Quantitative assessment of resilience safety culture using principal components analysis and numerical taxonomy: A case study in a petrochemical plant

Accepted Manuscript Quantitative assessment of resilience safety culture using principal components analysis and numerical taxonomy: A case study in a petrochemical plant Gh.A. Shirali, M. Shekari, K.A. Angali PII:

S0950-4230(16)30007-9

DOI:

10.1016/j.jlp.2016.01.007

Reference:

JLPP 3122

To appear in:

Journal of Loss Prevention in the Process Industries

Received Date: 22 May 2015 Revised Date:

6 December 2015

Accepted Date: 11 January 2016

Please cite this article as: Shirali, G.A., Shekari, M., Angali, K.A., Quantitative assessment of resilience safety culture using principal components analysis and numerical taxonomy: A case study in a petrochemical plant, Journal of Loss Prevention in the Process Industries (2016), doi: 10.1016/ j.jlp.2016.01.007. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Quantitative assessment of resilience safety culture using principal components analysis and numerical taxonomy: A case study in a petrochemical plant

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Gh.A. Shiralia, , M. Shekaria , K.A. Angalib

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a Department of Occupational Health Engineering, School of Public Health, Ahvaz Jundishapur University of Medical Sciences, P.O. Box: 61355-131, Iran. Tel: +98 6133738269; fax: +98 6133738282. [email protected] b Department of Biostatistics and Epidemiology, School of Public Health, Ahvaz Jundishapur University of Medical Sciences.

Abstract:

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Over the last decades, some major accidents have occurred in highly reliable industries such as petrochemical plants. Probably, sophisticated safety management systems and a high level safety culture have contributed to decreasing the number of usual accidents, but these classical approaches may not have been sufficient to prevent the occurrence of extraordinary incidents and accidents. Consequently, there is a need for new approaches like resilience engineering to promote the safety of these systems. In this light, and to use safety culture more efficiently in

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ultra-safe systems, a new concept as "resilience safety culture" has been proposed. However, due to the paucity of studies and their qualitative nature, there is now more interest in using numerical methods to quantitatively evaluate the resilience safety culture of a system. This research, however, aimed at a quantitative assessment of resilience safety culture of a

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petrochemical plant using a questionnaire and was based on

the two approaches of principal

components analysis (PCA) and numerical taxonomy (NT). Accordingly, a questionnaire

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including 59 questions about several aspects of the safety culture and resilience engineering was designed and distributed to 354 randomly selected employees of 12 units in a petrochemical plant. The results of exploratory factor analysis of the data extracted thirteen factors which represent the resilience safety culture. The analysis also led to the determination of the score of resilience safety culture and its weakness in the petrochemical units. Implementing the proposed approach would enable the policy makers and managers in petrochemical industries to identify current weaknesses and challenges regarding the resilience safety culture in their system. Keywords: safety culture, resilience safety culture, resilience engineering, PCA, NT

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1. Introduction For many years, most systems used to employ the conventional risk management to deal with risks. The conventional risk management approaches are based upon the knowledge of

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previous experiences, failure reportings, and risk assessments by computing historical data-based probabilities. Today, it is known that the causation of incidents and accidents can be traced to the organizational factors, functional performance variability, and the occurrence of unexpected combinations (Shirali et al., 2012). Existing uncertainty in several complex systems such as

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petrochemical plants, oil and gas refineries, and aviations can lead to an increased risk. Petrochemical plants are potentially prone to incidents which have catastrophic outcomes such as explosion, leakage of toxic materials, and the stoppage of the production (Azadeh et al., 2014b).

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During the last 20 years, many studies have shown that the notion of "safety culture" is a key factor for efficient risk mastering (Sorensen, 2002), (Olive et al., 2006) and the notion of safety culture has been successful both with the researchers and the practitioners(Chevreau, 2006). Although sophisticated safety management systems and a high-level safety culture have helped decrease the number of usual accidents, these classical approaches may not have been sufficient to prevent the occurrence of extraordinary incidents and accidents (Adolph et al., 2012).

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Consequently, there is a need for new approaches like resilience engineering to promote the safety of these systems. Therefore, resilience engineering (RE), as a new way of thinking about safety and accidents, has attracted widespread attention from industries and academia. The RE has been praised as an innovative way of managing safety. Those who developed the young and

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quickly expanding notion of the "RE" now wish to find a completely new way of thinking about safety (Hollnagel et al., 2007a). To use safety culture more efficiently in a plant, such as a

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petrochemical one where safety is already very high, the RE may be needed. So far, many studies on the safety culture have been conducted, and many of these quote safety culture as the most important factor in the ability of a company or organization for implementing safety management systems (Reason, 1997), but each of them has weaknesses in the conceptualization and utilization of safety culture (Akselsson et al., 2009). However, this research aimed at a quantitative assessment of resilience safety culture within a petrochemical plant using a questionnaire, based on two approaches: the PCA and the NT. Since only the potential for resilience, and not the resilience itself, could be measured (Shirali et al., 2013), the

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researchers’ purpose was to examine the validity of a survey method for measuring potential resilience safety culture in a petrochemical plant. 1.1. Principal components analysis

