# Interactions among Cognitive Factors Affecting Unsafe Behavior: Integrative Fuzzy DEMATEL ISM Approach

Authors: Mahnaz Shakerian; Alireza Choobineh; Mehdi Jahangiri; Moslem Alimohammadlou; Mohammad Nami; Jafar Hasanzadeh
Journal: Mathematical Problems in Engineering
 (2020)

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## Abstract

The prevention of industrial accidents is not completely practical by implementing safety programs unless focusing on protecting vulnerable workers. The unsafe behavior cognitive factors (UBCFs) are essential determinants contributing to human failure. This study aimed at eliciting the most important UBCFs, along with investigating hierarchical cause-effect interactions among them. A qualitative approach using metasynthesis was utilized to extract all essential UBCFs among industrial workplaces. Afterward, the relationships between UBCFs were recognized using the fuzzy decision-making trial and evaluation laboratory (DEMATEL) method and rated by an expert panel. Also, a hierarchical model was developed based on the final matrix of DEMATEL by employing the interpretive structural modeling (ISM) method. Ten criteria were extracted as UBCFs through the metasynthesis method. The threshold value was set as 0.175 in DEMATEL following experts’ ideas. Inadequacy of persons’ resilience and habitual rule ignorance were recognized as the most important predictive UBCFs. The developed model was tested through a case study in a petrochemical company. The results of the study can be used to help industrial managers and HSE practitioners to consider workers’ capabilities either cognitively or physically when designing the required tasks to reduce unsafe behaviors. Also, the findings of the study are applicable for other researchers to prioritize the most important factors affecting unsafe behavior in different workplaces.


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## Body

## 1. Introduction

Human resource is considered as one of the most important properties of any society and as the central pillar required for continuous development. Even with considering every types of engineering progress when establishment of the facilities, human engagement is a vital factor in industrial safety when performing different sensitive tasks such as monitoring and maintenance. Worker’s errors are more likely to be occurred while interacting with equipment or machinery systems due to different reasons [1]. Human behavior modeling and prediction has become one of the most popular research topics in many industries due to emerging challenging issues on human reliability. According to the World Health Organization, 45% of the worldwide population is workforce endangered by a considerable number of various occupational illnesses and accidents [2, 3]. Moreover, industrial accidents annually assist in billions of dollars in costs, thousands of casualties, and significant losses of working time worldwide [1]. According to the literature, human errors are responsible for nearly 90% of nuclear accidents, 80% of chemical process accidents, and more than 94% of accidents in oil and gas industries in developing countries. Thus, recognizing and preventing human error is essential to decrease the probability of accidents following by financial shortcomings, environmental contaminations, and fatality in different industrial workplaces [4]. In developing countries, also, the number of accidents has been recently considerable. In Iran, only in 2016, four accidents happened only in petrochemical companies leading to both fatal and financial huge loss for the country. Human unsafe behavior was confirmed as an important factor in their occurrence after accident cause analysis [5].The ignorance of near misses and small accidents causes may lead to “black swans” in industries. The “black swan” is a metaphoric term first used by Aristotle for catastrophic events seemed impossible. Previously occurred accidents have shown that an inevitable uncertainty exists in human ability to foreseen the probability of a catastrophic event. Due to the unpredictability of the black swans in industrial safety, the question of “how an error can be prevented?” is permanently asked by safety practitioners. Murphy and Conner suggest opinions for preventing the black swan incidents. They believed that root cause analysis could discover the past unpredictable pathway to the incident and will introduce some learned lessons, where the black swan converts to a “white swan.” A white swan is an event with main features of being certain and predictable which are easily recognized. Certainly, the root cause analysis as a means for making the black swan to a white swan has revealed human unsafe behavior as the most repeated important factor in industrial accident occurrence [6, 7].Since unsafe behavior is an essential risk for workers’ health and safety, it is important to recognize it to improve system safety. When workers are involved in complicated activities in different industries, safe behavior is critical. When a front-line worker is not well aware of the working procedure or is not sufficiently trained about safety, an accident would happen [8]. Hence, an accessible way of prohibiting workplace accidents is to detect vulnerable front-line workers as the last operating layer of a working system. An essential role of predicting incidents is dedicated to individual differences, and researchers of different disciplines have paid attention to them as increasingly attractive areas (e.g., cognitivists, safety engineers, and ergonomists) [9]. Additionally, industrial safety in the workplace cannot be highly improved unless safety hazards are appropriately determined [10]. Nevertheless, the challenging issue in recognition of hazards and reporting the incidents is the dynamic circumstances of industries and the fact that the behavior of human is not predictable [11]. The recommendation of several safety researchers is that the risks corresponding to safety could be controlled by a particular safety program [12]. While safety policies and approaches (e.g., safety management instructions, supervisions, attitudes, and penalties) are effectively conducted in different industries and resulted in considerable improvements [13], the “black swan” are still taking place in some industries. However, the recognition, assessment, and prevention of hazards seem to be far-fetched unless the policy-makers are well informed about the risks at the first stage [14]. The prevention of accidents is not completely practical by the programs corresponding to safety; even though focusing on protecting vulnerable people instead of affording to discover the risks for all workers, whether the accident-prone or not, grounds a superior situation to enhance industrial safety [15].Two main methods are famous for understanding unsafe behaviors, including system approach and individual approach, which deals with the unsafe actions primarily resulted from personal considerations (e.g., attention failure, forgetfulness, and rule ignorance) while the focus of the second approach is on the workplace conditions [16]. Previous investigations have assigned that instead of regarding one of the approaches as mentioned earlier, a mutual interactive method is capable of being more helpful when referring to various variables that cause unsafe behavior [17]. It is remarked by Hollnagel that presents models investigating erroneous human behaviors principally encounter inadequate practical taxonomies because of the lack of an appropriate distinct borderline to differentiate the accident demonstrations and origins. Furthermore, following the literature, identifying erroneous human behaviors in a working system may facilitate the detection of symptoms of system defectiveness and the recovering of them before the occurrence of an accident [18]. The investigation of contributing factors of unsafe behavior seems to be a preferable replacement for other methods such as those exclusively focusing on accidents/incidents indices for monitoring the safety of workplaces, especially the performance of front-line workers with a glance on finding a permanent proactive approach; hence, the approaches in safety which are according to the behavior were lately attractive for many researchers [19].Also, in the research literature on unsafe behaviors and in the studies focused on industrial front-line safety management of workers and prediction of human error, there is a gap in specific research about cognitive factors affecting workers to participate in different types of unsafe behaviors [20]. Although the number of models established in this area is not negligible, and these models are not sometimes applicable for industries, the relationships between various factors are mostly ignored [21].An elaborate study of unsafe behavior appears to be essential in promoting the safety of system and engineering designs of industries as a prerequisite for reducing potential error opportunities and also cognitive failures [22]. Metasynthesis can be considered as a promising tool for a better understanding of employees’ behavior at workplaces considering the advantages of applying qualitative studies in extracting influential factors affecting a certain phenomenon as a problem and using its new knowledge in decision-making practically [23].Based on the previous research, out of the multicriteria decision-making (MCDM) methods, ISM and DEMATEL are well suitable for deep analysis of complicated problems with hierarchical and interactive structure. ISM provides the possibility for the factors to be divided into different clusters, and DEMATEL explores cause-effect relationship. ISM, as a holistic approach, is applied for evaluating the complicated situations, while DEMATEL is used for identifying direct and indirect relationships [24]. Moreover, according to the literature, using fuzzy set can remarkably decrease uncertainty and yield more precise results [25, 26].This study aimed at (1) extending a structure for existing UBCF representation to help preventing similar future incidents by analyzing accidents, and (2) incorporating quantitative cause-effect relationships between UBCFs as well as dependency assessment as an important point in an industrial system. Therefore, regarding the essential role of unsafe behavior cognitive factors in human-centered process tasks and to meet the initial goals considered for this study, a hybrid fuzzy DEMATEL-ISM approach was introduced. The developed model is helpful for safety practitioners in the development of accident prevention strategies for recruiters and industrial managers in the establishment of eligibility-based task designs and also for novel research studies with the purpose of designing and development of cognitive-based human behavior monitoring devices in industrial safety applications.


## 2. The Developed Methodology

### 2.1. Metasynthesis Methodology for the Extraction of Unsafe Behavior Cognitive Factors (UBCFs)

An approximately new methodology named metasynthesis assists in combining the results obtained from relevant investigations by employing different methods which induce a novel emerged interpretation of the results [27, 28]. In this method, all studies corresponding to the studied problem were analyzed, and the content validity of the methodology was evaluated using experts’ ideas according to the seven-stage model of Sandelowski et al. [29]. In this seven-stage process, at the first step, the study question containing the main idea was putting forward to be investigated. After that, all related keywords were considered as a basis for performing a systematic review, and thus, the most eligible research papers were included to be assessed in metasynthesis. After extracting the research data, the themes were analyzed and fused to make a proper illustration of the findings. The explanations of different stages of the metasynthesis method containing the way of confirming the validity and reliability of extracted criteria, and also the stages of extracting USBCFs in detail are available in another article previously published from the primary finding of the project similar to the present study [30].


### 2.2. Fuzzy DEMATEL

In fuzzy situations with decision-making issues, utilizing an expanded crisp multicriteria decision-making technique is required.


#### 2.2.1. Basic Definitions and Preliminaries

In order to clarify the integrated methodology used in the current study, some required terms are briefly described in this section.(1) Fuzzy Set. A fuzzy set A˜ is a subset of a universe of discourse X, which is defined by a membership function μN˜X depicting a mapping μA˜:X⟶0.1. The membership value, as the function value of μA˜XA˜, indicates the degree of accuracy that x is a constituent of fuzzy set A˜. It is supposed that μA˜:X∈0.1, where μA˜X=0 means that x is belonged to A˜ but not to the fuzzy set A˜ [26, 31, 32](2) Triangular Fuzzy Number. A triangular fuzzy number N˜ is referred to as a triplet l⋅m⋅u, and the membership function μN˜x can be illustrated by equation (1) as follows:(1)μN˜x=0,x<lx−lm−l,l≪x≪mu−xu−m,m≪x≪u0,x>u,where l, m, and u are real numbers and l≪m≪u [31].If assuming that a set of criteriaC = {C1, C2,..., Cn} belongs to a system, the particular pairwise relations are obtained for modeling regarding mathematical relations [31].(3) The Pairwise Comparison. The pairwise comparison may be characterized by four levels, of which the values of 0, 1, 2, and 3 correspond to “no influence,” “low influence,” ‘‘high influence,” and “very high influence,” respectively [31].(4) The Initial Direct-Relation Matrix. The initial direct-relation matrix Z is an n×n matrix determined by pairwise comparisons depending influential relations between criteria, where Zij is designated as the level to which the criterion Ci influences criterion Cj. Consequently, as equation (2) illustrates, all main determinant constituents Zij of matrix Z will be zero [31]:(2)C1C2C3,Z=c1,c2,…,cn0Z12CnZ210Z2N⋮⋮Zn1Zn20.(5) Causal Diagram. The causal diagram is obtained by depicting the pairs of (Dk + Rk, DkRk), of which the horizontal axis (D + R) is achieved by adding Rk to Dk (prominence) while the vertical axis (D-R) is obtained by subtracting Rk from Dk (prominence) [31].


