on the relationship between job loss insecurity and miner safety behavior, the 95% confidence interval was [−0.080, −0.007], which did not include 0. This suggested that there was a significant mediating effect of psychological resilience on the relationship between job loss insecurity and miner safety behavior in this study, with a standardized effect size of −0.035. Hypothesis H2a was thus supported. Similarly, it could be inferred that there was a significant mediating effect of psychological resilience on the relationship between job performance insecurity, interpersonal insecurity, and miner safety behavior, with 95% confidence intervals of [−0.075, −0.008] and [−0.058, −0.005], respectively. The standardized mediating effect sizes were −0.034 and −0.025. Hypotheses H2b and H2c were thereby supported.

5.

Testing the moderating effect of team safety climate

The results of the model analysis show that the interaction between team safety climate and job loss insecurity (TSC × JLI) had a significant positive impact on psychological resilience (p = 0.002, β = 0.129). This means that the team safety climate in this study had a significant positive moderating effect on the relationship between job loss insecurity and psychological resilience, supporting hypothesis H3a. To better illustrate the moderating effect, following the suggestion of Aiken and West (1991), the moderation effect decomposition diagram is shown in Figure 3a. Similarly, it can be inferred that the interaction between team safety climate and job performance insecurity (TSC × JPI) had a significant positive impact on psychological resilience (p < 0.001, β = 0.157), indicating that team safety climate in this study had a significant positive moderating effect on the relationship between job performance insecurity and psychological resilience, supporting hypothesis H3b. The moderation effect decomposition diagram is shown in Figure 3b. Team safety climate had a significant positive moderating effect on the relationship between job performance insecurity and miner safety behavior (p = 0.020, β = 0.141), supporting hypothesis H4b. The moderation effect decomposition diagram is shown in Figure 3c.

In addition, the results showed that the team safety climate had no significant moderating effect on the relationship between interpersonal insecurity and psychological resilience (p = 0.244); the team safety climate had no significant moderating effect on the relationship between job loss insecurity and miner safety climate (p = 0.705); the team safety climate had no significant moderating effect on the relationship between interpersonal insecurity and miner safety climate (p = 0.897); hypotheses H3c, H4a, and H4c were not supported.

Using the PROCESS program in SmartPLS 4.0, the moderating effect of team safety climate on the mediating effect of psychological resilience on the relationship between job loss insecurity, job performance insecurity, interpersonal insecurity, and miner safety behavior was analyzed. As shown in

Table 6. Test of moderated mediation effect.

Mediation Path	Condition	Std. Estimate	S.E.	95% Confidence Interval		H	Results
				Lower	Upper
JLI → PR → MSB	High (+SD)	−0.009	0.016	−0.053	0.016
	Median (0)	−0.035	0.018	−0.081	−0.008
	Low standard deviation	−0.062	0.027	−0.124	−0.014
	Index of Moderated Mediation	0.026	0.014	0.006	0.063	H5a	support
JPI → PR → MSB	High (+SD)	−0.001	0.015	−0.032	0.031
	Median (0)	−0.034	0.016	−0.075	−0.009
	Low standard deviation	−0.066	0.029	−0.131	−0.017
	Index of Moderated Mediation	0.033	0.016	0.008	0.075	H5b	support
II → PR → MSB	High (+SD)	−0.015	0.014	−0.057	0.004
	Median (0)	−0.025	0.013	−0.058	−0.005
	Low standard deviation	−0.034	0.018	−0.08	−0.008
	Index of Moderated Mediation	0.009	0.01	−0.004	0.036	H5c	Not supported

Note: Job loss insecurity (JLI), job performance insecurity (JPI), interpersonal insecurity (II), psychological resilience (PR), miner safety behavior (MSB).

, the 95% confidence interval for the moderation-mediated effect coefficient of team safety climate on the relationship between job loss insecurity and miner safety behavior through psychological resilience was [0.006, 0.063], which did not include 0. This indicates that team safety climate had a significant moderating effect on the relationship between job loss insecurity and miner safety behavior through psychological resilience. Hypothesis H5a was supported. Similarly, it can be known that there was a significant moderating effect of team safety climate on the mediating role of psychological resilience between job performance insecurity and miner safety behavior (95% = [0.008, 0.075], IMM = 0.033), supporting hypothesis H5b; the moderating effect of team safety climate on the mediating role of psychological resilience between interpersonal insecurity and miner safety behavior was not significant (95% = [−0.004, 0.036]), the confidence interval included 0, rejecting hypothesis H5c.