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The PCA is a statistical technique used to examine the interrelations among a set of variables in order to identify the underlying structure of those variables. It combines a set of observed variables into a smaller set of “artificial” variables called principal components. The PCA is widely used in multivariate statistical procedures such as factor analysis. It is used to

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reduce the number of variables under study and consequently to rank and analyze decisionmaking units (DMUs)including industries, universities, hospitals, cities, and the like (Azadeh

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and Ebrahimipour, 2004), (Azadeh et al., 2007b). The objective of the PCA is to identify a new set of variables such that each new variable is a linear combination of original variables. Second, the first new variable y1 accounts for the maximum variance in the sample data and so on. Third, the new variables that are called the principal components are uncorrelated. The PCA is performed by identifying Eigen structure of the covariance or the singular value decomposition of the original data (Azadeh et al., 2011).

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Suppose there are m variables and n decision-making units (DMUs) and consider the following n×m data matrix: D= (d1, d2... dm)

n*m

with each row represents the values of each

variable for each DMU. The PCA process of D is carried out as follows: Step 1: Calculate the sample mean vector đ and covariance matrix S.

•

Step 2: Calculate the sample correlation matrix R.

•

Step 3: Solve the following equation (1):

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•

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|R − βI | = 0 (1)

Where Im is a p × p identity matrix. The ordered m characteristic roots (eigenvalues) β1≥ β2≥ β3≥ βm with ∑ 1 β = m and the related m characteristic vectors (eigenvectors) are obtained. The characteristic vectors (lm1; lm2; . . . ; lmn) (m= 1, 2,…, n) compose the principal components Yi. The components in eigenvectors are respectively the coefficient in each corresponding Yi:  Y = ∑   l d (2)

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Where d defined as the mean of the value of jth standardized index for nth DMU. lmj is the coefficient of mth variable for the jth principal component. Step 4: Calculate the weights (wk) of the principal components and the PCA scores (Zn) of each

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DMU (N=1, 2,…, N). The z vector (z1, z2,…, zn) where the score of the nth DMU, (Zn), is given by equation (3): Z = ∑   w Y (3)

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Where Yk are defined as principal components.

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The PCA can be used as a ranking methodology. The higher the PCA score, the better the rank of the corresponding DMU will be. Further information about the PCA can be provided from literatures (Azadeh and Ebrahimipour, 2004). 1.2. Numerical taxonomy

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The application of numerical methods (data) in the classification of taxonomic units is called numerical taxonomy. These methods study the relationships of taxa by the application of numerical similarity values to characters so as to rank into categories based on the degree of overall similarity. The NT is, in essence, the numerical evaluation of the affinities and

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similarities between the taxonomic unit and the ordering of these units into taxa on the basis of their affinities (Lu et al., 2009). It can identify homogeneous cases from non-homogeneous ones. A group of process units is divided into homogeneous sub-groups by giving indicators (Shirali et

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al., 2013). It converts the information content of homogeneous groups to numerical quantitative values. The NT also ranks DMUs in a particular group. After defining new measures, and in order to follow the NT process, the distance of every two DMUs for each indicator is computed. With regard to of the distance matrix, DMUs are then ranked (Azadeh et al., 2007a). Just as it was in the PCA, suppose there are m indicators and n decision-making units (DMUs). The NT approach is as follows: • Step 1: The n×m data matrix is made.

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• Step 2: The data matrix is standardized so that all indexes have a mean of 0 and a variance of 1. • Step 3: The distance of every two DMUs for each index is computed. This is done to ×

and vector d = |d |×

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homogenize the DMUs. Therefore, the distance matrix D = d  

where di is the minimum of ith row of matrix D are identified. To identify homogeneous cases,

 +2Sd and L2=  -2Sd the upper (L1) and lower (L2) limits of vector d are computed as L1=

 and Sd are the mean and the standard deviation of vector d, respectively. If all where 

instances of dij are within L1 and L2, homogeneity is achieved and we can initiate the next step. The distance of each DMU from the ideal unit for each index is computed as follows: C  = ∑  1 Z  − Z!"# $

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•

(4)

f =

+,+-

,

0 ≤ f ≤ 1

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where %&'() is the maximum of the kth index. The growth level for each DMU is:  + 2Sd (5) where Co=C

 and Sd are the mean and the standard deviation of Cio's, respectively. The lower the fi, the C

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better the score is. Therefore, in the NT approach, the DMU with the lowest score would be in the first place of ranking (Zhu, 1998), (Azadeh et al., 2011).