#### 2.2.2. DEMATEL

An extended five-step DEMTEL was introduced by Jassbi in 2011, as follows [33]:(i)
Step 1: establishment of the direct influence matrixIn this step, triangular fuzzy numbers corresponding to five linguistic terms were utilized (Table1).Table 1
The correspondence of linguistic values and linguistic terms.
Linguistic termsTriangular fuzzy numbersNo influence (No)(0, 0, 0.25)Very low influence (VL)(0, 0.25, 0.5)Low influence (L)(0.25, 0.5, 0.75)High influence (H)(0.5, 0.75, 1)Very high influence (VH)(0.75, 1, 1)After providing the list of extracted criteria or dimensions, pairwise comparisons of every single pair of the criteria were made by experts. The evaluations and priorities of individuals regarding the causality between every single pair of the criteria were assessed via adopting fuzzy numbers contributing to a fuzzy matrix, as depicted byZ̃ p. In this step, all experts were asked to respond to a previously prepared form. Certainly, the number of fuzzy matrices is indicated by p, and that p implies to the count of experts [34]. In the following step, it is needed to obtain and average the evaluation of the priorities of experts by employing the corresponding equation [33]:(3)z˜=x˜1⊕x˜2⊕x˜3⊕⋯⊕xpp.Here,p is the number of experts and x˜1 is the pair comparison matrix of expert 1, x˜2 is the pair comparison matrix of expert 2, and x˜p is the pair comparison matrix of expert p. Moreover, z˜ is the triangular fuzzy number as z˜ij=lij′,mij′,uij′.Then, using these equations, the aggregated fuzzy matrix was developed as the initial direct-relation fuzzy matrix.(ii)
Step 2: the initial direct-relation fuzzy matrix normalizationThen, the initial direct-relation fuzzy matrix would be normalized by applying equations (4) and (5) as mentioned below to build the normalized direct-relation fuzzy matrix H:(4)H˜ij=z˜ijr=lij′r,mij′r,uij′r=lij′,mij′,uij′,wherer is as follows:(5)r=max1≤i≤n∑j=1nuij′.(iii)
Step 3: computing the total-relation fuzzy matrix (TC)Then, the total-relation fuzzy matrixTC is calculated by computing the previously made matrices, which is defined as follows [33]:(6)T=limk⟶+∞H˜1⊕H˜2⊕⋯⊕H˜k,where(7)t˜ij=lijt,mijt,uijt,(8)lijt=Hl×I−Hl−1,(9)mijt=Hm×I−Hm−1,(10)uijt=Hu×I−Hu−1.(iv)
Step 4: computing influential importance and influential directionThe result obtained by the sum of columns and rows of matrixT is computed by equations (11) and (12) in this step. Here, ri represents all indirect and direct influences caused by the criterion i to the remaining factors, while the degree of influence would be represented by cj.Wheni = j, ri + cj, it represents all impacts that are received and given by the criterion i. In other words, ri + cj indicates both the system factors’ effect on factor i and the impact of criterion i upon the total system. Therefore, the level of significance of criterion i in the entire system can be demonstrated by the indicator ri + cj. Oppositely, the net impact of criterion i on the system can be indicated by the difference between the two, ri−cj.Noteworthy, the positive value ofri−cj shows that the factor i will be a net cause while its negative value shows that the factor will be a net result clustered into effect group [35]. For each i = j, it will be(11)D˜=D˜in×1=∑j=1nT˜ijn×1,(12)R˜=R˜i1×n=∑j=1nT˜ij1×n,whereD˜ and R˜ are the matrices n×1 and 1×n, respectively.ri+dj: relationship (cause and effect) strength (e.g., the higher the value of ri + dj for a criterion, the more interactions are found between the other factors).ri−dj: cause and effect direction (e.g., if ri−dj>0, the criterion is the effect, and if ri−dj<0, the criterion is the cause).Regarding the aforementioned calculated values, the indicesri+dj and ri−dj for the criteria and the indices D˜i+R˜i and D˜i−R˜i for the dimensions should be computed.At the end of the process, all calculatedD˜i+R˜i and D˜i−R˜i would be defuzzified via an appropriate defuzzification method:(13)defuzzification=u−l+m−l3+l,whereu, l, and m refer to the triangular fuzzy numbers, respectively.(v)
Step 5: building the net relation map (NRM)In this step, the value of the threshold is calculated to determine NRM. That is, building strong relationships while ignoring very complicated ones. Only those relations with a matrixT of greater than the threshold can be included in NRM. The relations with the value lower than the threshold value are considered zero, and the corresponding cause and effect relation is eliminated. Finally, the diagram related to the cause and effect relation is illustrated by representing the dataset of ri + cj and ri − cj. Figure 1 represents the steps of the fuzzy DEMATEL approach utilized in this investigation.Figure 1
The flowchart of the fuzzy DEMATEL approach.


### 2.3. Interpretive Structural Modeling (ISM)

Warfild introduced ISM methodology in 1974, which assists in dealing with complicated issues. It is an analytical method and helps groups or individuals to develop a framework of all possible correlations between the various components that existed in a complicated system. The basic purpose of this kind of model is to use the experts’ proficiency and also their knowledge to evaluate the system problems and afterward to build a multilevel structural model [36, 37].Hence, the model constructed to demonstrate the framework of a challenging issue in a depicted hierarchical diagram [38, 39] can assist in dealing with complicated conditions, including the situation under study, a series of factors which influence the unsafe behaviors of workers. In summary, ISM gives a driver-dependency clustered graph that can contribute to an appropriate prioritization of components to allocate the available resources and much higher accurate perception of the condition instead of only a single factor to be regarded. Therefore, ISM causes new insight into emerging recent concepts toward these indirect and direct interactions and, therefore, takes the advantages of experts’ ideas to find the hierarchical interactions among the components [40].


#### 2.3.1. ISM Methodology and Model Development

The steps considered in the ISM to establish the desired hierarchical relationship are as follows [41, 42]:(1) identification of the important factors influencing the studied system, which can be extracted from different methods (e.g., literature review, metasynthesis, and brainstorming with experts). The dimensions were recognized by employing the metasynthesis method in the present study; (2) establishment of a contextual relationship of the dimensions extracted from the previous step with regard to the point that these pairs of variables would be evaluated; (3) making a structural self-interaction matrix (SSIM) for dimensions, which demonstrates pairwise relationships between dimensions of the studied system; (4) building a reachability matrix using the SSIM and reviewing the matrix for any possible indirect relationship considering an underlying assumption made in ISM; if a variable A is associated to B and also B is associated to C, then A is inevitability relevant to C; (5) establishment of various levels on the basis of the reachability matrix composited in the previous step; (6) obtaining a directed graph according to the previously observed relationships in the reachability matrix and eliminating transitive links; (7) transforming the constructed digraph into a model which is the ISM model by substitution of variable nodes with the declarations; and (8) controlling the ISM model designed in the previous step to be revised for any possible conceptual contradiction [41–44].


#### 2.3.2. Matrice d’Impacts Croisés-Multiplication Appliquée á un Classement (MICMAC)

The cross-impact matrix multiplication applied to classification is a technique to graphically categorize dimensions of a complex predicament according to their driving power and dependence power. This approach composed of three main stages such as: (1) design of important practical factors and providing a comprehensive variable list applicable for future researches and analyses; (2) developing the causal relationship between variables; and (3) identification of critical factors that are essential for possible changes of embracing system [45]. Figure 2 depicts the proposed method of the current study.Figure 2
The presented model applied in this study.


## 2.1. Metasynthesis Methodology for the Extraction of Unsafe Behavior Cognitive Factors (UBCFs)

An approximately new methodology named metasynthesis assists in combining the results obtained from relevant investigations by employing different methods which induce a novel emerged interpretation of the results [27, 28]. In this method, all studies corresponding to the studied problem were analyzed, and the content validity of the methodology was evaluated using experts’ ideas according to the seven-stage model of Sandelowski et al. [29]. In this seven-stage process, at the first step, the study question containing the main idea was putting forward to be investigated. After that, all related keywords were considered as a basis for performing a systematic review, and thus, the most eligible research papers were included to be assessed in metasynthesis. After extracting the research data, the themes were analyzed and fused to make a proper illustration of the findings. The explanations of different stages of the metasynthesis method containing the way of confirming the validity and reliability of extracted criteria, and also the stages of extracting USBCFs in detail are available in another article previously published from the primary finding of the project similar to the present study [30].


## 2.2. Fuzzy DEMATEL

In fuzzy situations with decision-making issues, utilizing an expanded crisp multicriteria decision-making technique is required.


### 2.2.1. Basic Definitions and Preliminaries

In order to clarify the integrated methodology used in the current study, some required terms are briefly described in this section.(1) Fuzzy Set. A fuzzy set A˜ is a subset of a universe of discourse X, which is defined by a membership function μN˜X depicting a mapping μA˜:X⟶0.1. The membership value, as the function value of μA˜XA˜, indicates the degree of accuracy that x is a constituent of fuzzy set A˜. It is supposed that μA˜:X∈0.1, where μA˜X=0 means that x is belonged to A˜ but not to the fuzzy set A˜ [26, 31, 32](2) Triangular Fuzzy Number. A triangular fuzzy number N˜ is referred to as a triplet l⋅m⋅u, and the membership function μN˜x can be illustrated by equation (1) as follows:(1)μN˜x=0,x<lx−lm−l,l≪x≪mu−xu−m,m≪x≪u0,x>u,where l, m, and u are real numbers and l≪m≪u [31].If assuming that a set of criteriaC = {C1, C2,..., Cn} belongs to a system, the particular pairwise relations are obtained for modeling regarding mathematical relations [31].(3) The Pairwise Comparison. The pairwise comparison may be characterized by four levels, of which the values of 0, 1, 2, and 3 correspond to “no influence,” “low influence,” ‘‘high influence,” and “very high influence,” respectively [31].(4) The Initial Direct-Relation Matrix. The initial direct-relation matrix Z is an n×n matrix determined by pairwise comparisons depending influential relations between criteria, where Zij is designated as the level to which the criterion Ci influences criterion Cj. Consequently, as equation (2) illustrates, all main determinant constituents Zij of matrix Z will be zero [31]:(2)C1C2C3,Z=c1,c2,…,cn0Z12CnZ210Z2N⋮⋮Zn1Zn20.(5) Causal Diagram. The causal diagram is obtained by depicting the pairs of (Dk + Rk, DkRk), of which the horizontal axis (D + R) is achieved by adding Rk to Dk (prominence) while the vertical axis (D-R) is obtained by subtracting Rk from Dk (prominence) [31].


### 2.2.2. DEMATEL

An extended five-step DEMTEL was introduced by Jassbi in 2011, as follows [33]:(i)
Step 1: establishment of the direct influence matrixIn this step, triangular fuzzy numbers corresponding to five linguistic terms were utilized (Table1).Table 1
The correspondence of linguistic values and linguistic terms.
Linguistic termsTriangular fuzzy numbersNo influence (No)(0, 0, 0.25)Very low influence (VL)(0, 0.25, 0.5)Low influence (L)(0.25, 0.5, 0.75)High influence (H)(0.5, 0.75, 1)Very high influence (VH)(0.75, 1, 1)After providing the list of extracted criteria or dimensions, pairwise comparisons of every single pair of the criteria were made by experts. The evaluations and priorities of individuals regarding the causality between every single pair of the criteria were assessed via adopting fuzzy numbers contributing to a fuzzy matrix, as depicted byZ̃ p. In this step, all experts were asked to respond to a previously prepared form. Certainly, the number of fuzzy matrices is indicated by p, and that p implies to the count of experts [34]. In the following step, it is needed to obtain and average the evaluation of the priorities of experts by employing the corresponding equation [33]:(3)z˜=x˜1⊕x˜2⊕x˜3⊕⋯⊕xpp.Here,p is the number of experts and x˜1 is the pair comparison matrix of expert 1, x˜2 is the pair comparison matrix of expert 2, and x˜p is the pair comparison matrix of expert p. Moreover, z˜ is the triangular fuzzy number as z˜ij=lij′,mij′,uij′.Then, using these equations, the aggregated fuzzy matrix was developed as the initial direct-relation fuzzy matrix.(ii)
Step 2: the initial direct-relation fuzzy matrix normalizationThen, the initial direct-relation fuzzy matrix would be normalized by applying equations (4) and (5) as mentioned below to build the normalized direct-relation fuzzy matrix H:(4)H˜ij=z˜ijr=lij′r,mij′r,uij′r=lij′,mij′,uij′,wherer is as follows:(5)r=max1≤i≤n∑j=1nuij′.(iii)
Step 3: computing the total-relation fuzzy matrix (TC)Then, the total-relation fuzzy matrixTC is calculated by computing the previously made matrices, which is defined as follows [33]:(6)T=limk⟶+∞H˜1⊕H˜2⊕⋯⊕H˜k,where(7)t˜ij=lijt,mijt,uijt,(8)lijt=Hl×I−Hl−1,(9)mijt=Hm×I−Hm−1,(10)uijt=Hu×I−Hu−1.(iv)
Step 4: computing influential importance and influential directionThe result obtained by the sum of columns and rows of matrixT is computed by equations (11) and (12) in this step. Here, ri represents all indirect and direct influences caused by the criterion i to the remaining factors, while the degree of influence would be represented by cj.Wheni = j, ri + cj, it represents all impacts that are received and given by the criterion i. In other words, ri + cj indicates both the system factors’ effect on factor i and the impact of criterion i upon the total system. Therefore, the level of significance of criterion i in the entire system can be demonstrated by the indicator ri + cj. Oppositely, the net impact of criterion i on the system can be indicated by the difference between the two, ri−cj.Noteworthy, the positive value ofri−cj shows that the factor i will be a net cause while its negative value shows that the factor will be a net result clustered into effect group [35]. For each i = j, it will be(11)D˜=D˜in×1=∑j=1nT˜ijn×1,(12)R˜=R˜i1×n=∑j=1nT˜ij1×n,whereD˜ and R˜ are the matrices n×1 and 1×n, respectively.ri+dj: relationship (cause and effect) strength (e.g., the higher the value of ri + dj for a criterion, the more interactions are found between the other factors).ri−dj: cause and effect direction (e.g., if ri−dj>0, the criterion is the effect, and if ri−dj<0, the criterion is the cause).Regarding the aforementioned calculated values, the indicesri+dj and ri−dj for the criteria and the indices D˜i+R˜i and D˜i−R˜i for the dimensions should be computed.At the end of the process, all calculatedD˜i+R˜i and D˜i−R˜i would be defuzzified via an appropriate defuzzification method:(13)defuzzification=u−l+m−l3+l,whereu, l, and m refer to the triangular fuzzy numbers, respectively.(v)
Step 5: building the net relation map (NRM)In this step, the value of the threshold is calculated to determine NRM. That is, building strong relationships while ignoring very complicated ones. Only those relations with a matrixT of greater than the threshold can be included in NRM. The relations with the value lower than the threshold value are considered zero, and the corresponding cause and effect relation is eliminated. Finally, the diagram related to the cause and effect relation is illustrated by representing the dataset of ri + cj and ri − cj. Figure 1 represents the steps of the fuzzy DEMATEL approach utilized in this investigation.Figure 1
The flowchart of the fuzzy DEMATEL approach.


