scholarly journals Safety Assessment of High-Risk Operations in Hydroelectric-Project Based on Accidents Analysis, SEM, and ANP

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Jian-Lan Zhou ◽  
Bai Zhe-Hua ◽  
Zhi-Yu Sun
Keyword(s):  
Risk Factor ◽  
High Risk ◽  
Human Factors ◽  
Structural Equation ◽  
Human Error ◽  
Safety Management ◽  
Equation Modeling ◽  
Path Coefficient ◽  
Correlation Relationship ◽  
Hydroelectric Project

Safety risk analysis and assessment of high-risk work system in hydroelectric project has an important role in safety management. The interactive relationships between human factors and the importance of factors are analyzed and proposed. We analyze the correlation relationship among the factors by using statistical method, which is more objective than subjective judgment. The HFACS is provided to establish a rational and an applicable index system for investigating human error in accidents; the structural equation modeling (SEM) and accident data are used to construct system model and acquire the path coefficient among the risk factor variables; the ANP model is built to assess the importance of accident factors. 289 pieces of valid questionnaires data are analyzed to obtain the path coefficient between risk factor variables and to build the ANP model’s judgment matrix. Finally, the human factors’ weights are calculated by ANP model. Combining SEM’s results and factor's frequency analysis and building the ANP model, the results show that the four greatest weight values of the factors are, respectively, “personal readiness,” “perception and decision errors,” “skill-based errors,” and “violation operations.” The results of ANP model provide a reference for the engineering and construction management.

scholarly journals Path Analysis of Causal Factors Influencing Marine Traffic Accident via Structural Equation Numerical Modeling

2019 ◽  
Vol 7 (4) ◽  
pp. 96 ◽  
Author(s):  
Shenping Hu ◽  
Zhuang Li ◽  
Yongtao Xi ◽  
Xunyu Gu ◽  
Xinxin Zhang
Keyword(s):  
Human Factors ◽  
Structural Equation ◽  
Human Error ◽  
Structural Model ◽  
Causal Chain ◽  
Equation Modeling ◽  
Prevention Measures ◽  
Hypothetical Model ◽  
Causal Factors ◽  
Marine Traffic

Many causal factors to marine traffic accidents (MTAs) influence each other and have associated effects. It is necessary to quantify the correlation path mode of these factors to improve accident prevention measures and their effects. In the application of human factors to accident mechanisms, the complex structural chains on causes to MTA systems were analyzed by combining the human failure analysis and classification system (HFACS) with theoretical structural equation modeling (SEM). First, the accident causation model was established as a human error analysis classification in sight of a MTA, and the constituent elements of the causes of the accident were conducted. Second, a hypothetical model of human factors classification was proposed by applying the practice of the structural model. Third, with the data resources from ship accident cases, this hypothetical model was discussed and simulated, and as a result, the relationship path dependency mode between the latent independent variable of the accident was quantitatively analyzed based on the observed dependent variable of human behavior. Application examples show that relationships in the HFACS are verified and in line with the path developing mode, and resource management factors have a pronounced influence and a strong relevance to the causal chain of the accidents. Appropriate algorithms for the theoretical model can be used to numerically understand the safety performance of marine traffic systems under different parameters through mathematical analysis. Hierarchical assumptions in the HFACS model are quantitatively verified.

scholarly journals Path Analysis of Causal Factors Influencing Marine Traffic Accident via Structural Equation Numerical Modeling

2019 ◽  
Author(s):  
Shenping Hu ◽  
Zhuang Li ◽  
Yongtao Xi ◽  
Xunyu Gu ◽  
Xinxin Zhang
Keyword(s):  
Human Factors ◽  
Structural Equation ◽  
Human Error ◽  
Structural Model ◽  
Causal Chain ◽  
Equation Modeling ◽  
Prevention Measures ◽  
Hypothetical Model ◽  
Causal Factors ◽  
Marine Traffic

Many causal factors to marine traffic accidents (MTA) influence each other and have associated effects. It is necessary to quantify the correlation path mode of these factors to improve accident prevention measures and their effects. In the application of human factors to the accident mechanisms, the complex structural chains on causes to MTA systems were analyzed combining the Human Failure Analysis and Classification System (HFACS) with theoretical Structural Equation Modeling (SEM). First, the accident causation model was established as a human error analysis classification in sight of MTA, and the constituent elements of the causes of accident was conducted. Second, a hypothetical model of Human factors classification was proposed applying the practice of the structural model. Third, with the data resource from ship accident cases, this hypothetical model was discussed and simulated, and as a result the relationship path dependency mode between the latent independent variable of the accident was quantitatively analyzed based on the observed dependent variable of human behaviors. Application examples show that relationships in HFACS are verified and in line with the path developing mode, and resource management factors have a pronounced influence and a strong relevance to the causal chain of the accidents. Appropriate algorithms for the theoretical model can be used to numerically understand the safety performance of marine traffic systems under different parameters through mathematical analysis. Hierarchical assumptions in the HFACS model are quantitatively verified.