2.1. Safety culture

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2. Safety culture and resilience engineering

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The concept of safety culture was born in the aftermath of the Chernobyl accident in 1986. Since then, many studies have been done in the area of the safety culture (e.g.,(Pidgeon, 1991), (Pidgeon, 1998),(Clarke, 1999), (Cooper Ph D, 2000), (Guldenmund, 2000),(Gadd and Collins, 2002),(Richter and Koch, 2004), (Hopkins, 2005), (Flin, 2007), (Guldenmund, 2007), (Antonsen, 2009a), (Silbey, 2009), (Reiman and Rollenhagen, 2014), but as (Akselsson et al., 2009) pointed out, working with safety culture has its own weaknesses. The reasons for this could be: focusing on only one aspect, which is often the Just culture; looking for low-score groups or aspects; critical time-windows; the construct dilemmas; disregarding resilience aspects; management commitment and communication as the cornerstones of safety culture; gaps between what is said 5

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and written and what is practiced; and not using feed forward control (Akselsson et al., 2009). In addition to these weaknesses, other researchers have pointed out other drawbacks including qualitative differences in the dimensions; being based on human related concepts (assumptions, values, norms, behavior); not considering dynamic interactions among people, technology, and

consequences such as injuries, adverse events, and so forth.

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administration. These studies often try to link the safety culture concept to various negative

However, the concept of safety culture has now become incorporated into safety management applications in all major safety-critical areas, such as aviation, nuclear power

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production, petrochemical sector (including offshore oil production), railways, peacetime military operations, maritime, and mining operations (Reiman and Rollenhagen, 2014). Safety

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culture, in informal social-scientific terms, is an object of knowledge. As such, it is part of a larger discursive practice of accident prevention, together with other objects like technical failure and human error (Henriqson et al., 2014).

Despite the recent interest in safety culture, there is still considerable disagreement and confusion about what safety culture really is. The general definition proposed by the Advisory Committee on the Safety of Nuclear Installation (ACSNI) is probably the most widely accepted

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definition (Antonsen, 2009b). ACSNI defines safety culture as:

" Safety culture is the product of individual and group values, attitudes, perceptions, competencies and patterns of behavior that determine the commitment to and the style and proficiency of an organization's health and safety management" (Health and Commission, 1993).

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Safety culture at its best could facilitate systemic stories of success and act as a concept that can used for understanding how organizations view safety and how those views affect the

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overall safety of the system (Reiman and Rollenhagen, 2014). Therefore, a new concept as "resilience safety culture" has been proposed in order to cover weaknesses of working with safety culture.

2.2. Resilience engineering

Traditional risk assessment is not adequate for analyzing the risks that exist in the socio-technical systems (Qureshi, 2007). There is a clear need for new approaches in risk assessment and safety management of complex systems such as petrochemical plants. The RE has been proposed as a 6

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solution to satisfy this need (Azadeh et al., 2014a). Resilience was first introduced to the field of safety in the late 1980s. Similar to safety culture, there is no common understanding of this term. One common element within the identified definitions involves an organization’s ability to withstand pressure and continue performing under both expected and unexpected conditions.

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Wreathall defines resilience as:

“Resilience is the ability of an organization to keep, or recover quickly to, a stable state, allowing it to continue operations during and after a major mishap or in the presence of continuous significant stresses" (Wreathall, 2006).

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The RE attempts to control processes not only in terms of risk, but also in terms of safety and keeping the system within safety limits. If this fails, then the objective is to control the system back to normal safe functions. This means a focus on feed-forward control is required in addition

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to feedback control and or learning from accidents and incidents. The RE is one such new approach to provide tools for proactive safety management.

2.3. Resilience safety culture

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Managing risk proactively is difficult. When a system is struggling to meet daily pressures, how can it tell the difference between inefficiencies and buffers against the unexpected (Woods, 2006)? To achieve the very high requirements on safety, current concepts for safety

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management, including safety culture, have to be used more efficiently. Therefore, new concepts for safety management may be needed. The RE, a concept under development and one which emphasizes feed forward control as a complement to feedback control, may be such a concept. A

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safety culture with resilience, learning, continuous improvements and cost-effectiveness as its focus is referred to as resilience safety culture. In theory, resilience safety culture is not different from safety culture as normally defined, but the difference is in how it is used in practice (Akselsson et al., 2009). Resilience safety culture is defined as follows: " Resilience safety culture is an organizational culture that fosters safe practices for improved safety in an ultra-safe organization striving for cost-effective safety management by stressing the RE, organizational learning and continuous improvements" (Akselsson et al., 2009).

On one hand, a strong safety culture can avoid the drift of system to failures and losses by helping the organization to improve its safety performance and from the other hand, the RE helps 7

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the system to recover a stable state after a major mishap or an event rather than prevent the occurrence of incidents and accidents. In fact, a strong safety culture and the proactive nature of the RE can help the organization not only to prevent the occurrence of accidents but also to recover after an upset. Therefore, an organization having a resilience safety culture makes great

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efforts to establish a strong safety culture and resilience by adopting and developing safety culture indicators and the RE tools. This organization is able to respond to the pressures and influences causing the drift to states of higher risk.

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3. Method 3.1. Population and sample

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The study focused on a petrochemical plant in the southwest of Iran. The plant was founded in 1998 with more than 2300 employees. The target population of this research comprised employees from various units of the plant.