## 2.2.1. Basic Definitions and Preliminaries

In order to clarify the integrated methodology used in the current study, some required terms are briefly described in this section.(1) Fuzzy Set. A fuzzy set A˜ is a subset of a universe of discourse X, which is defined by a membership function μN˜X depicting a mapping μA˜:X⟶0.1. The membership value, as the function value of μA˜XA˜, indicates the degree of accuracy that x is a constituent of fuzzy set A˜. It is supposed that μA˜:X∈0.1, where μA˜X=0 means that x is belonged to A˜ but not to the fuzzy set A˜ [26, 31, 32](2) Triangular Fuzzy Number. A triangular fuzzy number N˜ is referred to as a triplet l⋅m⋅u, and the membership function μN˜x can be illustrated by equation (1) as follows:(1)μN˜x=0,x<lx−lm−l,l≪x≪mu−xu−m,m≪x≪u0,x>u,where l, m, and u are real numbers and l≪m≪u [31].If assuming that a set of criteriaC = {C1, C2,..., Cn} belongs to a system, the particular pairwise relations are obtained for modeling regarding mathematical relations [31].(3) The Pairwise Comparison. The pairwise comparison may be characterized by four levels, of which the values of 0, 1, 2, and 3 correspond to “no influence,” “low influence,” ‘‘high influence,” and “very high influence,” respectively [31].(4) The Initial Direct-Relation Matrix. The initial direct-relation matrix Z is an n×n matrix determined by pairwise comparisons depending influential relations between criteria, where Zij is designated as the level to which the criterion Ci influences criterion Cj. Consequently, as equation (2) illustrates, all main determinant constituents Zij of matrix Z will be zero [31]:(2)C1C2C3,Z=c1,c2,…,cn0Z12CnZ210Z2N⋮⋮Zn1Zn20.(5) Causal Diagram. The causal diagram is obtained by depicting the pairs of (Dk + Rk, DkRk), of which the horizontal axis (D + R) is achieved by adding Rk to Dk (prominence) while the vertical axis (D-R) is obtained by subtracting Rk from Dk (prominence) [31].


## 2.2.2. DEMATEL

An extended five-step DEMTEL was introduced by Jassbi in 2011, as follows [33]:(i)
Step 1: establishment of the direct influence matrixIn this step, triangular fuzzy numbers corresponding to five linguistic terms were utilized (Table1).Table 1
The correspondence of linguistic values and linguistic terms.
Linguistic termsTriangular fuzzy numbersNo influence (No)(0, 0, 0.25)Very low influence (VL)(0, 0.25, 0.5)Low influence (L)(0.25, 0.5, 0.75)High influence (H)(0.5, 0.75, 1)Very high influence (VH)(0.75, 1, 1)After providing the list of extracted criteria or dimensions, pairwise comparisons of every single pair of the criteria were made by experts. The evaluations and priorities of individuals regarding the causality between every single pair of the criteria were assessed via adopting fuzzy numbers contributing to a fuzzy matrix, as depicted byZ̃ p. In this step, all experts were asked to respond to a previously prepared form. Certainly, the number of fuzzy matrices is indicated by p, and that p implies to the count of experts [34]. In the following step, it is needed to obtain and average the evaluation of the priorities of experts by employing the corresponding equation [33]:(3)z˜=x˜1⊕x˜2⊕x˜3⊕⋯⊕xpp.Here,p is the number of experts and x˜1 is the pair comparison matrix of expert 1, x˜2 is the pair comparison matrix of expert 2, and x˜p is the pair comparison matrix of expert p. Moreover, z˜ is the triangular fuzzy number as z˜ij=lij′,mij′,uij′.Then, using these equations, the aggregated fuzzy matrix was developed as the initial direct-relation fuzzy matrix.(ii)
Step 2: the initial direct-relation fuzzy matrix normalizationThen, the initial direct-relation fuzzy matrix would be normalized by applying equations (4) and (5) as mentioned below to build the normalized direct-relation fuzzy matrix H:(4)H˜ij=z˜ijr=lij′r,mij′r,uij′r=lij′,mij′,uij′,wherer is as follows:(5)r=max1≤i≤n∑j=1nuij′.(iii)
Step 3: computing the total-relation fuzzy matrix (TC)Then, the total-relation fuzzy matrixTC is calculated by computing the previously made matrices, which is defined as follows [33]:(6)T=limk⟶+∞H˜1⊕H˜2⊕⋯⊕H˜k,where(7)t˜ij=lijt,mijt,uijt,(8)lijt=Hl×I−Hl−1,(9)mijt=Hm×I−Hm−1,(10)uijt=Hu×I−Hu−1.(iv)
Step 4: computing influential importance and influential directionThe result obtained by the sum of columns and rows of matrixT is computed by equations (11) and (12) in this step. Here, ri represents all indirect and direct influences caused by the criterion i to the remaining factors, while the degree of influence would be represented by cj.Wheni = j, ri + cj, it represents all impacts that are received and given by the criterion i. In other words, ri + cj indicates both the system factors’ effect on factor i and the impact of criterion i upon the total system. Therefore, the level of significance of criterion i in the entire system can be demonstrated by the indicator ri + cj. Oppositely, the net impact of criterion i on the system can be indicated by the difference between the two, ri−cj.Noteworthy, the positive value ofri−cj shows that the factor i will be a net cause while its negative value shows that the factor will be a net result clustered into effect group [35]. For each i = j, it will be(11)D˜=D˜in×1=∑j=1nT˜ijn×1,(12)R˜=R˜i1×n=∑j=1nT˜ij1×n,whereD˜ and R˜ are the matrices n×1 and 1×n, respectively.ri+dj: relationship (cause and effect) strength (e.g., the higher the value of ri + dj for a criterion, the more interactions are found between the other factors).ri−dj: cause and effect direction (e.g., if ri−dj>0, the criterion is the effect, and if ri−dj<0, the criterion is the cause).Regarding the aforementioned calculated values, the indicesri+dj and ri−dj for the criteria and the indices D˜i+R˜i and D˜i−R˜i for the dimensions should be computed.At the end of the process, all calculatedD˜i+R˜i and D˜i−R˜i would be defuzzified via an appropriate defuzzification method:(13)defuzzification=u−l+m−l3+l,whereu, l, and m refer to the triangular fuzzy numbers, respectively.(v)
Step 5: building the net relation map (NRM)In this step, the value of the threshold is calculated to determine NRM. That is, building strong relationships while ignoring very complicated ones. Only those relations with a matrixT of greater than the threshold can be included in NRM. The relations with the value lower than the threshold value are considered zero, and the corresponding cause and effect relation is eliminated. Finally, the diagram related to the cause and effect relation is illustrated by representing the dataset of ri + cj and ri − cj. Figure 1 represents the steps of the fuzzy DEMATEL approach utilized in this investigation.Figure 1
The flowchart of the fuzzy DEMATEL approach.


## 2.3. Interpretive Structural Modeling (ISM)

Warfild introduced ISM methodology in 1974, which assists in dealing with complicated issues. It is an analytical method and helps groups or individuals to develop a framework of all possible correlations between the various components that existed in a complicated system. The basic purpose of this kind of model is to use the experts’ proficiency and also their knowledge to evaluate the system problems and afterward to build a multilevel structural model [36, 37].Hence, the model constructed to demonstrate the framework of a challenging issue in a depicted hierarchical diagram [38, 39] can assist in dealing with complicated conditions, including the situation under study, a series of factors which influence the unsafe behaviors of workers. In summary, ISM gives a driver-dependency clustered graph that can contribute to an appropriate prioritization of components to allocate the available resources and much higher accurate perception of the condition instead of only a single factor to be regarded. Therefore, ISM causes new insight into emerging recent concepts toward these indirect and direct interactions and, therefore, takes the advantages of experts’ ideas to find the hierarchical interactions among the components [40].


### 2.3.1. ISM Methodology and Model Development

The steps considered in the ISM to establish the desired hierarchical relationship are as follows [41, 42]:(1) identification of the important factors influencing the studied system, which can be extracted from different methods (e.g., literature review, metasynthesis, and brainstorming with experts). The dimensions were recognized by employing the metasynthesis method in the present study; (2) establishment of a contextual relationship of the dimensions extracted from the previous step with regard to the point that these pairs of variables would be evaluated; (3) making a structural self-interaction matrix (SSIM) for dimensions, which demonstrates pairwise relationships between dimensions of the studied system; (4) building a reachability matrix using the SSIM and reviewing the matrix for any possible indirect relationship considering an underlying assumption made in ISM; if a variable A is associated to B and also B is associated to C, then A is inevitability relevant to C; (5) establishment of various levels on the basis of the reachability matrix composited in the previous step; (6) obtaining a directed graph according to the previously observed relationships in the reachability matrix and eliminating transitive links; (7) transforming the constructed digraph into a model which is the ISM model by substitution of variable nodes with the declarations; and (8) controlling the ISM model designed in the previous step to be revised for any possible conceptual contradiction [41–44].


### 2.3.2. Matrice d’Impacts Croisés-Multiplication Appliquée á un Classement (MICMAC)

The cross-impact matrix multiplication applied to classification is a technique to graphically categorize dimensions of a complex predicament according to their driving power and dependence power. This approach composed of three main stages such as: (1) design of important practical factors and providing a comprehensive variable list applicable for future researches and analyses; (2) developing the causal relationship between variables; and (3) identification of critical factors that are essential for possible changes of embracing system [45]. Figure 2 depicts the proposed method of the current study.Figure 2
The presented model applied in this study.


## 2.3.1. ISM Methodology and Model Development

The steps considered in the ISM to establish the desired hierarchical relationship are as follows [41, 42]:(1) identification of the important factors influencing the studied system, which can be extracted from different methods (e.g., literature review, metasynthesis, and brainstorming with experts). The dimensions were recognized by employing the metasynthesis method in the present study; (2) establishment of a contextual relationship of the dimensions extracted from the previous step with regard to the point that these pairs of variables would be evaluated; (3) making a structural self-interaction matrix (SSIM) for dimensions, which demonstrates pairwise relationships between dimensions of the studied system; (4) building a reachability matrix using the SSIM and reviewing the matrix for any possible indirect relationship considering an underlying assumption made in ISM; if a variable A is associated to B and also B is associated to C, then A is inevitability relevant to C; (5) establishment of various levels on the basis of the reachability matrix composited in the previous step; (6) obtaining a directed graph according to the previously observed relationships in the reachability matrix and eliminating transitive links; (7) transforming the constructed digraph into a model which is the ISM model by substitution of variable nodes with the declarations; and (8) controlling the ISM model designed in the previous step to be revised for any possible conceptual contradiction [41–44].


## 2.3.2. Matrice d’Impacts Croisés-Multiplication Appliquée á un Classement (MICMAC)

The cross-impact matrix multiplication applied to classification is a technique to graphically categorize dimensions of a complex predicament according to their driving power and dependence power. This approach composed of three main stages such as: (1) design of important practical factors and providing a comprehensive variable list applicable for future researches and analyses; (2) developing the causal relationship between variables; and (3) identification of critical factors that are essential for possible changes of embracing system [45]. Figure 2 depicts the proposed method of the current study.Figure 2
The presented model applied in this study.


## 3. Application of the Methodology

The findings of the current investigation are explained in three main sections, including metasynthesis analysis, fuzzy DEMATEL analysis, and ISM.


### 3.1. Metasynthesis Analysis

Ten unsafe behavior cognitive factors (UBCFs) were elicited from metasynthesis as the essential variables causing unsafe behaviors in different industries (Table2).Table 2
The extracted UBCFs from the metasynthesis approach.
Criteria: unsafe behavior cognitive factors (UBCFs)C1Inadequacy of alertness as a result of mind overloadC2Failure in performing skillsC3Challenges in remembrance of information related to workC4Problem-solving difficultiesC5Misapplication of working methodsC6Habitual rule ignoranceC7Circumstantial rule disobedienceC8Inadequacy of persons’ resilience∗C9Circumstantial awareness failureC10The inability of emotion management∗Persons’ resilience is referred to as decreased vulnerability to environmental stress and coping with experienced adversity [46].Totally, 257 related resources were selected, and 47 of which were rated as eligible articles to be deeply analyzed. Table3 represents the extracted original themes (first-level codes), and also the ten criteria extracted themes abbreviated in this study as UBCFs. The explanation of the metasynthesis method including the assessed validity and accuracy as well as the reference support for extracting 10 criteria were discussed in detail in the previously published article from the same project of the current study [30].Table 3
Normalized initial direct-relation fuzzy matrix.
C1C2C3C4C5C6C7C8C9C10C1(0, 0, 0)(0.045, 0.83, 0.128)(0.051, 0.083, 0.128)(0.026, 0.038, 0.077)(0.077, 0.122, 0.154)(0.013, 0.019, 0.064)(0.013, 0.019, 0.064)(0.026, 0.038, 0.083)(0.115, 0.16, 0.173)(0.013, 0.038, 0.083)C2(0.038, 0.058, 0.103)(0, 0, 0)(0.006, 0.013, 0.058)(0.045, 0.77, 0.109)(0.09, 0.135, 0.16)(0.013, 0.019, 0.064)(0, 0, 0.45)(0.006, 0.013, 0.058)(0.051, 0.071, 0.103)(0.019, 0.032, 0.71)C3(0.051, 0.077, 0.115)(0.058, 0.09, 0.128)(0, 0, 0)(0.071, 0.103, 0.141)(0.115, 0.16, 0.179)(0.006, 0.0130, 0.058)(0.051, 0.071, 0.103)(0.019, 0.032, 0.077)(0.058, 0.077, 0.103)(0.032, 0.058, 0.096)C4(0.006, 0.013, 0.058)(0.019, 0.026, 0.064)(0.026, 0.038, 0.077)(0, 0, 0)(0.109, 0.154, 0.179)(0.013, 0.019, 0.051)(0.051, 0.071, 0.103)(0.045, 0.071, 0.109)(0.071, 0.103, 0.128)(0.045, 0.071, 0.103)C5(0.032, 0.051, 0.09)(0.006, 0.013, 0.058)(0.013, 0.019, 0.064)(0.013, 0.019, 0.064)(0, 0, 0)(0.019, 0.038, 0.083)(0.006, 0.013, 0.058)(0, 0.006, 0.051)(0.045, 0.064, 0.096)(0.032, 0.058, 0.103)C6(0.032, 0.045, 0.083)(0.032, 0.058, 0.096)(0.013, 0.026, 0.071)(0.019, 0.032, 0.077)(0.064, 0.103, 0.135)(0, 0, 0)(0.038, 0.064, 0.103)(0, 0.013, 0.058)(0.038, 0.058, 0.096)(0.026, 0.045, 0.083)C7(0.032, 0.045, 0.083)(0.026, 0.051, 0.09)(0.019, 0.032, 0.077)(0.026, 0.038, 0.083)(0.058, 0.096, 0.128)(0.038, 0.051, 0.083)(0, 0, 0)(0.006, 0.019, 0.064)(0.032, 0.051, 0.09)(0.019, 0.038, 0.083)C8(0.032, 0.058, 0.096)(0.019, 0.032, 0.077)(0.019, 0.032, 0.077)(0.071, 0.103, 0.141)(0.045, 0.071, 0.115)(0.006, 0.019, 0.064)(0.013, 0.026, 0.071)(0, 0, 0)(0.038, 0.064, 0.103)(0.038, 0.077, 0.122)C9(0.038, 0.051, 0.083)(0.032, 0.051, 0.09)(0.019, 0.026, 0.064)(0.006, 0.013, 0.058)(0.038, 0.064, 0.103S)(0.013, 0.026, 0.071)(0.013, 0.032, 0.077)(0.006, 0.019, 0.064)(0, 0, 0)(0.077, 0.103, 0.122)C10(0.019, 0.032, 0.077)(0.006, 0.013, 0.058)(0.013, 0.019, 0.064)(0.045, 0.64, 0.096)(0.045, 0.064, 0.103)(0.019, 0.038, 0.083)(0.019, 0.045, 0.09)(0, 0.006, 0.051)(0.019, 0.026, 0.064)(0, 0, 0)