4.2. Data Analysis Based on Fuzzy Set Qualitative Comparative Analysis (fs QCA)

With limitations, traditional structural equation models cannot reveal the multiple concurrent causal relationships of numerous influencing factors on miner safety behavior and their asymmetry in influencing miner safety behavior, as well as the causal complexity issues of multiple equivalent schemes for high miner safety behavior formation. Given the comprehensiveness and complexity of the influencing factors of miner safety behavior and its own complex characteristics, it is necessary to use non-heap research methods to explore the complex impact relationships among the influencing factors of miner safety behavior. Therefore, this article further analyzes the complex relationship affecting miner safety behavior using fuzzy set qualitative comparative analysis (fs QCA).

As the kernel of the fsQCA method is Boolean operations, uncalibrated raw data cannot be directly subjected to Boolean operations. Therefore, in the study, the direct calibration method was used first, and the sample statistics of the antecedent conditions and results were calibrated using the 95-50-5 calibration method; that is, the 95th percentile, median, and 5th percentile were set as the complete membership, crossover point, and completely non-membership calibration anchors. In addition, to avoid cases being deleted due to the exact 0.50 membership in the antecedent conditions or consequent set, a constant of 0.001 was added to all conditions and results with a membership of 0.50 [37,38] below. The calibration anchor points and descriptive statistics of the target set are shown in

Table 7. Variable description and data calibration.

	Descriptive Analysis				Fuzzy Set Calibration
	Minimum Value	Maximum Value	Mean	Standard Deviation	Completely Subordinate (95%)	Intersection (50%)	Not Affiliated at All (5%)
Result variable
Miner safety behavior	1.000	5.000	3.470	0.553	4.414	3.444	2.725
Condition variable
Job loss insecurity	1.000	5.000	6.21	0.741	4.250	2.750	1.500
Job performance insecurity	1.000	4.750	2.569	0.875	4.000	2.750	1.000
Interpersonal insecurity	1.000	5.000	2.211	0.953	4.000	2.000	1.000
Psychological resilience	1.000	5.000	3.674	0.687	5.000	3.667	2.833
Team safety climate	1.000	5.000	3.732	0.779	5.000	6.21	2.200

.

Secondly, to further examine the necessity of job loss insecurity, job performance insecurity, interpersonal insecurity, psychological resilience, and team safety climate in explaining miner safety behavior, and then determine whether the generation of miner safety behavior depends on a single conditional variable, in the thesis, the “NCA” package in R software was called, and necessity test used two methods, ceiling regression (CR) and ceiling envelopment (CE), were used. Furthermore, the CR method was applied to analyze the bottleneck level of miner safety behavior. In addition, the necessity of individual conditions for miner safety behavior was tested for robustness using QCA4.1. Consistency could reflect the degree to which these five independent variables were necessary conditions for miner safety behavior; coverage could reflect how many samples could explain the existence of these necessary conditions. To ensure the accuracy of the empirical conclusions, this study referred to previous research and set the consistency threshold at 0.9. The necessary condition analysis results are shown in

Table 8. Analysis of the necessity of antecedent conditions.

	High Miner Safety Behavior		Non-High Miner Safety Behavior
	Consistency	Coverage	Consistency	Coverage
High job loss insecurity	0.545	0.581	0.732	0.814
Not-high job loss insecurity	0.825	0.746	0.623	0.589
High job performance insecurity	0.547	0.569	0.735	0.799
Not-high job performance insecurity	0.806	0.745	0.603	0.582
High interpersonal insecurity	0.564	0.564	0.745	0.779
Not-high interpersonal insecurity	0.779	0.745	0.583	0.583
High psychological resilience	0.760	0.792	0.540	0.588
Not-high psychological resilience	0.605	0.557	0.810	0.779
High team safety climate	0.772	0.750	0.607	0.616
Not-high team safety climate	0.605	6.21	0.753	0.776

, where “~” represents “not”.