scholarly journals Path Analysis of Causal Factors Influencing Marine Traffic Accident via Structural Equation Numerical Modeling

2019 ◽  
Author(s):  
Shenping Hu ◽  
Zhuang Li ◽  
Yongtao Xi ◽  
Xunyu Gu ◽  
Xinxin Zhang
Keyword(s):  
Human Factors ◽  
Structural Equation ◽  
Human Error ◽  
Structural Model ◽  
Causal Chain ◽  
Equation Modeling ◽  
Prevention Measures ◽  
Hypothetical Model ◽  
Causal Factors ◽  
Marine Traffic

Many causal factors to marine traffic accidents (MTA) influence each other and have associated effects. It is necessary to quantify the correlation path mode of these factors to improve accident prevention measures and their effects. In the application of human factors to the accident mechanisms, the complex structural chains on causes to MTA systems were analyzed combining the Human Failure Analysis and Classification System (HFACS) with theoretical Structural Equation Modeling (SEM). First, the accident causation model was established as a human error analysis classification in sight of MTA, and the constituent elements of the causes of accident was conducted. Second, a hypothetical model of Human factors classification was proposed applying the practice of the structural model. Third, with the data resource from ship accident cases, this hypothetical model was discussed and simulated, and as a result the relationship path dependency mode between the latent independent variable of the accident was quantitatively analyzed based on the observed dependent variable of human behaviors. Application examples show that relationships in HFACS are verified and in line with the path developing mode, and resource management factors have a pronounced influence and a strong relevance to the causal chain of the accidents. Appropriate algorithms for the theoretical model can be used to numerically understand the safety performance of marine traffic systems under different parameters through mathematical analysis. Hierarchical assumptions in the HFACS model are quantitatively verified.

scholarly journals Is Worse Attention a Risk Factor for or a Consequence of Depression, or Are Worse Attention and Depression Better Accounted for by Stress? A Prospective Test of Three Hypotheses

2018 ◽  
Vol 7 (1) ◽  
pp. 93-109 ◽  
Author(s):  
Naoise Mac Giollabhui ◽  
Thomas M. Olino ◽  
Johanna Nielsen ◽  
Lyn Y. Abramson ◽  
Lauren B. Alloy
Keyword(s):  
Risk Factor ◽  
Depressive Symptoms ◽  
Life Stress ◽  
Structural Equation ◽  
Equation Modeling ◽  
Childhood Stress ◽  
Attentional Functioning ◽  
First Onset ◽  
Impaired Cognition ◽  
Switching Attention

It is unclear whether impaired cognition is a risk factor for depression or a consequence of depression, or whether both depression and impaired cognition are caused by a third underlying process (e.g., stress). These three hypotheses were tested in 523 adolescents assessed annually for depression, attentional functioning, and childhood/recent life stress. Baseline switching, sustained, and selective attention did not predict first onset of depression or depressive symptoms. Divided attention predicted depressive symptoms only. Piecewise growth modeling indicated that the trajectory of switching attention declined prior to first onset of depression; there was evidence of significant recovery in switching attention following first onset of depression. Structural equation modeling indicated that impaired switching attention prospectively predicted higher depressive symptoms and that higher depressive symptoms predicted worse selective and switching attention. Further, childhood stress prospectively predicted higher depressive symptoms via switching attention and worse switching attention via depressive symptoms.

scholarly journals The Identification of Factors Affecting the Use of Pressurized Irrigation Systems by Farmers in Iran

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1532 ◽  
Author(s):  
Abdullah Momvandi ◽  
Maryam Omidi Najafabadi ◽  
Jamal Hosseini ◽  
Farhad Lashgarara
Keyword(s):  
Structural Equation ◽  
Agricultural Sector ◽  
Water Productivity ◽  
Equation Modeling ◽  
Path Coefficient ◽  
Mixed Method Research ◽  
Statistical Research ◽  
Irrigation Systems ◽  
Factors Affecting ◽  
Pressurized Irrigation