### 3.2. DEMATEL Analysis

The cause and effect relationships between the elicited UBCFs were measured by employing the fuzzy DEMATEL method, in which experts rated the UBCFs according to the scale of 0–4 considering the effect of one UBCF compared to other UBCFs.Regarding the “dominant” values (i.e.,r + c), the whole influence of every essential UBCF can be shown (Tables 4–6). Based on the (r + c) values, the prominence or relevant significance degree for these elicited UBCFs is obtained as follows:Table 4
The initial direct-relation fuzzy matrix.
C1C2C3C4C5C6C7C8C9C10C1(0, 0, 0)(0.25, 0.464, 0.714)(0.286, 0.464, 0.714)(0.143, 0.214, 0.429)(0.429, 0.679, 0)(0.071, 0.107, 0.357)(0.071, 0.107, 0.357)(0.143, 0.214, 0.464)(0.643, 0.893, 0.964)(0.071, 0.214, 0.464)C2(0.214, 0.321, 0.571)(0, 0, 0)(0.036, 0.0710, 0.321)(0.25, 0.429, 0.607)(0.5, 0.75, 0.893)(0.071, 0.107, 0.357)(0, 0, 0.25)(0.036, 0.071, 0.321)(0.286, 0.393, 0.571)(0.107, 0.179, 0.393)C3(0.286, 0.429, 0.643)(0.321, 0.5, 0.714)(0, 0, 0)(0.393, 0.571, 0.786)(0.643, 0.893, 1)(0.036, 0.071, 0.321)(0.286, 0.393, 0.571)(0.107, 0.179, 0.429)(0.321, 0.429, 0.571)(0.179, 0.321, 0.536)C4(0.036, 0.071, 0.321)(0.107, 0.143, 0.357)(0.143, 0.214, 0.429)(0, 0, 0)(0.607, 0.857, 1)(0.0.71, 0.107, 0.286)(0.286, 0.393, 0.571)(0.250, 0.393, 0.607)(0.393, 0.571, 0.714)(0.250, 0.393, 0.571)C5(0.179, 0.286, 0.5)(0.036, 0.071, 0.321)(0.071, 0.107, 0.357)(0.071, 0.107, 0.357)(0, 0, 0)(0.107, 0.214, 0.464)(0.036, 0.071, 0.321)(0, 0.036, 0.286)(0.25, 0.357, 0.536)(0.179, 0.321, 0.571)C6(0.179, 0.25, 0.464)(0.179, 0.321, 0.536)(0.0, 0.0, 0.0)(0.107, 0.179, 0.429)(0.357, 0.571, 0.75)(0, 0, 0)(0.214, 0.357, 0.571)(0, 0.071, 0.321)(0.214, 0.321, 0.536)(0.143, 0.25, 0.464)C7(0.179, 0.25, 0.464)(0.143, 0.286, 0.5)(0.107, 0.179, 0.429)(0.143, 0.214, 0.464)(0.321, 0.536, 0.714)(0.214, 0.286, 0.464)(0, 0, 0)(0.036, 0.107, 0.357)(0.179, 0.286, 0.5)(0.107, 0.214, 0.464)C8(0.179, 0.321, 0.536)(0.107, 0.179, 0.429)(0.107, 0.179, 0.429)(0.393, 0.571, 0.786)(0.25, 0.393, 0.643)(0.0.36, 0.107, 0.357)(0.071, 0.143, 0.393)(0, 0, 0)(0.214, 0.357, 0.571)(0.214, 0.429, 0.679)C9(0.214, 0.286, 0.464)(0.179, 0.286, 0.5)(0.107, 0.143, 0.357)(0.036, 0.071, 0.321)(0.214, 0.357, 0.571)(0.071, 0.143, 0.393)(0.071, 0.179, 0.429)(0.036, 0.107, 0.357)(0, 0, 0)(0.429, 0.429, 0.679)C10(0.107, 0.179, 0.429)(0.036, 0.071, 0.321)(0.071, 0.107, 0.357)(0.25, 0.357, 0.536)(0.25, 0.357, 0.571)(0.107, 0.214, 0.464)(0.107, 0.25, 0.5)(0, 0.036, 0.286)(0.107, 0.143, 0.357)(0, 0, 0)Table 5
Total-relation fuzzy matrix.
C1C2C3C4C5C6C7C8C9C10C1(0.017, 0.049, 0.38)(0.057, 0.122, 0.487)(0.059, 0.109, 0.442)(0.04, 0.084, 0.464)(0.105, 0.209, 0.71)(0.02, 0.047, 0.369)(0.022, 0.052, 0.4)(0.03, 0.059, 0.375)(0.134, 0.219, 0.604)(0.034, 0.1, 0.496)C2(0.048, 0.086, 0.401)(0.008, 0.027, 0.302)(0.014, 0.035, 0.322)(0.052, 0.101, 0.416)(0.108, 0.191, 0.61)(0.018, 0.039, 0.312)(0.007, 0.024, 0.32)(0.011, 0.03, 0.299)(0.068, 0.44119, 0.464)(0.033, 0.073, 0.407)C3(0.068, 0.124, 0.498)(0.07, 0.13, 0.51)(0.012, 0.035, 0.343)(0.085, 0.147, 0.534)(0.149, 0.258, 0.758)(0.017, 0.046, 0.377)(0.061, 0.104, 0.447)(0.026, 0.058, 0.384)(0.087, 0.155, 0.566)(0.053, 0.12, 0.523)C4(0.022, 0.057, 0.398)(0.03, 0.06, 0.394)(0.034, 0.063, 0.368)(0.015, 0.042, 0.355)(0.132, 0.223, 0.675)(0.021, 0.047, 0.331)(0.059, 0.098, 0.0)(0.048, 0.086, 0.369)(0.089, 0.156, 0.522)(0.062, 0.123, 0.474)C5(0.038, 0.071, 0.354)(0.013, 0.35, 0.323)(0.018, 0.035, 0.297)(0.019, 0.04, 0.342)(0.014, 0.046, 0.413)(0.022, 0.051, 0.3)(0.011, 0.031, 0.3)(0.003, 0.017, 0.264)(0.055, 0.095, 0.412)(0.04, 0.084, 0.393)C6(0.042, 0.077, 0.397)(0.04, 0.085, 0.402)(0.020, 0.047, 0.343)(0.028, 0.063, 0.42)(0.083, 0.165, 0.605)(0.006, 0.022, 0.262)(0.044, 0.085, 0.381)(0.004, 0.028, 0.307)(0.055, 0.106, 0.469)(0.037, 0.085, 0.429)C7(0.042, 0.076, 0.391)(0.034, 0.079, 0.391)(0.026, 0.053, 0.344)(0.034, 0.069, 0.402)(0.078, 0.158, 0.592)(0.043, 0.07, 0.334)(0.007, 0.024, 0.282)(0.010, 0.035, 0.308)(0.049, 0.1, 0.457)(0.0031, 0.078, 0.423)C8(0.042, 0.091, 0.433)(0.028, 0.064, 0.41)(0.027, 0.057, 0.372)(0.08, 0.135, 0.485)(0.069, 0.147, 0.631)(0.012, 0.042, 0.343)(0.022, 0.055, 0.379)(0.006, 0.021, 0.275)(0.057, 0.121, 0.507)(0.052, 0.122, 0.492)C9(0.047, 0.078, 0.371)(0.039, 0.075, 0.37)(0.025, 0.045, 0.315)(0.017, 0.044, 0.36)(0.057, 0.121, 0.538)(0.018, 0.044, 0.307)(0.019, 0.052, 0.335)(0.009, 0.032, 0.291)(0.016, 0.044, 0.349)(0.84, 0.132, 0.433)C10(0.026, 0.054, 0.35)(0.012, 0.035, 0.329)(0.018, 0.036, 0.303)(0.05, 0.084, 0.377)(0.06, 0.113, 0.519)(0.023, 0.053, 0.306)(0.025, 0.063, 0.335)(0.004, 0.020, 0.271)(0.032, 0.064, 0.395)(0.009, 0.031, 0.308)Table 6
Total-relation matrix after defuzzification.
C1C2C3C4C5C6C7C8C9C10C10.1490.2220.2030.1960.3410.1450.1580.1550.3190.210C20.1780.1120.1240.1900.3030.1230.1170.1130.2170.171C30.2300.2330.1300.2550.3880.1470.2040.1560.2690.232C40.1590.1610.1550.1370.3430.1330.1860.1680.2560.220C50.1540.1230.1170.1340.1580.1240.1140.0950.1880.172C60.1720.1750.1370.1640.2850.0970.1700.1130.2100.184C70.1700.1680.1410.1690.2760.1490.1050.1180.2020.177C80.1880.1670.1520.2330.2820.1330.1520.1010.2280.22C90.1650.1610.1280.1400.2390.1230.1350.1110.1370.216C100.1430.1250.1190.1700.2310.1270.1410.0980.1640.116Problem-solving difficulties (C5) > inadequacy of alertness as a result of mind overload (C1) > circumstantial awareness failure (C9)> misapplication of working methods (C4)> challenges in remembrance of information related to work (C3)> failure in performing skills (C2)> inability of emotion management (C10)> circumstantial rule disobedience (C7)> inadequacy of persons’ resilience (C8)> habitual rule ignorance (C6), as shown in Table7.Table 7
The received matrix from the fuzzy DEMATEL approach for the studied accident.
CriteriaC1C2C3C4C5C6C7C8C9C10C10111—0101—C21001—0001—C31101—0101—C40000—0101—C5——————————C60100—0101—C70101—0001—C81101—0001—C90000—0000—C10——————————Similarly, the “relation” values (i.e.,r−c) are applied to classify the UBCFs into cause and effect groups in terms of the negative values (net receive) and the positive values (net cause) obtained in the total relationship matrix. Noteworthy, the threshold value (0.175) of the determined UBCFs was calculated by employing the values of the total relationship matrix. Moreover, based on the causal diagram, the values of C1, C3, C4, C6, C7, and C8 came under the cause group as they showed positive polarity in the cause-effect relationship net (Table 8).Table 8
Row and column values between dimensions.
DiRi(Di)defuzzy(Ri)defuzzyD + RD−RC1(0.519, 1.049, 4.729)(0.391, 0.764, 3.972)2.0991.7093.8080.390C2(0.366, 0.724, 3.855)(0.329, 0.71, 3.908)1.6481.6493.297−0.001C3(0.628, 1.177, 4.932)(0.252, 0.515, 3.451)2.2451.4063.6520.839C4(0.512, 0.954, 4.288)(0.419, 0.808, 4.138)1.9181.7883.7060.129C5(0.233, 0.505, 3.397)(0.855, 1.631, 6.05)1.3782.8454.223−1.467C6(0.357, 0.765, 3.997)(0.202, 0.461, 3.242)1.7061.3023.0080.405C7(0.353, 0.741, 3.925)(0.276, 0.588, 3.581)1.6731.4823.1550.191C8(0.394, 0.855, 4.327)(0.151, 0.386, 3.144)1.8591.2273.0860.632C9(0.331, 0.667, 0.669)(0.643, 1.178, 4.746)1.5562.1893.745−0.633C10(0.260, 0.552, 3.491)(0.433, 0.946, 4.378)1.4341.9193.353−0.485Similarly, C5, C9, and C10 were dedicated to the effect group indicating the opposite polarity, with (r − c) values of (−1.467), (−0.633), and (−0.485), respectively. As shown in Figure 3, C2 with the value of (−0.001) can be considered as a neutral UBCF that could have the functions of both cause and effect groups. The UBCFs under the cause group are required to be immediately highlighted by the industrial safety practitioner together with corresponding supervisors to understand the UBCFs dedicating to the effect group (Figure 4).Figure 3
The internal relationships between UBCFs by the DEMATEL approach.Figure 4
Cause and effect diagram among dimensions.Regarding the different UBCFs, their priorities, and associated weights in the final relationship matrix, the mentioned UBCFs were determined by the experts.