The results in the table above show that the necessary consistency of individual conditions for high/not-high miner safety behavior in this study is generally low (all less than 0.9), and they are not necessary conditions for high/not-high miner safety behavior, which was consistent with the results of the NCA, indicating that job loss insecurity, job performance insecurity, interpersonal insecurity, psychological resilience, and team safety climate are not necessary conditions for high/not-high miner safety behavior.

In order to further analyze the explanatory power of the comprehensive correlation between multiple independent variables and miners’ safety behaviors and explore the configuration path that affects safety behaviors, the configuration that generated high/not-high miner safety behavior, as well as the synergistic relationship between the various conditional variables within the configuration were analyzed. Based on simple solutions, intermediate solutions, and complex solutions, the configuration was named and explained. Through the combination of theory and practice, it was clarified that job loss insecurity, job performance insecurity, interpersonal insecurity, and the adaptation and substitution relationship between psychological resilience and team safety climate are the causes of high/not-high miner safety behavior.

Specifically, the fsQCA method was applied to analyze the configuration of job loss insecurity, job performance insecurity, interpersonal insecurity, psychological resilience, and team safety climate that lead to high/not-high miner safety behavior. Due to the inconsistent research conclusions of the correlation between job loss insecurity, job performance security, interpersonal insecurity, psychological resilience, and team safety climate and miners’ safety behavior, it was hypothesized in the study that the job loss insecurity, job performance insecurity, interpersonal insecurity, psychological resilience, and team safety climate, individually or collectively, may constitute one of the antecedents of high/not-high miner safety behavior in counterfactual analysis.

In addition, the core and boundary conditions of each configuration (intermediate solution) were further distinguished by comparing the intermediate solution with the simplified solution. The conditions that appear in both solutions are core conditions, and the conditions that only appear in the intermediate solution are boundary conditions. The conditional variables in the configuration are expressed in three forms: present (●) or (■), missing (U), and optional (not care). There are five states of conditional variables in the configuration, namely, the presence of core conditions (●), the presence of boundary conditions (■), the absence of core conditions (⃝), the absence of boundary conditions (☐), and optional (not care). The presence or absence of these conditions has no impact on the outcome.

Configuration S1: Core driving type of work insecurity and psychological resilience. The configuration is based on the core missing conditions of job loss insecurity, job performance insecurity, interpersonal insecurity, and the core existence condition of psychological resilience. This means that under the conditions of low job loss insecurity, low job performance insecurity, low interpersonal insecurity, and high psychological resilience, high miner safety behavior can be produced.

Configuration S2: Core driving type of job insecurity and team safety climate. The configuration is based on the core missing conditions of job loss insecurity and interpersonal insecurity, the edge missing condition of job performance insecurity, and the core existence condition of team safety climate. This means that under the conditions of a high team safety climate, low job loss insecurity, and low interpersonal insecurity, accompanied by low job performance insecurity, it is possible to produce high miner safety behavior.

Configuration S3: Core driving factors of job loss insecurity, interpersonal insecurity, and team safety climate. The configuration is based on the core missing conditions of job loss insecurity and interpersonal insecurity, the core existence condition of team safety climate, and the marginal existence condition of psychological resilience; it means that under the conditions of low job loss insecurity, low interpersonal insecurity, and high team safety climate, accompanied by high psychological resilience, the result of high miner safety behavior can be produced.

As shown in

Table 9. Configuration adequacy inspection.

	High Miner Safety Behavior			Non-High Miner Safety Behavior
	S1	S2	S3	NS1	NS2	NS3	Y20
Job loss insecurity	⃝	⃝	⃝	●	●		●
Job insecurity	⃝	☐			●	●	●
Interpersonal insecurity	⃝	⃝	⃝			●	●
Psychological resilience	●		■	⃝			⃝
Team safety climate		●	●	⃝	⃝	⃝
Original coverage	0.562	0.527	0.514	0.590	0.552	0.535	0.525
Real coverage	0.092	0.056	0.044	0.051	0.005	0.047	0.045
Consistency	0.902	0.890	0.917	0.912	0.928	0.926	0.919
Overall solution coverage	0.662			0.695
Consistency of the overall solution	0.881			0.887

Note: ● indicates the presence of core conditions, ■ indicates the presence of marginal conditions, ⃝ indicates the absence of core conditions, ☐ indicates the absence of marginal conditions. High miner safety behavior (case threshold = 5; original consistency threshold (raw) = 0.80, inconsistency threshold (PRI) = 0.60); non-high miner safety behavior (case threshold = 5; original consistency threshold (raw) = 0.80, inconsistency threshold (PRI) = 0.65).