The units of the plant (with 1274

personnel), which were directly and indirectly related to production, were classified into 12 (Table 1). In reality, 5 out of 12 are production units and the others are ancillary organizational units. Data was gathered by a self-constructed questionnaire with the five-point Likert-type

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scales. The questionnaires were delivered directly to the employees. The respondents were assured that the responses would remain confidential so that the information could not be traced back to employee respondents. 354 questionnaires were distributed and 312 valid ones were gathered, representing a high valid response rate (response rate of 88.1%). Among 312

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respondents, 61 were managers or supervisors (19.6%) and 251 were operators and technicians (80.4%). 2% of the employees were female and 98% were male.

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Table 1. DMUs name in this study

3.2. Measures

Following an exhaustive review of the literature and interviews with safety experts, a set of key safety culture and resilience engineering dimensions or indicators was selected (Table2). Next, the authors elaborated items related to each selected dimension. The first version of the questionnaire contained 66 items related to the field of safety culture and resilience engineering. The questionnaire was reviewed by nine experts who had worked in the field of safety or had 8

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studied safety issues to revise the questions and provide professional suggestions for establishing the content validity. Seven questions were removed from the questionnaire to improve the

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content validity. The final version of the questionnaire consisted of 59 items.

Table2. Description of the variables of the research Variable

Description

Learning culture Risk assessment/management

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Management of change

An atmosphere of trust that workers are encouraged to report essential safety concerns and issues (Reason, 1997) . A best practice used to ensure that safety risks are controlled when a plant makes changes in their facilities, documentation, personnel, or operations. How much does the plant respond to problems with denial versus modification?(Hollnagel et al., 2007b) A systematic process of evaluating the potential risks that may be involved in a process or activity.

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Just culture

Preparedness

Actively anticipates various threats and prepares for them

Flexibility

Ability to restructure in response to various changes and variabilities

Reporting culture

Cultivating an atmosphere where employees have confidence to report safety-related issues without fear of blame.

Awareness

Accident investigation Involvement of staff

What an employee is capable of doing (Re and Macchi, 2010).

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Competency

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Safety management system

Recognizing the human performance concerns and tiring to address them, devoting to safety above or to the same extent as the other goals in the plant (Hollnagel et al., 2007b), (Shirali et al., 2013). Aware of risks and systems' boundaries and know how close it is to their edge (Hollnagel et al., 2007b), (Saurin and Júnior, 2011), as well as aware of the safeguards and procedures efficiency. Systematic approach to proactively managing safety, including the necessary organizational structures, accountabilities, policies and procedures. Process of detailed and systematically collecting and analyzing information relating to an accident. How much employees are contributed in decision making and planning for safety.

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Management commitment

3.3. Best practice

After collecting the questionnaires, the researchers required a reference or criterion for comparing all the resilience safety culture indicators with the questionnaire. Although resilience safety culture indicators for intra- and inter-units were compared, it was not enough since it is believed that the plant is not able to recognize its weaknesses. As there is no reference in the literature review, a reference questionnaire was designed using responses of the respondents. 9

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This reference questionnaire was used as the best practice with regard to the safety experts' and statisticians' comments (Shirali et al., 2013).

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4. Results For the statistical analysis, SPSS 16.0 was used. Exploratory factor analysis was used to examine the construct validity of the resilience safety culture indicators. The Kaiser-MeyerOlkin measure of sampling adequacy was 0.88, which demonstrates the factor analysis would be

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appropriate for this data (Kaiser, 1974). Bartlett's test of sphericity was significant for the test (χ2=9951, p< 0.001), which also shows that correlations exist between the items. The underlying

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factors were found using principal components analysis, with an orthogonal varimax rotation applied. Thirteen factors were found and the factor loadings varied between 0.557 - 0.854. The common factors were determined by the Eigenvalues greater than one. The total explained variance was 68.29%.

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Table 3. Cronbach's alpha coefficient for every variable of scale

Based on the texts in questions

associated with each domain of safety culture and

resilience engineering, these factors were named as just culture, management of change, learning

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culture, risk assessment/management, preparedness, flexibility, reporting culture, management commitment, awareness, safety management system, accident investigation, involvement of staffs and competency. Hereafter, these factors would be known as resilience safety culture

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variables. Internal consistency coefficient, split half, and test-retest methods were used to evaluate the reliability of the scale. In addition, Cronbach's alpha was used to determine internal consistency. For the full scale, Cronbach's alpha was 0.943. The Cronbach's alpha coefficient for every summed scale was calculated (Table 3). To determine the consistency of the questionnaire across time, 56 respondents completed the questionnaire again after two weeks. Pearson correlation coefficient of test-retest was 0.882. Spearman-brown correlation coefficient of the split half method was 0.817. These show that the questionnaire has a high level of reliability.

4.1. The PCA results 10

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After determining the validity and reliability of the questionnaire, the data was processed through PCA and Minitab 16. Since there are 12 DMUs and 13 variables (indicators), and there is typically too much output of the PCA related to these DMUs and variables typically, the results of one of the 12 DMUs (the output of Minitab for DMU1) is presented (Table 4).

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Obviously, the results of the other DMUs can be calculated in the same manner. The eigenvalue analysis of the correlation matrix of the sample data in relation to the PCA, and the eigenvalues and eigenvectors obtained from the correlation matrix of indices can be seen in Table 4. Cumulative percent of the sample data is reported in the third line of the Table. The first M

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principal components may be selected by satisfying cumulative percent higher than 90% (Wreathall, 2006). In the present research, 95% contribution to the total sample variance was

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considered.