#### 3.2.1. Establishment of Reachability Matrix

In this step, after defuzzification of the fuzzy matrix of the total relationship, the threshold valueλ should be determined in order to the building of the reachability matrix. Here, if the degree of influence of a factor on another factor is higher than the value of λ, a response will be achieved. Correspondingly, if the degree of influence of factor on another factor is lower than the value of λ, no response will be obtained. A similar method is applied in some previous studies when establishing cause-effect modes [47]. The value of λ was 0.175 in the present study following the expert ideas. The reachability matrix consisting of 0 and 1, was established, as illustrated in Table 9. Here, 1 shows the significant correlation between two factors, and 0 indicates absent or ignorable relation among these two factors.Table 9
The received matrix from the fuzzy DEMATEL approach.
C1C2C3C4C5C6C7C8C9C10C11111101011C21111101011C31111101011C40001101011C50000100011C61101110011C70000101011C81111101111C90000100011C100000100011


### 3.3. ISM Analysis

#### 3.3.1. Establishment of Reachability Matrix

The reachability matrix was established by the reachability matrix extracted from the DEMATEL method. For each variable, the driving power was the total of variables with an impact potential (Tables10–12). The factor named dependence was the overall number of variables (also including the dependence), which might affect it (Tables 12–16). These dependencies and also driving power were applied in the MICMAC analysis through which the variables were clustered into four sections, including dependent, autonomous, independent (driver), and linkage variables.Table 10
Final reachability matrix.
C1C2C3C4C5C6C7C8C9C10C11111101011C21111101011C31111101011C40001101011C50000100011C61101110011C70000101011C81111101111C90000100011C100000100011Table 11
Driving/dependence power of factors under study.
CriteriaDriving powerDependence powerC1Inadequacy of alertness as a result of mind overload85C2Failure in performing skills85C3Challenges in remembrance of information related to work84C4Problem-solving difficulties56C5Misapplication of working methods310C6Habitual rule ignorance71C7Circumstantial rule disobedience46C8Inadequacy of persons’ resilience91C9Circumstantial awareness failure310C10The inability of emotion management310Table 12
Iteration1.
CriteriaReachability setAntecedent setIntersection setLevelC1C1C2C3C4C5C7C9C10C1C2C3C6C8C1C2C3C2C1C2C3C4C5C7C9C10C1C2C3C6C8C1C2C3C3C1C2C3C4C5C7C9C10C1C2C3C8C1C2C3C4C4C5C7C9C10C1C2C3C4C6C8C4C5C5C9C10C1C2C3C4C5C6C7C8C9C10C5C9C101C6C1C2C4C5C6C9C10C6C6C7C5C7C9C10C1C2C3C4C7C8C7C8C1C2C3C4C5C7C8C9C10C8C8C9C5C9C10C1C2C3C4C5C6C7C8C9C10C5C9C101C10C5C9C10C1C2C3C4C5C6C7C8C9C10C5C9C101Table 13
Iteration1.
CriteriaReachability setAntecedent setIntersection setLevelC1C1C2C3C4C7C1C2C3C6C8C1C2C3C2C1C2C3C4C7C1C2C3C6C8C1C2C3C3C1C2C3C4C7C1C2C3C8C1C2C3C4C4C7C1C2C3C4C6C8C4C6C1C2C4C6C6C6C7C7C1C2C3C4C7C8C72C8C1C2C3C4C7C8C8C8Table 14
Iteration1.
CriteriaReachability setAntecedent setIntersection setLevelC1C1C2C3C4C1C2C3C6C8C1C2C3C2C1C2C3C4C1C2C3C6C8C1C2C3C3C1C2C3C4C1C2C3C8C1C2C3C4C4C1C2C3C4C6C8C43C6C1C2C4C6C6C6C8C1C2C3C4C8C8C8Table 15
Iteration1.
CriteriaReachability setAntecedent setIntersection setLevelC1C1C2C3C1C2C3C6C8C1C2C34C2C1C2C3C1C2C3C6C8C1C2C34C3C1C2C3C1C2C3C8C1C2C34C6C1C2C6C6C6C8C1C2C3C8C8C8Table 16
Iteration1.
CriteriaReachability setAntecedent setIntersection setLevelC6C6C6C65C8C8C8C85


#### 3.3.2. MICMAC Analysis

MICMAC is a technique with the purpose of graphically categorizing of dimensions of a complex predicament according to their driving power and dependence power. The dependence and driving powers of every single cell are to be measured, in which the driving power is the total of cells (also containing itself), and it may influence [48].Furthermore, the dependence is the total of cells (also containing itself), which may influence it. The ratio (driving power/dependence power) provides the degree of being an independent variable of the issue for a dimension (instead of dependent, in the event of lower values of the ratio).In the present research, the UBCFs are clustered into four sections. The subsequent four clusters are autonomous UBCFs, independent UBCFs, linkage UBCFs, and dependent UBCFs. According to Table3, the dependence power and driving power for every single UBCF are calculated. The UBCFs under sector-I possess weak dependence and driving powers, termed autonomous UBCFs. These UBCFs are not linked with the entire system. The UBCFs under sector-II show considerable dependence power but inconsiderable driving power and are regarded as dependent UBCFs. In sector-III, UBCFs show considerable driving and dependence powers; therefore, these UBCFs are regarded as linkage UBCFs. The UBCFs which possess low dependence power and high driving power located in sector-IV and are known as independent UBCFs [49]. The diagram of dependence and driving powers developed according to the MICMAC analysis is illustrated in Figure 5.Sector-I: autonomous barriers show reduced dependence and driving powers, which have no link with the entire system due to the weak linkage with other UBCFs. According to Figure6, no autonomous UBCFs existed in the present investigation. An empty sector of the autonomous UBCF group recommends that all elicited UBCFs are considerably affecting the provision of an appropriate industrial safety program.Sector-II: dependent UBCFs showing too weak driving power are those being influenced by the independent UBCFs. The UBCFs of C5, C7, C9, and C10 indicate high dependence power but lower driving power.Sector-III: the linkage UBCFs show high driving and dependence powers in the MICMAC analysis. In the present study, UBCFs, including C1, C2, and C4, are innately changeable that any action performed on them will influence other UBCFs and can show a closed-loop effect on them as well.Sector-IV: independent UBCFs show considerable driving power and lower dependence power in the MICMAC analysis. In the present investigation, C3, C6, and C8 are placed in this quadrant and are considered as critical UBCFs, which demonstrate weak dependence power and also reliable driving power. Naturally, UBCFs of C6 and C6 are located at the bottom of the ISM hierarchy model (Figure6). Hence, industrial practitioners and managers must take the UBCFs under this category with the highest priority. Figure 7 indicates the integrative model proposed in the current study and makes the understanding of cause-effect hierarchical relationships easier.Figure 5
ISM model of UBCFs.Figure 6
Clusters of factors affecting the hazardous actions of industrial workers.Figure 7
The final integrative fuzzy DEMATEL ISM model of UBCFs.


### 3.4. Case Study

An industrial accident was selected in the current study to evaluate the utilization of the introduced methodology. The accident to be analyzed by research team occurred in 2016 in a petrochemical company located in the south of Iran.An explosion followed by fire happened in a main pressure reducing station (PRS) feeding gas required for several other neighboring plants. The investigations showed that all conditions of the station had been normal until one week before the incident occurred. The main cause of the incident was the large volume of impurities (e.g., waste and ferric oxides) in the main upstream pipeline to the PRS located in the industrial region. The entry of these unknown-originated impurities and contaminants caused the failure of the gas station filtration system and then the sudden increase in pressure difference. Following the filtration system failure, several regulators also failed concurrently. Meanwhile, the repair operations of filters and regulators began and continued until the day of the incident. During this period, various substations were constantly experiencing bugs, and they were often confronting repeated failures even after the repair and maintenance service. On the day of accident, the filters were collapsed due to a sudden high pressure of gas flow and, accordingly, the alarm system was failed.Moreover, the gas impurities had an unfavorable impact on the performance of the gas station control system and the related gasket also failed due to the fire. Noteworthy, just prior to the explosion, the operator of the site intended to inhibit the incident and hurried toward the involved valve to close it. As there was not enough opportunity to close the valve, the explosion happened. Probably, the collision of particles in the gas stream (e.g., iron sulfide) at high speed to the adjacent equipment created the source of ignition.After an exact analysis of aforementioned incident, failure to design and equipment layout (not to consider safe area, emergency exit route, etc.), organizational problems (e.g., lack of applicable and tailored training for emergency management) and human factors (attention deficits, mistakes, etc.) were among the most important factors contributing to the accidents. Although having a more holistic attitude, the root cause of design or organizational contributing factors referred to erroneous behaviors; according to the objectives defined for the present study, operators’ UBCFs related to the occurrence of the accident were exclusively considered to be studied as the last preventable link of any process accident chain (the white swan events).To illustrate all the leading human factors in the aforementioned accident, the intuitionistic fuzzy DEMATEL ISM approach was utilized. First, as the accident innate of process industries is different from other industries (e.g., construction, manufacturing, etc.), to evaluate the applicability of 10 criteria extracted from the metasynthesis method for the process accident to be studied, the brain storming method was applied in three meetings attended by a heterogeneous group of experts (HSE managers, senior supervisor, and emergency committee representative). Out of the 10 UBCFs, extracted from the metasynthesis method (Section2.1), 8 UBCFs were determined as appropriate factors to be analyzed in the case study (Table 17).Table 17
The extracted UBCFs from the case study by expert panel.
No.Modeling criteriaCase study criteriaC1Inadequacy of alertness as a result of mind overloadMechanical failures (e.g., gasket (s)/filter (s)) were not completely noticed due to operator’s mind overloadC2Failure in performing skillsOperator’s failure in performing repairing tasks (e.g., changing filters) properly which might have referred to task repetitive innateC3Challenges in remembrance of information related to workSome mechanical-related task sequences/stages (e.g., checking bolts/filters) were forgotten by operator (s)C4Problem-solving difficultiesHesitation/delay in blocking flow main sources due to uncertainty in making decisionC5Misapplication of working methodsNot applicableC6Habitual rule ignoranceTemporal leakage removal without considering procedures by repair teamC7Circumstantial rule disobedienceFailure in precisely performing the procedures and repairing leakage source due to circumstantial problems (e.g., high temperature)C8Inadequacy of persons’ resilienceInadequate concentration on manual monitoring in awkward positions (e.g., compact equipment/instruments) due to lack of operator’s resilienceC9Circumstantial awareness failureFailure in perception of real hazard related to previous leakages by repair teamC10The inability of emotion managementNot applicableThen, to assign the factors’ hierarchical cause-effect relationships, the experts’ opinion was collected via a questionnaire containing the eight elicited criteria in a matrix form. All computations were performed step by step according to Sections2.2 to 2.3.2. The internal relationships between the UBCFs are presented in Table 7. Here, as previously discussed, 1 shows the significant correlation between two UBCFs while 0 indicates absent or ignorable relation among them. Finally, integrating ISM and fuzzy DEMATEL methods, the model of the studied accident was constructed (Figure 8).Figure 8
The final integrative fuzzy DEMATEL ISM model of UBCFs elicited from the studied accident.