Climate change and water scarcity are the most important challenges of the agricultural sector, and pressurized irrigation systems (PISs) are one of the most significant ways to improve agricultural water productivity. The main purpose of this research was to identify the factors affecting the use of PISs by farmers. The statistical research population was a total of 2396 Iranian model farmers. The Cochran formula was used to determine the number of statistical samples. Accordingly, this comprised 331 people. The methodology of the study was mixed method research. The structural equation modeling technique, Mann–Whitney U, and Kruskal–Wallis tests were used to test the hypotheses. The results showed that the personal characteristics, tendency, attitude, self-efficacy, subjective norms, governmental support, environmental tensions, and technological features were the most important factors which influenced the farmers. It was found that all of these variables had a positive and significant relationship with the using of PISs by farmers, and they were able to predict 52% of the behavioral changes (R2) of the farmers. Among these variables, the attitude, with a path coefficient (β) of 0.48, had the highest impact on the using of PISs by the farmers.

scholarly journals Relationship between Social Capital and Job Performance among Faculty Members of a Medical University: The Moderating Role of Entrepreneurship Behavior

2020 ◽  
Author(s):  
Roohollah Kalhor ◽  
Nadia Neysari ◽  
Saeed Shahsavari ◽  
Sima Rafiei
Keyword(s):  
Social Capital ◽  
Job Performance ◽  
Structural Equation ◽  
Faculty Members ◽  
Equation Modeling ◽  
Path Coefficient ◽  
Medical Sciences ◽  
Entrepreneurial Behavior ◽  
Moderating Role

Abstract Background Job performance is an important organizational factor that plays a significant role in the success of organizations. This study aims to investigate the moderating role of entrepreneurial behavior in the relationship between social capital and job performance among faculty members of Qazvin University of Medical Sciences. Methods This is a descriptive-analytical study which has been conducted through a structural equation modeling among all university faculty members working in different faculties of Qazvin University of Medical Sciences in 2017. To evaluate the causal relationships between study variables, Structural Equation Modeling (SEM) on AMOS software, with the significant level of 0.05 was used. Results Findings indicated that entrepreneurial behaviors and social capital could predict job performance. The direct effect of social capital on job performance (path coefficient: 0.17) and its indirect effect with the moderating role of entrepreneurial behavior (path coefficient: 0.39) were confirmed (P< 0.05). Furthermore, Sobel test affirmed the indirect associations between variables (P< 0.05). Conclusions Strengthening social capital and promoting entrepreneurial behavior can lead to higher levels of performance. Building trust among organizational members and designing new incentive methods which use entrepreneurial indicators for performance evaluation can improve social capital. Therefore, managers can contribute to the improvement of job performance through developing entrepreneurial behavior among their employees.

scholarly journals Faktor - Faktor yang Memengaruhi Perilaku Konsumen Produk Kosmetik Halal di Kota Bogor

2019 ◽  
Vol 9 (1) ◽  
pp. 59
Author(s):  
Stevia Septiani ◽  
Retno Indraswari
Keyword(s):  
Women Workers ◽  
Latent Variable ◽  
Structural Equation ◽  
Descriptive Analysis ◽  
Sem Analysis ◽  
Primary Data ◽  
Equation Modeling ◽  
Path Coefficient ◽  
Purchasing Decisions ◽  
Positive Effect

The halal cosmetics industry has a very potential trend both globally and nationally. In Indonesia, the halal cosmetics industry is one of the industries that contribute to the improvement of the Islamic economy. Along with the development of public knowledge about organic products that are environmentally friendly and do not contain animal ingredients, the demand of halal cosmetics products are increase.Unfortunately, the great potential of the halal industry cannot be used properly by local brand. This study aims to analyze some factors that related in halal purchasing decisions. The primary data collection in this study was carried out by purposive sampling method which is women workers. Data processing methods include descriptive analysis and Structural Equation Modeling (SEM) analysis with Partial Least Squares (PLS) approach. The results of SEM analysis shown that the Psychological latent variable has a direct positive effect to Purchasing, with a path coefficient of 0.603. Psychological aspects are a relevant factor in the halal cosmetic purchasing as halal using motifs could be reflect fulfillment an attractive confession as female workers.

scholarly journals Predictive Model of High-risk Behaviors in Iranian Male Adolescents Based on Psychological Motivation