Table 4 The result of PCA related to DMU1

Therefore, as it is observed in Table 4, the amounts of the first nine components (PC1,

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PC2... PC9) were sufficient to comprise 95% of the data variance. Consequently, the other components were ignored. Table 5 shows the value of principal components for each variable in all DMUs. These values are obtained by multiplying the proportion of principal component by coefficient of the corresponding variable. For example, for management of change in DMU1, the

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value is calculated as follows:

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Z = 0.244 PC1 + 0.179 PC2 + 0.121 PC3 + 0.112 PC4 + 0.091 PC5 + 0.067 PC6 + 0.053PC7 + 0.052 PC8 + 0.031 PC9 (6)

For other variables in the DMUs, the value of Z can be calculated in the same way. Finally, the final PCA scores for all DMUs are presented in Table 6. Table 5. The values of principal components for all DMUs Table 6. The final PCA scores for all DMUs

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4.2. Validation and verification

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Since like the PCA, the NT technique can also be applied to rank DMUs, we can use the NT to verify and validate the PCA results. By computing the correlation between the results of the two approaches, we can compare them. The Spearman-brown correlation coefficient demonstrates the amount of agreement between the results of the two methods. The NT results

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can be seen in the next section.

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4.3. The NT results

As mentioned above, to apply the NT approach, it is necessary to form the data matrix and standardize it. After forming the data matrix and standardizing it, the distance of every two DMUs is calculated. In distance matrix (Table 7), all diagonal numbers are 0 because the distance of each unit from itself is zero. The distance of DMU1 from DMU2 is equal to the distance of DMU2 from DMU1. Therefore, the distance matrix is a symmetric matrix. Then

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vector distance and its upper and lower limits are computed to identify homogeneous scenarios. As the homogeneity was obtained, the distance of each DMU from the ideal unit for each index was calculated. After that, the growth level for each DMU was computed. Finally, by knowing

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the growth levels, the DMUs were ranked (Table 8).

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Table 7. Distance matrix for DMUs

As mentioned before, to verify and validate the result of the PCA, we can compare it with the results of the NT approach. Table 9 shows the Ranking of the DMUs by the PCA and the NT. To compare the amount of agreement between the results of the two methods, the Spearmanbrown correlation coefficient was calculated by the following formula: 6 ∑ 32,

r2 = 1 - 4

42 51$

(7)

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Where N=12 is the number of DMUs and ∑ d2 is the summation of the differences between the values of the two methods. The correlation coefficient between the PCA and the NT was computed as 0.734 at α=0.01 level of significance. At this level of significance, it can be said that

Table 8. Ranking of DMUs by NT

Table 9. PCA and NT ranking of DMUs

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5. Discussion

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the results of the PCA are validated by the NT.

The main purpose of the present study was to quantitatively assess the resilience safety culture within a petrochemical plant using a questionnaire and based on the PCA. The NT

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approach was used to verify and validate the results of the PCA. The results of the PCA related to resilience safety culture in the 12 units of the petrochemical plant (Table 5) indicate that several dimensions of resilience safety culture have low scores in contrast to the others. In the following, the results of all DMUs will be discussed in detail. Analysis in DMU1 showed that management of change, risk assessment/management, accident investigation, just culture, involvement of staff, and flexibility with negative scores has the worst condition. This unit was,

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therefore, ranked 10 with the final score of 5.226 was. Note that these indicators have intrarelations. For example, the procedures for the management of change are elements of a safety management system as are the procedures for accident investigation. These indicators can also affect each other. It is obvious that changes and modifications are part of the nature of dynamic

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industries such as petrochemical ones for them to survive. Therefore, identification of risks arising from these changes is a key element in such industries. Failure in identification of the

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changes can, as a result, lead to deficiency in risk assessment/management and accident investigation. In addition, promotion of just culture can lead to increased employees' involvement, subsequently increasing the flexibility of the system under study. However, to improve the resilience safety culture and consequently the rank of this unit, it is necessary to pay more attention to all of these indicators, rather than one or a few of them. The condition for indicators in DMU2 is a little better than DMU1. However, five indicators including risk assessment/management, accident investigation, reporting culture, safety management system, and competency obtained negative scores. In this case, the competency based on training can be increased by the reporting of resilience (safety) concerns and problems. The rank of DMU2 with 13

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the final score of 14.575 is 8. The state of DMU3 is not satisfying. Its final score and rank are 12.675 and 9, respectively. To make the level of resilience safety culture better in this unit, it is required to improve the indicators with negative scores such as management of change, risk assessment/management, preparedness, and competency. The score for preparedness shows that

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DMU3 do not have a comprehensive plan to deal with possible future failures. The scores of two indicators, i.e., management of change and management commitment in DMU4 are negative and lower than the others. These two indicators have a greater impact on the level of resilience safety culture in the unit. Therefore, they are placed in rank 7 with the final score of 16.663. Improving