## 3.1. Metasynthesis Analysis

Ten unsafe behavior cognitive factors (UBCFs) were elicited from metasynthesis as the essential variables causing unsafe behaviors in different industries (Table2).Table 2
The extracted UBCFs from the metasynthesis approach.
Criteria: unsafe behavior cognitive factors (UBCFs)C1Inadequacy of alertness as a result of mind overloadC2Failure in performing skillsC3Challenges in remembrance of information related to workC4Problem-solving difficultiesC5Misapplication of working methodsC6Habitual rule ignoranceC7Circumstantial rule disobedienceC8Inadequacy of persons’ resilience∗C9Circumstantial awareness failureC10The inability of emotion management∗Persons’ resilience is referred to as decreased vulnerability to environmental stress and coping with experienced adversity [46].Totally, 257 related resources were selected, and 47 of which were rated as eligible articles to be deeply analyzed. Table3 represents the extracted original themes (first-level codes), and also the ten criteria extracted themes abbreviated in this study as UBCFs. The explanation of the metasynthesis method including the assessed validity and accuracy as well as the reference support for extracting 10 criteria were discussed in detail in the previously published article from the same project of the current study [30].Table 3
Normalized initial direct-relation fuzzy matrix.
C1C2C3C4C5C6C7C8C9C10C1(0, 0, 0)(0.045, 0.83, 0.128)(0.051, 0.083, 0.128)(0.026, 0.038, 0.077)(0.077, 0.122, 0.154)(0.013, 0.019, 0.064)(0.013, 0.019, 0.064)(0.026, 0.038, 0.083)(0.115, 0.16, 0.173)(0.013, 0.038, 0.083)C2(0.038, 0.058, 0.103)(0, 0, 0)(0.006, 0.013, 0.058)(0.045, 0.77, 0.109)(0.09, 0.135, 0.16)(0.013, 0.019, 0.064)(0, 0, 0.45)(0.006, 0.013, 0.058)(0.051, 0.071, 0.103)(0.019, 0.032, 0.71)C3(0.051, 0.077, 0.115)(0.058, 0.09, 0.128)(0, 0, 0)(0.071, 0.103, 0.141)(0.115, 0.16, 0.179)(0.006, 0.0130, 0.058)(0.051, 0.071, 0.103)(0.019, 0.032, 0.077)(0.058, 0.077, 0.103)(0.032, 0.058, 0.096)C4(0.006, 0.013, 0.058)(0.019, 0.026, 0.064)(0.026, 0.038, 0.077)(0, 0, 0)(0.109, 0.154, 0.179)(0.013, 0.019, 0.051)(0.051, 0.071, 0.103)(0.045, 0.071, 0.109)(0.071, 0.103, 0.128)(0.045, 0.071, 0.103)C5(0.032, 0.051, 0.09)(0.006, 0.013, 0.058)(0.013, 0.019, 0.064)(0.013, 0.019, 0.064)(0, 0, 0)(0.019, 0.038, 0.083)(0.006, 0.013, 0.058)(0, 0.006, 0.051)(0.045, 0.064, 0.096)(0.032, 0.058, 0.103)C6(0.032, 0.045, 0.083)(0.032, 0.058, 0.096)(0.013, 0.026, 0.071)(0.019, 0.032, 0.077)(0.064, 0.103, 0.135)(0, 0, 0)(0.038, 0.064, 0.103)(0, 0.013, 0.058)(0.038, 0.058, 0.096)(0.026, 0.045, 0.083)C7(0.032, 0.045, 0.083)(0.026, 0.051, 0.09)(0.019, 0.032, 0.077)(0.026, 0.038, 0.083)(0.058, 0.096, 0.128)(0.038, 0.051, 0.083)(0, 0, 0)(0.006, 0.019, 0.064)(0.032, 0.051, 0.09)(0.019, 0.038, 0.083)C8(0.032, 0.058, 0.096)(0.019, 0.032, 0.077)(0.019, 0.032, 0.077)(0.071, 0.103, 0.141)(0.045, 0.071, 0.115)(0.006, 0.019, 0.064)(0.013, 0.026, 0.071)(0, 0, 0)(0.038, 0.064, 0.103)(0.038, 0.077, 0.122)C9(0.038, 0.051, 0.083)(0.032, 0.051, 0.09)(0.019, 0.026, 0.064)(0.006, 0.013, 0.058)(0.038, 0.064, 0.103S)(0.013, 0.026, 0.071)(0.013, 0.032, 0.077)(0.006, 0.019, 0.064)(0, 0, 0)(0.077, 0.103, 0.122)C10(0.019, 0.032, 0.077)(0.006, 0.013, 0.058)(0.013, 0.019, 0.064)(0.045, 0.64, 0.096)(0.045, 0.064, 0.103)(0.019, 0.038, 0.083)(0.019, 0.045, 0.09)(0, 0.006, 0.051)(0.019, 0.026, 0.064)(0, 0, 0)


## 3.2. DEMATEL Analysis

The cause and effect relationships between the elicited UBCFs were measured by employing the fuzzy DEMATEL method, in which experts rated the UBCFs according to the scale of 0–4 considering the effect of one UBCF compared to other UBCFs.Regarding the “dominant” values (i.e.,r + c), the whole influence of every essential UBCF can be shown (Tables 4–6). Based on the (r + c) values, the prominence or relevant significance degree for these elicited UBCFs is obtained as follows:Table 4
The initial direct-relation fuzzy matrix.
C1C2C3C4C5C6C7C8C9C10C1(0, 0, 0)(0.25, 0.464, 0.714)(0.286, 0.464, 0.714)(0.143, 0.214, 0.429)(0.429, 0.679, 0)(0.071, 0.107, 0.357)(0.071, 0.107, 0.357)(0.143, 0.214, 0.464)(0.643, 0.893, 0.964)(0.071, 0.214, 0.464)C2(0.214, 0.321, 0.571)(0, 0, 0)(0.036, 0.0710, 0.321)(0.25, 0.429, 0.607)(0.5, 0.75, 0.893)(0.071, 0.107, 0.357)(0, 0, 0.25)(0.036, 0.071, 0.321)(0.286, 0.393, 0.571)(0.107, 0.179, 0.393)C3(0.286, 0.429, 0.643)(0.321, 0.5, 0.714)(0, 0, 0)(0.393, 0.571, 0.786)(0.643, 0.893, 1)(0.036, 0.071, 0.321)(0.286, 0.393, 0.571)(0.107, 0.179, 0.429)(0.321, 0.429, 0.571)(0.179, 0.321, 0.536)C4(0.036, 0.071, 0.321)(0.107, 0.143, 0.357)(0.143, 0.214, 0.429)(0, 0, 0)(0.607, 0.857, 1)(0.0.71, 0.107, 0.286)(0.286, 0.393, 0.571)(0.250, 0.393, 0.607)(0.393, 0.571, 0.714)(0.250, 0.393, 0.571)C5(0.179, 0.286, 0.5)(0.036, 0.071, 0.321)(0.071, 0.107, 0.357)(0.071, 0.107, 0.357)(0, 0, 0)(0.107, 0.214, 0.464)(0.036, 0.071, 0.321)(0, 0.036, 0.286)(0.25, 0.357, 0.536)(0.179, 0.321, 0.571)C6(0.179, 0.25, 0.464)(0.179, 0.321, 0.536)(0.0, 0.0, 0.0)(0.107, 0.179, 0.429)(0.357, 0.571, 0.75)(0, 0, 0)(0.214, 0.357, 0.571)(0, 0.071, 0.321)(0.214, 0.321, 0.536)(0.143, 0.25, 0.464)C7(0.179, 0.25, 0.464)(0.143, 0.286, 0.5)(0.107, 0.179, 0.429)(0.143, 0.214, 0.464)(0.321, 0.536, 0.714)(0.214, 0.286, 0.464)(0, 0, 0)(0.036, 0.107, 0.357)(0.179, 0.286, 0.5)(0.107, 0.214, 0.464)C8(0.179, 0.321, 0.536)(0.107, 0.179, 0.429)(0.107, 0.179, 0.429)(0.393, 0.571, 0.786)(0.25, 0.393, 0.643)(0.0.36, 0.107, 0.357)(0.071, 0.143, 0.393)(0, 0, 0)(0.214, 0.357, 0.571)(0.214, 0.429, 0.679)C9(0.214, 0.286, 0.464)(0.179, 0.286, 0.5)(0.107, 0.143, 0.357)(0.036, 0.071, 0.321)(0.214, 0.357, 0.571)(0.071, 0.143, 0.393)(0.071, 0.179, 0.429)(0.036, 0.107, 0.357)(0, 0, 0)(0.429, 0.429, 0.679)C10(0.107, 0.179, 0.429)(0.036, 0.071, 0.321)(0.071, 0.107, 0.357)(0.25, 0.357, 0.536)(0.25, 0.357, 0.571)(0.107, 0.214, 0.464)(0.107, 0.25, 0.5)(0, 0.036, 0.286)(0.107, 0.143, 0.357)(0, 0, 0)Table 5
Total-relation fuzzy matrix.
C1C2C3C4C5C6C7C8C9C10C1(0.017, 0.049, 0.38)(0.057, 0.122, 0.487)(0.059, 0.109, 0.442)(0.04, 0.084, 0.464)(0.105, 0.209, 0.71)(0.02, 0.047, 0.369)(0.022, 0.052, 0.4)(0.03, 0.059, 0.375)(0.134, 0.219, 0.604)(0.034, 0.1, 0.496)C2(0.048, 0.086, 0.401)(0.008, 0.027, 0.302)(0.014, 0.035, 0.322)(0.052, 0.101, 0.416)(0.108, 0.191, 0.61)(0.018, 0.039, 0.312)(0.007, 0.024, 0.32)(0.011, 0.03, 0.299)(0.068, 0.44119, 0.464)(0.033, 0.073, 0.407)C3(0.068, 0.124, 0.498)(0.07, 0.13, 0.51)(0.012, 0.035, 0.343)(0.085, 0.147, 0.534)(0.149, 0.258, 0.758)(0.017, 0.046, 0.377)(0.061, 0.104, 0.447)(0.026, 0.058, 0.384)(0.087, 0.155, 0.566)(0.053, 0.12, 0.523)C4(0.022, 0.057, 0.398)(0.03, 0.06, 0.394)(0.034, 0.063, 0.368)(0.015, 0.042, 0.355)(0.132, 0.223, 0.675)(0.021, 0.047, 0.331)(0.059, 0.098, 0.0)(0.048, 0.086, 0.369)(0.089, 0.156, 0.522)(0.062, 0.123, 0.474)C5(0.038, 0.071, 0.354)(0.013, 0.35, 0.323)(0.018, 0.035, 0.297)(0.019, 0.04, 0.342)(0.014, 0.046, 0.413)(0.022, 0.051, 0.3)(0.011, 0.031, 0.3)(0.003, 0.017, 0.264)(0.055, 0.095, 0.412)(0.04, 0.084, 0.393)C6(0.042, 0.077, 0.397)(0.04, 0.085, 0.402)(0.020, 0.047, 0.343)(0.028, 0.063, 0.42)(0.083, 0.165, 0.605)(0.006, 0.022, 0.262)(0.044, 0.085, 0.381)(0.004, 0.028, 0.307)(0.055, 0.106, 0.469)(0.037, 0.085, 0.429)C7(0.042, 0.076, 0.391)(0.034, 0.079, 0.391)(0.026, 0.053, 0.344)(0.034, 0.069, 0.402)(0.078, 0.158, 0.592)(0.043, 0.07, 0.334)(0.007, 0.024, 0.282)(0.010, 0.035, 0.308)(0.049, 0.1, 0.457)(0.0031, 0.078, 0.423)C8(0.042, 0.091, 0.433)(0.028, 0.064, 0.41)(0.027, 0.057, 0.372)(0.08, 0.135, 0.485)(0.069, 0.147, 0.631)(0.012, 0.042, 0.343)(0.022, 0.055, 0.379)(0.006, 0.021, 0.275)(0.057, 0.121, 0.507)(0.052, 0.122, 0.492)C9(0.047, 0.078, 0.371)(0.039, 0.075, 0.37)(0.025, 0.045, 0.315)(0.017, 0.044, 0.36)(0.057, 0.121, 0.538)(0.018, 0.044, 0.307)(0.019, 0.052, 0.335)(0.009, 0.032, 0.291)(0.016, 0.044, 0.349)(0.84, 0.132, 0.433)C10(0.026, 0.054, 0.35)(0.012, 0.035, 0.329)(0.018, 0.036, 0.303)(0.05, 0.084, 0.377)(0.06, 0.113, 0.519)(0.023, 0.053, 0.306)(0.025, 0.063, 0.335)(0.004, 0.020, 0.271)(0.032, 0.064, 0.395)(0.009, 0.031, 0.308)Table 6
Total-relation matrix after defuzzification.
C1C2C3C4C5C6C7C8C9C10C10.1490.2220.2030.1960.3410.1450.1580.1550.3190.210C20.1780.1120.1240.1900.3030.1230.1170.1130.2170.171C30.2300.2330.1300.2550.3880.1470.2040.1560.2690.232C40.1590.1610.1550.1370.3430.1330.1860.1680.2560.220C50.1540.1230.1170.1340.1580.1240.1140.0950.1880.172C60.1720.1750.1370.1640.2850.0970.1700.1130.2100.184C70.1700.1680.1410.1690.2760.1490.1050.1180.2020.177C80.1880.1670.1520.2330.2820.1330.1520.1010.2280.22C90.1650.1610.1280.1400.2390.1230.1350.1110.1370.216C100.1430.1250.1190.1700.2310.1270.1410.0980.1640.116Problem-solving difficulties (C5) > inadequacy of alertness as a result of mind overload (C1) > circumstantial awareness failure (C9)> misapplication of working methods (C4)> challenges in remembrance of information related to work (C3)> failure in performing skills (C2)> inability of emotion management (C10)> circumstantial rule disobedience (C7)> inadequacy of persons’ resilience (C8)> habitual rule ignorance (C6), as shown in Table7.Table 7
The received matrix from the fuzzy DEMATEL approach for the studied accident.
CriteriaC1C2C3C4C5C6C7C8C9C10C10111—0101—C21001—0001—C31101—0101—C40000—0101—C5——————————C60100—0101—C70101—0001—C81101—0001—C90000—0000—C10——————————Similarly, the “relation” values (i.e.,r−c) are applied to classify the UBCFs into cause and effect groups in terms of the negative values (net receive) and the positive values (net cause) obtained in the total relationship matrix. Noteworthy, the threshold value (0.175) of the determined UBCFs was calculated by employing the values of the total relationship matrix. Moreover, based on the causal diagram, the values of C1, C3, C4, C6, C7, and C8 came under the cause group as they showed positive polarity in the cause-effect relationship net (Table 8).Table 8
Row and column values between dimensions.
DiRi(Di)defuzzy(Ri)defuzzyD + RD−RC1(0.519, 1.049, 4.729)(0.391, 0.764, 3.972)2.0991.7093.8080.390C2(0.366, 0.724, 3.855)(0.329, 0.71, 3.908)1.6481.6493.297−0.001C3(0.628, 1.177, 4.932)(0.252, 0.515, 3.451)2.2451.4063.6520.839C4(0.512, 0.954, 4.288)(0.419, 0.808, 4.138)1.9181.7883.7060.129C5(0.233, 0.505, 3.397)(0.855, 1.631, 6.05)1.3782.8454.223−1.467C6(0.357, 0.765, 3.997)(0.202, 0.461, 3.242)1.7061.3023.0080.405C7(0.353, 0.741, 3.925)(0.276, 0.588, 3.581)1.6731.4823.1550.191C8(0.394, 0.855, 4.327)(0.151, 0.386, 3.144)1.8591.2273.0860.632C9(0.331, 0.667, 0.669)(0.643, 1.178, 4.746)1.5562.1893.745−0.633C10(0.260, 0.552, 3.491)(0.433, 0.946, 4.378)1.4341.9193.353−0.485Similarly, C5, C9, and C10 were dedicated to the effect group indicating the opposite polarity, with (r − c) values of (−1.467), (−0.633), and (−0.485), respectively. As shown in Figure 3, C2 with the value of (−0.001) can be considered as a neutral UBCF that could have the functions of both cause and effect groups. The UBCFs under the cause group are required to be immediately highlighted by the industrial safety practitioner together with corresponding supervisors to understand the UBCFs dedicating to the effect group (Figure 4).Figure 3
The internal relationships between UBCFs by the DEMATEL approach.Figure 4
Cause and effect diagram among dimensions.Regarding the different UBCFs, their priorities, and associated weights in the final relationship matrix, the mentioned UBCFs were determined by the experts.