2021 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Sajad Aminimanesh ◽  
Ali Asghar Hayat ◽  
Mostafa Khanzadeh ◽  
Mehdi Taheri
Keyword(s):  
Structural Equation Modeling ◽  
High Risk ◽  
Predictive Model ◽  
Risk Behaviors ◽  
Risk Taking ◽  
Structural Equation ◽  
Therapeutic Interventions ◽  
Equation Modeling ◽  
High Risk Behaviors ◽  
Male Adolescents

Background: Awareness of people’s motivations for committing high-risk behaviors helps to explain the underlying causes and provides a framework for their use in preventive and therapeutic interventions. Objectives: This study aimed to evaluate the predictive model of high-risk behaviors in adolescents based on their motivations. Methods: The present research has a correlational design and uses structural equation modeling. The sample included 450 male students selected through a convenience sampling method to complete the Iranian Adolescents’ Risk-taking and Motives for Risk-taking scale. Data were analyzed using structural equation modeling. Results: The results showed that thrill-seeking, calculation, audience control, irresponsibility, and hedonistic motivation had significant relationships with high-risk behaviors. Also, except for attention-seeking, other motivations could significantly contribute to the prediction of high-risk behaviors. Also, the motivations had the strongest impact on alcohol consumption and the minimum impact on smoking. Finally, motivations generally explained 44% of the high-risk behaviors variance. Conclusions: Considering the role of motivations in doing high-risk behaviors, more attention should be given to these factors in preventive and therapeutic interventions.

scholarly journals The Moderating Role of Entrepreneurship Behavior in the Relationship Between Social Capital and Job Performance: A study among Faculty Members of a Health University

Health Scope ◽  
2020 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Rohollah Kalhor ◽  
Fariba Hashemi ◽  
Nadia Neysari ◽  
Saeed Shahsavari ◽  
Sima Rafiei
Keyword(s):  
Social Capital ◽  
Job Performance ◽  
Structural Equation ◽  
Faculty Members ◽  
Equation Modeling ◽  
Path Coefficient ◽  
Medical Sciences ◽  
Entrepreneurial Behavior ◽  
Moderating Role

Background: Job performance is an important organizational factor that plays a significant role in the success of organizations. Objectives: This study aimed to investigate the moderating role of entrepreneurial behavior in the association between social capital and job performance among faculty members of the Qazvin University of Medical Sciences. Methods: This is a cross-sectional, analytical study that is conducted using a structural equation modeling on 260 university faculty members in different schools of Qazvin University of Medical Sciences in 2017. To evaluate the causal relationships between study variables, Structural Equation Modeling Modeling (SEM) on AMOS software, with a significant level of 0.05, was used. Results: The findings indicated that entrepreneurial behaviors and social capital are good predictors for job performance. The direct effect of social capital on job performance (path coefficient: 0.17) and its indirect effect with the moderating role of entrepreneurial behavior (path coefficient: 0.39) were confirmed (P < 0.05). Furthermore, the Sobel test affirmed the indirect associations between variables (P < 0.05). Conclusions: Strengthening social capital and promoting entrepreneurial behavior improve overall performance. Trust-building among staff and designing new motivation methods, which use entrepreneurial indicators for performance evaluation, can improve social capital. Therefore, managers can contribute to the improvement of job performance through developing entrepreneurial behavior among their employees.

scholarly journals Effect analysis of the driving factors of super-gentrification using structural equation modeling

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248265
Author(s):  
Jiangang Shi ◽  
Kaifeng Duan ◽  
Quanwei Xu ◽  
Jiajia Li
Keyword(s):  
Social Factors ◽  
Structural Equation ◽  
Driving Factors ◽  
Sustainable Urban Development ◽  
Practical Significance ◽  
Equation Modeling ◽  
Path Coefficient ◽  
Economic Factors ◽  
Political Factors ◽  
Spatial Factors

The study of super-gentrification has important practical significance for maintaining social fairness, spatial justice and achieving sustainable urban development. In this article, 23 driving factors influencing super-gentrification are identified by literature research and Delphi method. Then, the 23 driving factors affecting super-gentrification are divided into four dimensions: political, economic, social and spatial dimension. On this basis, hypotheses are proposed and a structural equation model is established. Then, SPSS 25.0 and AMOS 24.0 software are used to test the reliability and validity of the questionnaire data, and the model results are fitted and modified. Finally, the optimization model and path coefficient of super-gentrification driving factors are calculated. The results of the study show that political factors, economic factors, social factors, and spatial factors, all play a positive role in the development of super-gentrification. Social factors are the fundamental factors to promote super-gentrification, political factors, economic factors, and spatial factors also play a key role in the super-gentrification process.

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