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the resilience safety culture in DMU4 can be achieved by paying more attention to these two indicators because management commitment to safety and resilience has a significant role in the

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safety culture of all the operations on site. In this context, the plant management is responsible for defining and maintaining the safety management system. Learning culture and competency with negative scores should be improved in order to promote resilience safety culture in DMU5. This unit, with the final score of 16.929, is placed in rank 5. The results of the PCA reveal that DMU6 with the final score of 25.960 is the best unit regarding resilience safety culture indicators. Perhaps it is not surprising that this unit, i.e., the health, safety and environment

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(HSE) department, ranked the top in all. The condition of almost all indicators in DMU7 is very good so that the rank of this unit with a final score of 25.252 is 2. The score of three indicators, i.e., reporting culture, awareness, and preparedness in DMU8 are lower than the others. Since their scores are negative, the level of resilience safety culture in this unit has decreased.

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Awareness and preparedness are interdependent. In other words, preparedness and effective and rapid responses to unexpected and unknown events require awareness of the danger areas and

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risks. The final score of DMU8 is 16.668, classifying it in rank 6. DMU9 has a reasonable state considering the resilience safety culture indicators. Its final score and rank are 21.470 and 4, respectively. Yet, flexibility with a negative score requires more attention in this unit. With regard to the final score and rank, DMU10 and DMU11 are critical units. Considering the importance of these units in a petrochemical plant, they require serious attention. Due to the important role of maintenance in the safety and resilience of the plant, and knowing the fact that safety and resilience in this unit can challenge the safety and resilience related to other units, special attention must be paid in this regard. DMU10, with a final score of 4.870, is ranked 11. The results of PCA show that DMU11 with the final score of 0.560 is the worst unit. As the 14

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technical inspection unit is an important unit in recognizing the plant failures (e.g. corrosion), which can lead to adverse consequences, this unit needs serious attention in order to promote resilience safety culture indicators. DMU12 with a final score of 21.505 has a good position in respect to resilience safety culture indicators. Its rank is 3 among all units.

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To better understand the situation of the petrochemical plant units, they were compared with the best practice. This comparison reveals that all scores have a value lower than the best practice (Table 5). It means that the plant has serious problems in resilience safety culture.

It was demonstrated that two indicators, i.e., competency and management of change, have

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negative scores in five and four units, respectively. Therefore, top managers of the plant should apply a proper policy to be sure about the ability of individuals to do their jobs properly and start

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establishing management of change in their system.

With respect to the number of occupational accidents, a comparison was made randomly among three units, i.e., DMU1, DMU4, and DMU9. According to safety documents of the plant, in the last two years, DMU1 and DMU9 have had the highest and lowest accident rates, respectively. Knowing the ranking of these units from the PCA results (Table 6), it is clear that the unit with a better rank and consequently a better safety performance has a lower occupational

Griffin, 2006).

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6. Conclusion

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accident rate. This is confirmed by the studies of Zohar (Zohar, 1980) and Neal et. al. (Neal and

As most of the work done in the field of resilience engineering contains qualitative and

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conceptual results rather than quantitative ones, there is now more interest in numerical methods to evaluate system resilience. This research aimed at the quantitative assessment of resilience safety culture within a petrochemical plant using a questionnaire and based on the PCA and the NT approaches. The findings of this study suggest that the designed questionnaire is valid and reliable enough to be used for assessing resilience safety culture. In this study, we proposed the PCA to identify the situation and the rankings of the units of a petrochemical plant with respect to resilience safety culture indicators. The NT results verified and validated the results of the PCA. As the PCA and the NT approaches are suitable for quantitative assessment of resilience safety culture, implementation of these two methods would enable the managers to recognize the 15

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current weaknesses and challenges against the resilience safety culture of their system. Because the petrochemical industry continues to be affected by a great number of accidents, embracing the opportunities afforded by safety culture and resilience engineering may be a step forward in

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improving petrochemical safety performance.

Acknowledgement

This research project has been financially supported by Ahvaz Jundishapur University of

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Medical Sciences (grant no. U-92164). The authors are grateful for the valuable comments and they would like to thank the petrochemical plant's managers, supervisors, technicians, and

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departments of the HSE and R&D for their help.

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ACCEPTED MANUSCRIPT Table 1. DMUs name in this study DMUs number

Department's Name

DMUs number

PTA1 process unit

DMU1

Process engineering

DMU7

PTA2 process unit

DMU2

Polymer Laboratory

DMU8

CF process unit

DMU3

Chemical Laboratory

DMU9

PET1 process unit

DMU4

Maintenance

DMU10

PET2 process unit

DMU5

Technical inspection

HSE

DMU6

Product bagging and loading

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Department's Name

DMU11

DMU12

SC

Table2. Description of the variables of the research Variable

Description

An atmosphere of trust that workers are encouraged to report essential safety concerns and issues (Reason, 1997) . A best practice used to ensure that safety risks are controlled when a plant makes changes in their facilities, documentation, personnel, or operations. How much does the plant respond to problems with denial versus modification?(Hollnagel et al., 2007b) A systematic process of evaluating the potential risks that may be involved in a process or activity.