, there are four configurations that can generate non-high mining worker safety behaviors. The overall consistency of their solutions is 0.887, and the overall coverage of their solutions is 0.695.

Configuration NS1 means that under conditions of high job loss insecurity, low psychological resilience, and low team safety climate, the result of miner safety behavior is not high.

Configuration NS2 means that under conditions of high job loss insecurity, high job performance insecurity, and low team safety climate, the resulting miner safety behavior will not be high.

Configuration of NS3 means that under conditions of high job insecurity, high interpersonal insecurity, and low team safety climate, the resulting safety behavior of miners will not be high.

Configuration NS4 means that under conditions of high job loss insecurity, high job performance insecurity, high interpersonal insecurity, and low psychological resilience, the resulting miner safety behavior will not be high.

5. Conclusions and Recommendations

From the above data analysis, it can be seen that the SEM and fsQCA methods used in this article not only verified the correlation between various factors and miners’ safety behavior but also identified the primary and secondary factors related to safety behavior. Compared with other methods, it can more accurately analyze the factors related to miners’ safety behavior. So this article discusses the actual situation of enterprise security management and puts forward the following suggestions:

(1)

The job insecurity of miners and its various dimensions (job loss insecurity, job performance insecurity, interpersonal insecurity) are significantly negatively correlated with security behavior. In other words, the more intense the sense of job insecurity among miners is, the greater the likelihood that they will engage in unsafe behaviors. Compared with research on other miners’ job insecurities, the loss of insecurity and interpersonal insecurity of miners is more prominent, reflecting that after entering a new stage of intelligent coal mining, miners’ job insecurity has not decreased. On the contrary, it has been exacerbated by psychological insecurity caused by changes in personnel and functions. Therefore, coal mining enterprises should strengthen the establishment of effective feedback mechanisms so that team members can receive feedback timely, provide more detailed and systematic training on miner functions, improve work transparency, and enhance communication effectiveness so that miners can trust the company from multiple aspects, and improve the resistance of miners’ sense of insecurity, thereby reducing the risk of bad behaviors for miners.

(2)

Psychological resilience plays a mediating role between job insecurity and safety behavior among miners. Among the current frontline miners, those with high levels of psychological resilience are more likely to regulate and recover from negative emotions or unexpected situations through self-control, which manifests as calmness while facing adversity and ultimately reduces the occurrence of coal mining accidents. The results indicate that in coal mine safety, management should pay attention to the cultivation of miners’ psychological resilience, strengthen communication with miners, improve miners’ psychological resilience level by providing training and psychological counseling, and thereby alleviate the adverse effects of job insecurity on miner safety behavior.

(3)

The regulatory role of team safety climate. In the context of intelligent transformation in a team with a good safety climate, the mediating effect of psychological resilience on the relationship between job insecurity and safety behavior is more significant. A good team security atmosphere can enhance the psychological toughness of the miners, thereby further weakening the negative correlation between the sense of insecurity and security behavior. By establishing team security principles, promoting interaction and communication within the team, enhancing the psychological resilience of team members, creating a positive team safety climate, promoting mutual assistance and support among miners, and enabling psychological resilience to play a better role, overall safety behavior can be further improved.

6. Insufficient Research and Outlook

Although some important conclusions have been obtained in the process of studying job insecurity and the correlation of the safety behavior of miners, with psychological resilience as the mediating variable and team safety climate as the moderating variable, there are still some shortcomings and many areas worth exploring in future research. The research may be limited to specific mining areas or companies, and the representativeness of the samples may be insufficient, limiting the generalizability of the conclusions. Future research should expand the sample range to cover different regions and different types of mining enterprises in order to improve the wide applicability of the research results. Most studies may adopt a cross-sectional design, which cannot fully reveal the dynamic changes and causal relationships between variables such as job insecurity, safety behavior, and psychological resilience. In the future, longitudinal research designs can be used to better understand the long-term effects and trends between these variables, further deepening the understanding of the mechanisms that influence miner safety behavior and providing more comprehensive and scientific guidance for improving mine security management.