### 3.2.1. Establishment of Reachability Matrix

In this step, after defuzzification of the fuzzy matrix of the total relationship, the threshold valueλ should be determined in order to the building of the reachability matrix. Here, if the degree of influence of a factor on another factor is higher than the value of λ, a response will be achieved. Correspondingly, if the degree of influence of factor on another factor is lower than the value of λ, no response will be obtained. A similar method is applied in some previous studies when establishing cause-effect modes [47]. The value of λ was 0.175 in the present study following the expert ideas. The reachability matrix consisting of 0 and 1, was established, as illustrated in Table 9. Here, 1 shows the significant correlation between two factors, and 0 indicates absent or ignorable relation among these two factors.Table 9
The received matrix from the fuzzy DEMATEL approach.
C1C2C3C4C5C6C7C8C9C10C11111101011C21111101011C31111101011C40001101011C50000100011C61101110011C70000101011C81111101111C90000100011C100000100011


## 3.2.1. Establishment of Reachability Matrix

In this step, after defuzzification of the fuzzy matrix of the total relationship, the threshold valueλ should be determined in order to the building of the reachability matrix. Here, if the degree of influence of a factor on another factor is higher than the value of λ, a response will be achieved. Correspondingly, if the degree of influence of factor on another factor is lower than the value of λ, no response will be obtained. A similar method is applied in some previous studies when establishing cause-effect modes [47]. The value of λ was 0.175 in the present study following the expert ideas. The reachability matrix consisting of 0 and 1, was established, as illustrated in Table 9. Here, 1 shows the significant correlation between two factors, and 0 indicates absent or ignorable relation among these two factors.Table 9
The received matrix from the fuzzy DEMATEL approach.
C1C2C3C4C5C6C7C8C9C10C11111101011C21111101011C31111101011C40001101011C50000100011C61101110011C70000101011C81111101111C90000100011C100000100011


## 3.3. ISM Analysis

### 3.3.1. Establishment of Reachability Matrix

The reachability matrix was established by the reachability matrix extracted from the DEMATEL method. For each variable, the driving power was the total of variables with an impact potential (Tables10–12). The factor named dependence was the overall number of variables (also including the dependence), which might affect it (Tables 12–16). These dependencies and also driving power were applied in the MICMAC analysis through which the variables were clustered into four sections, including dependent, autonomous, independent (driver), and linkage variables.Table 10
Final reachability matrix.
C1C2C3C4C5C6C7C8C9C10C11111101011C21111101011C31111101011C40001101011C50000100011C61101110011C70000101011C81111101111C90000100011C100000100011Table 11
Driving/dependence power of factors under study.
CriteriaDriving powerDependence powerC1Inadequacy of alertness as a result of mind overload85C2Failure in performing skills85C3Challenges in remembrance of information related to work84C4Problem-solving difficulties56C5Misapplication of working methods310C6Habitual rule ignorance71C7Circumstantial rule disobedience46C8Inadequacy of persons’ resilience91C9Circumstantial awareness failure310C10The inability of emotion management310Table 12
Iteration1.
CriteriaReachability setAntecedent setIntersection setLevelC1C1C2C3C4C5C7C9C10C1C2C3C6C8C1C2C3C2C1C2C3C4C5C7C9C10C1C2C3C6C8C1C2C3C3C1C2C3C4C5C7C9C10C1C2C3C8C1C2C3C4C4C5C7C9C10C1C2C3C4C6C8C4C5C5C9C10C1C2C3C4C5C6C7C8C9C10C5C9C101C6C1C2C4C5C6C9C10C6C6C7C5C7C9C10C1C2C3C4C7C8C7C8C1C2C3C4C5C7C8C9C10C8C8C9C5C9C10C1C2C3C4C5C6C7C8C9C10C5C9C101C10C5C9C10C1C2C3C4C5C6C7C8C9C10C5C9C101Table 13
Iteration1.
CriteriaReachability setAntecedent setIntersection setLevelC1C1C2C3C4C7C1C2C3C6C8C1C2C3C2C1C2C3C4C7C1C2C3C6C8C1C2C3C3C1C2C3C4C7C1C2C3C8C1C2C3C4C4C7C1C2C3C4C6C8C4C6C1C2C4C6C6C6C7C7C1C2C3C4C7C8C72C8C1C2C3C4C7C8C8C8Table 14
Iteration1.
CriteriaReachability setAntecedent setIntersection setLevelC1C1C2C3C4C1C2C3C6C8C1C2C3C2C1C2C3C4C1C2C3C6C8C1C2C3C3C1C2C3C4C1C2C3C8C1C2C3C4C4C1C2C3C4C6C8C43C6C1C2C4C6C6C6C8C1C2C3C4C8C8C8Table 15
Iteration1.
CriteriaReachability setAntecedent setIntersection setLevelC1C1C2C3C1C2C3C6C8C1C2C34C2C1C2C3C1C2C3C6C8C1C2C34C3C1C2C3C1C2C3C8C1C2C34C6C1C2C6C6C6C8C1C2C3C8C8C8Table 16
Iteration1.
CriteriaReachability setAntecedent setIntersection setLevelC6C6C6C65C8C8C8C85


### 3.3.2. MICMAC Analysis

MICMAC is a technique with the purpose of graphically categorizing of dimensions of a complex predicament according to their driving power and dependence power. The dependence and driving powers of every single cell are to be measured, in which the driving power is the total of cells (also containing itself), and it may influence [48].Furthermore, the dependence is the total of cells (also containing itself), which may influence it. The ratio (driving power/dependence power) provides the degree of being an independent variable of the issue for a dimension (instead of dependent, in the event of lower values of the ratio).In the present research, the UBCFs are clustered into four sections. The subsequent four clusters are autonomous UBCFs, independent UBCFs, linkage UBCFs, and dependent UBCFs. According to Table3, the dependence power and driving power for every single UBCF are calculated. The UBCFs under sector-I possess weak dependence and driving powers, termed autonomous UBCFs. These UBCFs are not linked with the entire system. The UBCFs under sector-II show considerable dependence power but inconsiderable driving power and are regarded as dependent UBCFs. In sector-III, UBCFs show considerable driving and dependence powers; therefore, these UBCFs are regarded as linkage UBCFs. The UBCFs which possess low dependence power and high driving power located in sector-IV and are known as independent UBCFs [49]. The diagram of dependence and driving powers developed according to the MICMAC analysis is illustrated in Figure 5.Sector-I: autonomous barriers show reduced dependence and driving powers, which have no link with the entire system due to the weak linkage with other UBCFs. According to Figure6, no autonomous UBCFs existed in the present investigation. An empty sector of the autonomous UBCF group recommends that all elicited UBCFs are considerably affecting the provision of an appropriate industrial safety program.Sector-II: dependent UBCFs showing too weak driving power are those being influenced by the independent UBCFs. The UBCFs of C5, C7, C9, and C10 indicate high dependence power but lower driving power.Sector-III: the linkage UBCFs show high driving and dependence powers in the MICMAC analysis. In the present study, UBCFs, including C1, C2, and C4, are innately changeable that any action performed on them will influence other UBCFs and can show a closed-loop effect on them as well.Sector-IV: independent UBCFs show considerable driving power and lower dependence power in the MICMAC analysis. In the present investigation, C3, C6, and C8 are placed in this quadrant and are considered as critical UBCFs, which demonstrate weak dependence power and also reliable driving power. Naturally, UBCFs of C6 and C6 are located at the bottom of the ISM hierarchy model (Figure6). Hence, industrial practitioners and managers must take the UBCFs under this category with the highest priority. Figure 7 indicates the integrative model proposed in the current study and makes the understanding of cause-effect hierarchical relationships easier.Figure 5
ISM model of UBCFs.Figure 6
Clusters of factors affecting the hazardous actions of industrial workers.Figure 7
The final integrative fuzzy DEMATEL ISM model of UBCFs.


## 3.3.1. Establishment of Reachability Matrix

The reachability matrix was established by the reachability matrix extracted from the DEMATEL method. For each variable, the driving power was the total of variables with an impact potential (Tables10–12). The factor named dependence was the overall number of variables (also including the dependence), which might affect it (Tables 12–16). These dependencies and also driving power were applied in the MICMAC analysis through which the variables were clustered into four sections, including dependent, autonomous, independent (driver), and linkage variables.Table 10
Final reachability matrix.
C1C2C3C4C5C6C7C8C9C10C11111101011C21111101011C31111101011C40001101011C50000100011C61101110011C70000101011C81111101111C90000100011C100000100011Table 11
Driving/dependence power of factors under study.
CriteriaDriving powerDependence powerC1Inadequacy of alertness as a result of mind overload85C2Failure in performing skills85C3Challenges in remembrance of information related to work84C4Problem-solving difficulties56C5Misapplication of working methods310C6Habitual rule ignorance71C7Circumstantial rule disobedience46C8Inadequacy of persons’ resilience91C9Circumstantial awareness failure310C10The inability of emotion management310Table 12
Iteration1.
CriteriaReachability setAntecedent setIntersection setLevelC1C1C2C3C4C5C7C9C10C1C2C3C6C8C1C2C3C2C1C2C3C4C5C7C9C10C1C2C3C6C8C1C2C3C3C1C2C3C4C5C7C9C10C1C2C3C8C1C2C3C4C4C5C7C9C10C1C2C3C4C6C8C4C5C5C9C10C1C2C3C4C5C6C7C8C9C10C5C9C101C6C1C2C4C5C6C9C10C6C6C7C5C7C9C10C1C2C3C4C7C8C7C8C1C2C3C4C5C7C8C9C10C8C8C9C5C9C10C1C2C3C4C5C6C7C8C9C10C5C9C101C10C5C9C10C1C2C3C4C5C6C7C8C9C10C5C9C101Table 13
Iteration1.
CriteriaReachability setAntecedent setIntersection setLevelC1C1C2C3C4C7C1C2C3C6C8C1C2C3C2C1C2C3C4C7C1C2C3C6C8C1C2C3C3C1C2C3C4C7C1C2C3C8C1C2C3C4C4C7C1C2C3C4C6C8C4C6C1C2C4C6C6C6C7C7C1C2C3C4C7C8C72C8C1C2C3C4C7C8C8C8Table 14
Iteration1.
CriteriaReachability setAntecedent setIntersection setLevelC1C1C2C3C4C1C2C3C6C8C1C2C3C2C1C2C3C4C1C2C3C6C8C1C2C3C3C1C2C3C4C1C2C3C8C1C2C3C4C4C1C2C3C4C6C8C43C6C1C2C4C6C6C6C8C1C2C3C4C8C8C8Table 15
Iteration1.
CriteriaReachability setAntecedent setIntersection setLevelC1C1C2C3C1C2C3C6C8C1C2C34C2C1C2C3C1C2C3C6C8C1C2C34C3C1C2C3C1C2C3C8C1C2C34C6C1C2C6C6C6C8C1C2C3C8C8C8Table 16
Iteration1.
CriteriaReachability setAntecedent setIntersection setLevelC6C6C6C65C8C8C8C85


## 3.3.2. MICMAC Analysis

MICMAC is a technique with the purpose of graphically categorizing of dimensions of a complex predicament according to their driving power and dependence power. The dependence and driving powers of every single cell are to be measured, in which the driving power is the total of cells (also containing itself), and it may influence [48].Furthermore, the dependence is the total of cells (also containing itself), which may influence it. The ratio (driving power/dependence power) provides the degree of being an independent variable of the issue for a dimension (instead of dependent, in the event of lower values of the ratio).In the present research, the UBCFs are clustered into four sections. The subsequent four clusters are autonomous UBCFs, independent UBCFs, linkage UBCFs, and dependent UBCFs. According to Table3, the dependence power and driving power for every single UBCF are calculated. The UBCFs under sector-I possess weak dependence and driving powers, termed autonomous UBCFs. These UBCFs are not linked with the entire system. The UBCFs under sector-II show considerable dependence power but inconsiderable driving power and are regarded as dependent UBCFs. In sector-III, UBCFs show considerable driving and dependence powers; therefore, these UBCFs are regarded as linkage UBCFs. The UBCFs which possess low dependence power and high driving power located in sector-IV and are known as independent UBCFs [49]. The diagram of dependence and driving powers developed according to the MICMAC analysis is illustrated in Figure 5.Sector-I: autonomous barriers show reduced dependence and driving powers, which have no link with the entire system due to the weak linkage with other UBCFs. According to Figure6, no autonomous UBCFs existed in the present investigation. An empty sector of the autonomous UBCF group recommends that all elicited UBCFs are considerably affecting the provision of an appropriate industrial safety program.Sector-II: dependent UBCFs showing too weak driving power are those being influenced by the independent UBCFs. The UBCFs of C5, C7, C9, and C10 indicate high dependence power but lower driving power.Sector-III: the linkage UBCFs show high driving and dependence powers in the MICMAC analysis. In the present study, UBCFs, including C1, C2, and C4, are innately changeable that any action performed on them will influence other UBCFs and can show a closed-loop effect on them as well.Sector-IV: independent UBCFs show considerable driving power and lower dependence power in the MICMAC analysis. In the present investigation, C3, C6, and C8 are placed in this quadrant and are considered as critical UBCFs, which demonstrate weak dependence power and also reliable driving power. Naturally, UBCFs of C6 and C6 are located at the bottom of the ISM hierarchy model (Figure6). Hence, industrial practitioners and managers must take the UBCFs under this category with the highest priority. Figure 7 indicates the integrative model proposed in the current study and makes the understanding of cause-effect hierarchical relationships easier.Figure 5
ISM model of UBCFs.Figure 6
Clusters of factors affecting the hazardous actions of industrial workers.Figure 7
The final integrative fuzzy DEMATEL ISM model of UBCFs.