Just culture

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Management of change Learning culture Risk assessment/management

Actively anticipates various threats and prepares for them

Flexibility

Ability to restructure in response to various changes and variabilities

Reporting culture

Cultivating an atmosphere where employees have confidence to report safety-related issues

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Preparedness

without fear of blame.

Management commitment

EP

Awareness Safety management system Accident investigation

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Involvement of staff Competency

Recognizing the human performance concerns and tiring to address them, devoting to safety above or to the same extent as the other goals in the plant (Hollnagel et al., 2007b), (Shirali et al., 2013). Aware of risks and systems' boundaries and know how close it is to their edge (Hollnagel et al., 2007b), (Saurin and Júnior, 2011), as well as aware of the safeguards and procedures efficiency. Systematic approach to proactively managing safety, including the necessary organizational structures, accountabilities, policies and procedures. Process of detailed and systematically collecting and analyzing information relating to an accident. How much employees are contributed in decision making and planning for safety. What an employee is capable of doing (Re and Macchi, 2010).

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Table 3. Cronbach's alpha coefficient for every variable of scale Cronbach's alpha

Just culture

7

0.840

Management of change

6

0.854

Learning culture

6

0.862

Risk assessment/management

5

Preparedness

4

Flexibility

6

Reporting culture

4

Management commitment

6

Awareness

3

Safety management system

3

Accident investigation Involvement of staff Competency

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Total scale

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Number of items

0.890

0.906

0.832

0.887

SC

0.768

0.891

0.836

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Variables

3

0.881

3

0.828

3

0.670

59

0.943

Table 4. The result of PCA related to DMU1

Eigenvalue

3.174

Proportion

0.244

Cumulative

0.244

Variable

Risk assessment/management

1.568

1.459

1.179

0.867

0.689

0.679

0.400

0.317

0.199

0.086

0.059

0.179

0.121

0.112

0.091

0.067

0.053

0.052

0.031

0.024

0.015

0.007

0.005

0. 423

0.544

0.656

0.746

0.813

0.866

0.918

0.949

0.974

0.989

0.995

1.000

PC1

PC2

PC3

PC4

PC5

PC6

PC7

PC8

PC9

PC10

PC11

PC12

PC13

0.157

0.218

-0.598

-0.120

-0.092

0.146

-0.139

-0.431

-0.217

-0.143

-0.380

-0.268

0.193

0.053

-0.491

-0.076

0.117

0.307

0.278

0.108

0.420

-0.420

0.183

-0.163

-0.316

0.204

EP

Management of change

2.324

0.144

-0.401

0.184

0.267

-0.305

0.266

-0.405

-0.002

0.436

-0.204

-0.388

0.033

0.049

Just culture

0.356

-0.294

-0.171

-0.181

-0.038

-0.502

-0.105

0.065

0.258

-0.076

0.300

-0.535

-0.087

Reporting culture

0.406

-0.031

-0.291

0.367

-0.069

-0.192

-0.100

0.186

-0.341

-0.096

-0.038

0.392

-0.498

Involvement of staff

0.216

0.036

-0.052

-0.614

-0.297

0.285

0.066

0.254

0.111

0.423

-0.179

0.065

-0.328

Management commitment

0.341

-0.342

-0.014

0.101

-0.033

0.190

0.132

-0.560

-0.013

0.413

0.372

0.233

0.161

Awareness

0.341

0.147

0.283

0.161

0.371

-0.125

0.454

-0.208

0.211

0.086

-0.480

-0.168

-0.209

Flexibility

0.135

-0.299

0.356

-0.460

-0.126

-0.259

0.125

-0.142

-0.395

-0.397

-0.192

0.237

0.170

Preparedness

0.276

0.059

-0.183

-0.271

0.632

0.084

-0.231

0.166

0.290

-0.158

0.044

0.389

0.253

Safety management system

0.309

0.250

0.376

-0.038

0.093

0.482

-0.208

-0.066

-0.224

-0.313

0.340

-0.300

-0.241

Learning culture

0.278

0.342

0.311

0.097

-0.100

-0.295

-0.435

0.126

-0.197

0.440

-0.113

-0.035

0.389

Competency

0.339

0.227

-0.089

0.160

-0.371

0.125

0.505

0.333

0.134

-0.241

0.142

0.035

0.430

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Accident investigation

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0.207

-0.071

0.094

0.181

0.126

0.153

0.261

-0.151

-0.090

0.038

-0.068

0.227

0.227

0.184

0.157

0.059

-0.215

0.188

-0.069

DMU3

-0.138

-0.030

0.163

0.207

0.098

0.142

0.116

0.168

0.122

-0.055

0.061

0.039

-0.082

DMU4

-0.068

