Study on the influencing factors of the flight crew’s TSA based on DEMATEL–ISM method

Meng, Bin; Lu, Na; Lin, Chen; Zhang, Yunjing; Si, Qingmin; Zhang, Jinsong

doi:10.1007/s10111-021-00688-7

Cited by 10 publications

(8 citation statements)

References 43 publications

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“…From a content perspective, the use of a combined DEMATEL-ISM-MICMAC approach to constructing artificial intelligence healthcare service resources adoption problems using a pensions perspective as a context is currently a gap in academia. [58][59][60][61][62][63][64]47,[65][66][67][68][69][70][71][72][73][74][75][76][77] Most studies have focused on the ISM-MICMAC approach to constructing explanatory models without answering the relationships within the factors from a micro perspective, which is an incomplete study for constructing models with a large number of factors. From a contextual perspective, the context of research using a combination of the three DEMATEL-ISM-MICMAC methods is still a gap in the healthcare and elderly care field.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence healthcare service resources adoption by medical institutions based on TOE framework

2022

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Objectives This study used the Technology-Organization-Environment (TOE) framework to identify the factors involved in the decisions made by integrated medical and healthcare organizations to adopt artificial intelligence (AI) elderly care service resources. Method This study identified the Decision-making Trial and Evaluation Laboratory-Interpretive Structural Modeling (DEMATEL-ISM) method was used to construct a multilayer recursive structural model and to analyze the interrelationships between the levels. A MICMAC quadrant diagram was used for a cluster analysis. Results The ISM recursive structural model was divided into a total of seven layers. The bottom layer contained the four factors of High risk of data leakage (T1), Lack of awareness of the value and benefits of AI healthcare technology (T5), Lack of management leadership support (O1), and Government policies (E1). Having a low dependency but high driving force, these factors are the root causes of adoption by healthcare organizations. The topmost layer contained the most direct factors, which had a high dependency but the low driving force, influencing adoption: Competitive pressures (E2), Lack of patient trust (E5), and Lack of excellent partnerships (E7). Healthcare organizations are more concerned with technology and their environments when deciding to adopt intelligent healthcare resources. Conclusion The combination of the three methods of DEMATEL-ISM-MICMAC construction models provides new ideas for smart healthcare services for hospitals. The DEMATEL method favors the construction dimension of the micro-model, while the ISM method favors the construction dimension of the macro-model. Combining these two methods may reduce the loss of information within the system, simplify the matrix calculation workload, and improve the efficiency of operations while decomposing the complex problems into several sub-problems in a more comprehensive and detailed way. Conducting cluster analysis of the adoption determinants utilizing MICMAC quadrant diagrams may provide strong methodological guidance and decision-making recommendations for government departments, senior decision-makers in healthcare organizations, and policy-makers in associations in the senior care industry.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence healthcare service resources adoption by medical institutions based on TOE framework

2022

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“…Topological diagrams show the function paths between factors, such as F9 → F8 → F3 → F6 → F16 → F15 → F17, reflecting the complex interaction among factors [ 48 , 68 ]. Although not act directly, these intermediate-influencing factors play intermediary roles in the transmission from the fundamental-influencing factors to novice pilot SA.…”

Section: Discussionmentioning

confidence: 99%

“…The intermediate-influencing factors are the medium between the direct-influencing factors and the root-influencing factors. The root-influencing factors have the most fundamental and root influence, and they are the Pareto optimal factors that should deserve the highest priority in theory [ 48 ].…”

Section: Methodsmentioning

confidence: 99%

“…The interaction among various factors and their comprehensive impact on pilot SA are rarely involved, limiting the application potential of current studies. A workable solution is to use the systematic approach [ [48] , [49] , [50] ] to establish the influencing factor index system of pilot SA and to further explore the inherent interrelationship and interaction mechanism of these factors. This can be regarded as a direct reference to analyze the causes of pilot SA degradation and propose reasonable strategies for pilot SA improvement.…”

Section: Introductionmentioning

confidence: 99%

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Influencing factors of novice pilot SA based on DEMATEL-AISM method: From pilots’ view

Chen¹,

Liu²,

Wanyan³

et al. 2023

Heliyon

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“…Step 5: Establish a multi-level hierarchical structure model. A multi-level hierarchical structure model can be obtained by layering the variables according to the hierarchical order and then connecting the interaction relationship between the variables with the directed vector line [25].…”

Section: Interpretative Structural Modeling (Ism)mentioning

confidence: 99%

Risk Analysis of Airplane Upsets in Flight: An Integrated System Framework and Analysis Methodology

Meng

2023

Aerospace

Self Cite

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Generally, airplane upsets in flight are considered a precursor to loss of control in flight (LOC-I) accidents, and unfortunately LOC-I is classified as the leading cause of fatal accidents. To further explore the risk factors, causal relationships, and coupling mechanism of airplane upsets, this study proposed a risk analysis model integrating the Interpretative Structural Modeling (ISM) and Bayesian Network (BN). Seventeen key risk factors leading to airplane upsets were identified through the analysis of typical accident cases and the literature. The ISM approach was used to construct the multi-level interpretative structural model of airplane upsets, which could reveal the causal relationship among various risk factors and risk propagation paths. Then, taking 286 accident/incident investigation data as training samples, a data-driven BN model was established using machine learning for dependency intensity assessment and inference analysis. The results reveal that the interaction among risk factors of fatal accidents caused by airplane upsets is more significant than that of non-fatal accidents/incidents. Risk factors such as pilot-induced oscillations/airplane-pilot coupling and non-adherence to Standard Operating Procedures (SOPs)/neglect of cross-validation have a significant effect on airplane upsets in flight among seventeen risk factors. Moreover, this study also identifies the most likely set of risk factors that lead to fatal accidents caused by airplane upsets. The research results have an important theoretical significance and application value for preventing airplane upsets risk.

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Study on the influencing factors of the flight crew’s TSA based on DEMATEL–ISM method

Cited by 10 publications

References 43 publications

Artificial intelligence healthcare service resources adoption by medical institutions based on TOE framework

Artificial intelligence healthcare service resources adoption by medical institutions based on TOE framework

Influencing factors of novice pilot SA based on DEMATEL-AISM method: From pilots’ view

Risk Analysis of Airplane Upsets in Flight: An Integrated System Framework and Analysis Methodology

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