## 3.4. Case Study

An industrial accident was selected in the current study to evaluate the utilization of the introduced methodology. The accident to be analyzed by research team occurred in 2016 in a petrochemical company located in the south of Iran.An explosion followed by fire happened in a main pressure reducing station (PRS) feeding gas required for several other neighboring plants. The investigations showed that all conditions of the station had been normal until one week before the incident occurred. The main cause of the incident was the large volume of impurities (e.g., waste and ferric oxides) in the main upstream pipeline to the PRS located in the industrial region. The entry of these unknown-originated impurities and contaminants caused the failure of the gas station filtration system and then the sudden increase in pressure difference. Following the filtration system failure, several regulators also failed concurrently. Meanwhile, the repair operations of filters and regulators began and continued until the day of the incident. During this period, various substations were constantly experiencing bugs, and they were often confronting repeated failures even after the repair and maintenance service. On the day of accident, the filters were collapsed due to a sudden high pressure of gas flow and, accordingly, the alarm system was failed.Moreover, the gas impurities had an unfavorable impact on the performance of the gas station control system and the related gasket also failed due to the fire. Noteworthy, just prior to the explosion, the operator of the site intended to inhibit the incident and hurried toward the involved valve to close it. As there was not enough opportunity to close the valve, the explosion happened. Probably, the collision of particles in the gas stream (e.g., iron sulfide) at high speed to the adjacent equipment created the source of ignition.After an exact analysis of aforementioned incident, failure to design and equipment layout (not to consider safe area, emergency exit route, etc.), organizational problems (e.g., lack of applicable and tailored training for emergency management) and human factors (attention deficits, mistakes, etc.) were among the most important factors contributing to the accidents. Although having a more holistic attitude, the root cause of design or organizational contributing factors referred to erroneous behaviors; according to the objectives defined for the present study, operators’ UBCFs related to the occurrence of the accident were exclusively considered to be studied as the last preventable link of any process accident chain (the white swan events).To illustrate all the leading human factors in the aforementioned accident, the intuitionistic fuzzy DEMATEL ISM approach was utilized. First, as the accident innate of process industries is different from other industries (e.g., construction, manufacturing, etc.), to evaluate the applicability of 10 criteria extracted from the metasynthesis method for the process accident to be studied, the brain storming method was applied in three meetings attended by a heterogeneous group of experts (HSE managers, senior supervisor, and emergency committee representative). Out of the 10 UBCFs, extracted from the metasynthesis method (Section2.1), 8 UBCFs were determined as appropriate factors to be analyzed in the case study (Table 17).Table 17
The extracted UBCFs from the case study by expert panel.
No.Modeling criteriaCase study criteriaC1Inadequacy of alertness as a result of mind overloadMechanical failures (e.g., gasket (s)/filter (s)) were not completely noticed due to operator’s mind overloadC2Failure in performing skillsOperator’s failure in performing repairing tasks (e.g., changing filters) properly which might have referred to task repetitive innateC3Challenges in remembrance of information related to workSome mechanical-related task sequences/stages (e.g., checking bolts/filters) were forgotten by operator (s)C4Problem-solving difficultiesHesitation/delay in blocking flow main sources due to uncertainty in making decisionC5Misapplication of working methodsNot applicableC6Habitual rule ignoranceTemporal leakage removal without considering procedures by repair teamC7Circumstantial rule disobedienceFailure in precisely performing the procedures and repairing leakage source due to circumstantial problems (e.g., high temperature)C8Inadequacy of persons’ resilienceInadequate concentration on manual monitoring in awkward positions (e.g., compact equipment/instruments) due to lack of operator’s resilienceC9Circumstantial awareness failureFailure in perception of real hazard related to previous leakages by repair teamC10The inability of emotion managementNot applicableThen, to assign the factors’ hierarchical cause-effect relationships, the experts’ opinion was collected via a questionnaire containing the eight elicited criteria in a matrix form. All computations were performed step by step according to Sections2.2 to 2.3.2. The internal relationships between the UBCFs are presented in Table 7. Here, as previously discussed, 1 shows the significant correlation between two UBCFs while 0 indicates absent or ignorable relation among them. Finally, integrating ISM and fuzzy DEMATEL methods, the model of the studied accident was constructed (Figure 8).Figure 8
The final integrative fuzzy DEMATEL ISM model of UBCFs elicited from the studied accident.


## 4. Discussion

In the present study, all unsafe behavior cognitive factors (UBCFs) were elicited by employing a metasynthesis approach to develop a new hierarchical model with respect to the interactional relationships between the UBCFs related to industrial workers by collecting the opinions from seven experts and applying an integrative fuzzy DEMATEL ISM approach. Moreover, in order to evaluate the model application, an accident was studied. Performance assessment of the metasynthesis method indicated its sufficient comprehensiveness to encompass all essential factors affecting the individual’s cognition system emerging as an unsafe behavior. The validity of metasynthesis was confirmed, and the analyzed studies in the method were evaluated and rated in the “highly acceptable” category [30].The UBCF of “inadequacy of alertness as a result of mind overload” can contribute to “misapplication of working methods,” especially when using the safety procedures. This mutual relationship has been also confirmed based on the ISM model (Figure5). The UBCF “inadequacy of alertness as a result of mind overload” was also mutually related to “challenges in remembrance of information related to work,” which was interestingly confirmed by the ISM approach used in this investigation as they were both located at the same level in ISM structure. Similarly, in the model constructed based on the case study, the two factors of “mechanical failures (e.g., gasket (s)/filter (s)) were not completely noticed due to operator’s mind overload” and “some mechanical-related task sequences/stages (e.g., checking bolts/filters) were forgotten by operator (s)” were mutually linked to each other and showed a cause-effect relationship as well. Similarly, in a study by Rostamabadi et al., a fuzzy Bayesian network was used to analyze human and organizational factors contributed to process accidents, and mental overload was identified as the most important factor to be prioritized and managed [50].The UBCF “inadequacy of persons’ resilience ” is directly affecting the “inadequacy of alertness as a result of mind overload” and is indirectly influential on “failure in performing skills” and “misapplication of working methods.” Also, the “inadequacy of persons’ resilience” is located in level 5 according to the ISM model, indicating the importance of this UBCF compared to the others. Hence, as the UBCF of the “inadequacy of persons’ resilience” is also indirectly effective on the two UBCFs of “circumstantial awareness failure” and “the inability of emotion management,” it can be implied that this UBCF is highly determinant in committing unsafe behaviors by industrial workers.Likewise, in the studied accident, also similar findings were observed. Although the UBCF, “the inability of emotion management,” was not applicable, the most influential factor in the analyzed accident was also “inadequate concentration on manual monitoring in awkward positions (e.g., compact equipment/instruments) due to lack of operator’s resilience” which had an effect on “operator’s failure in performing repairing tasks (e.g., changing filters) properly which might have referred to task repetitive innate.” A large number of psychological researchers discovered that the management of emotions profoundly influences the attention, hazard perception, and process of decision-making [11, 51]. Moreover, many types of researches have also focused on the importance of implementing ergonomic parameters in different industries to prevent human errors [52]. Also, in a study conducted by Hass and Yorio in an industry, the effect of hazard understanding on incidents was significant with a cognitive factor of locus of control [53]. Furthermore, Sneddon et al. assigned a relationship between lower work situation awareness and increased commitment to unsafe behavior. Furthermore, the workers who had at least one occupational accident report in their document records had remarkably lower scores for the work situation awareness compared to those without any accident. Individuals with imperfect situation awareness also had more rule-violating behaviors at work [54]. In another study, it was assigned that a workers’ eligibility to perceive hazards can cause avoiding hazardous situations, which can contribute to near-miss accidents [55].On the other hand, the UBCF “habitual rule ignorance” is considered as a “casual” factor for “failure in performing skills” that is an influential factor for both “problem-solving difficulties” and “misapplication of working methods” which can be the indicative of the importance level of C6 compared to the other UBCFs in forming unsafe behaviors in industrial workplaces. As it can be implied from Figure 8, in the investigated accident, similar relationships have been also achieved between the UBCFs “temporal leakage removal without considering procedures by repair team” and “hesitation/delay in blocking flow main sources due to uncertainty in making decision” which can indicate the feasibility of the developed methodology in the current study. Apparently, in case that the working methods and regulations are disregarded too frequently by the workers, it would be transformed to habitual negligence contributing to a reduction in workers’ attention, precision, and information processing, which may cause more human error commitment, particularly in industrial workplaces. In the literature, psychological theories demonstrate a negative relationship between people’s decision-making on risk-taking behavior and their risk perception [55]. Also, in a study carried out by Zarei et al. using the fuzzy Bayesian network, 40% of the top ten contributing root events were attributed to routine violations [4].Moreover, C3 or “challenges in remembrance of information related to work” is placed in lower levels of ISM which means a high level of influence by this factor on several other parameters such as “problem-solving difficulties,” “circumstantial rule disobedience,” and “inability of emotion management” that is also confirmed in the DEMATEL model used in this investigation since the number of interactions by other factors is considerable for “challenges in remembrance of information related to work” (Figure 5). Interestingly, the two UBCFs of “failure in precisely performing the procedures and repairing leakage source due to circumstantial problems (e.g., high temperature)” and “hesitation/delay in blocking flow main sources due to uncertainty in making decision” showed cause-effect relationships (Figure 8). It should be noted that in previous researches, similar findings were also obtained. For instance, Endsley discovered three involving steps for workers experiencing risky situations, such as (1) determination of hazardous signals, (2) perception and understanding the risks, and (3) anticipation of the results associated with decision selections [56]. Longo et al., in 2019, used the fuzzy method to introduce human factor taxonomy for industrial accidents and found that in emergency situations, in particular, cognitive aspects had a fundamental influence on the manager’s response performance [11].The present study can be considered to be unique due to its novelty in developing a new hierarchical cause-effect model, along with its concentration on cognitive factors inducing risky behaviors among front-line workers. However, this study may possess some limitations which can be discussed. First, there was a lack of enough studies related to human errors among front-line workers in industrial workplaces since a large number of them have only concentrated on the air industry or healthcare systems. Therefore, not many studies were existed in the metasynthesis methodology to be analyzed. Second, the basis of DEMATEL and ISM methodologies were based on experts’ opinions, which may be slightly deviated from the exact answer depending on their unique experiences or interests. Third, using dynamic methods e.g., Bayesian networks or improved DEMATEL could obtain more confidential results in our study due to considering the important factor of “time” when predicting unsafe behavior of front-line workers. Nevertheless, the researchers suggest further studies on human cognitive factors contributing to unsafe behavior using exact statistical data of different causal accident-inducing factors as well as incorporating dynamic methods.


## 5. Conclusions

The results of the present study indicated that there are internal and hierarchical relationships between the UBCFs, which can highly assist prioritizing the UBCFs for the provision of a safety program concentrating on those workers endangered with various hazards in different industries. The UBCF “inadequacy of persons’ resilience” was the most influential compared to the others. The UBCF of the “inadequacy of persons’ resilience” was located on the basis of the ISM model, indicating the importance of this UBCF compared to the other UBCFs. Thus, since the UBCF “inadequacy of persons’ resilience” was also indirectly practical for the two UBCFs, “circumstantial awareness failure” and “the inability of emotion management,” it can be implied that this key UBCF is highly determinant in committing unsafe behaviors in industrial workers. Therefore, implementing a regularly organized personnel training for front-line workers on human error cognitive roots is essential for promoting human performance in industrial workplaces. Further studies seem to be required on cognitive factors taxonomy incorporating dynamic methods.



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*Source: 8952624-2020-11-21.xml*