0.090

0.085

0.142

0.107

0.128

-0.020

DMU5

0.208

0.185

0.066

0.084

0.116

0.194

0.053

DMU6

0.107

0.223

0.204

0.197

-0.206

0.117

0.126

DMU7

0.146

0.119

0.119

0.135

0.175

0.087

0.139

0.136

DMU8

0.134

0.226

0.232

0.157

-0.065

0.117

0.058

DMU9

0.134

0.047

0.022

0.172

0.138

0.183

DMU10

0.104

0.044

0.111

0.036

-0.140

DMU11

-0.060

0.050

0.043

0.204

DMU12

0.146

0.175

0.208

0.261

0.226

0.232

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SC 0.104

0.170

0.058

0.093

0.191

0.206

0.126

0.061

0.005

-0.026

-0.065

0.046

0.079

0.219

0.108

0.128

0.140

0.135

0.079

0.029

0.075

0.185

-0.029

0.043

-0.035

0.060

0.082

0.046

0.055

0.040

-0.011

0.057

0.223

0.167

0.172

-0.012

0.061

0.121

0.096

-0.037

-0.012

-0.143

0.183

0.076

0.048

0.099

-0.165

-0.004

-0.021

-0.126

-0.126

-0.203

0.030

0.067

0.101

0.141

0.190

0.193

0.045

0.145

0.107

-0.006

0.207

0.175

0.227

0.227

0.226

0.193

0.219

0.223

0.188

0.191

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0.226

Best

Table 6. The final PCA scores for all DMUs

EP

DMU

PCA final score

Ranking by PCA

DMU1

5.226

10

DMU2

14.575

8

DMU3

12.675

9

DMU4

16.663

7

DMU5

16.929

5

DMU6

25.960

1

DMU7

25.252

2

DMU8

16.668

6

DMU9

21.470

4

DMU10

4.870

11

DMU11

0.560

12

DMU12

21.505

3

Best practice

39.145

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practice

Competency

0.019

Learning culture

Preparedness

-0.004

system

Flexibility

0.074

Safety management

Involvement of staff

-0.038

Awareness

Reporting culture

-0.002

commitment

Just culture

-0.010

Management

Accident investigation

-0.044

DMU2

/management

DMU1

Variable

Risk assessment

DMU

Management of change

Table 5. The values of principal components for all DMUs

ACCEPTED MANUSCRIPT Table 7. Distance matrix for DMUs DMU2

DMU3

DMU4

DMU5

DMU6

DMU7

DMU8

DMU9

DMU10

DMU11

DMU12

DMU1

0

2.841

4.721

4.253

3.287

7.867

6.759

5.851

5.519

3.710

5.599

5.454

DMU2

2.841

0

3.948

2.758

2.613

5.979

5.693

3.654

3.827

2.756

5.005

4.161

DMU3

4.721

3.948

0

3.896

4.248

6.586

5.717

4.359

4.222

4.233

5.286

4.990

DMU4

4.253

2.758

3.896

0

2.916

5.459

4.589

3.531

3.341

2.948

5.685

4.151

DMU5

3.287

2.613

4.248

2.916

0

7.459

6.582

4.854

4.863

2.883

4.311

3.739

DMU6

7.867

5.979

6.586

5.459

7.459

0

4.692

4.367

4.222

7.202

9.715

7.025

DMU7

6.759

5.693

5.717

4.589

6.582

4.692

0

3.257

4.255

6.191

8.131

6.356

DMU8

5.851

3.654

4.359

3.351

4.854

4.367

3.257

0

3.334

4.225

6.019

4.992

DMU9

5.519

3.827

4.222

3.341

4.863

4.222

4.255

3.334

0

5.077

7.133

4.165

DMU10

3.710

2.756

4.233

2.948

2.883

7.202

6.191

4.225

5.077

0

4.029

4.954

DMU11

5.599

5.005

5.286

5.685

4.311

9.715

8.131

6.019

7.133

4.029

0

5.913

DMU12

5.454

4.161

4.990

4.151

3.739

7.025

6.356

4.992

4.165

4.954

5.913

0

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SC

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DMU1

Table 8. Ranking of DMUs by NT Distance of DMUs from

DMU1

ideal unit

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DMU

fi

Ranking by the NT

5.226

0.801

11

14.575

0.628

6

12.675

0.659

7

16.663

0.557

5

DMU5

16.929

0.745

10

DMU6

25.960

0.215

1

DMU7

25.252

0.376

2

DMU8

16.668

0.423

3

DMU9

21.470

0.450

4

DMU10

4.870

0.726

9

DMU11

0.560

0.916

12

DMU12

21.505

0.687

8

DMU2 DMU3

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EP

DMU4

ACCEPTED MANUSCRIPT PCA rank

NT rank

DMU1

10

11

DMU2

8

6

DMU3

9

7

DMU4

7

5

DMU5

5

10

DMU6

1

1

DMU7

2

2

DMU8

6

3

DMU9

4

4

DMU10

11

9

DMU11

12

12

DMU12

3

8

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EP

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SC

DMU

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Table 9. PCA and NT ranking of DMUs

ACCEPTED MANUSCRIPT •

Resilience aspects were considered in this study. In addition, dynamic interactions were evaluated among people, technology, and administration. Resilience safety culture was assessed in several aspects, and not only one aspect.

•

We measure quantitatively resilience safety culture.

•

Poor units were ranked in terms of the resilience safety culture indicators.

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•