Key Factors for a Successful OBM Transformation with DEMATEL–ANP

Nguyen, Tien Son; Chen, Jen‐Ming; Tseng, Shih-Hsien; Lu, Li-Cheng

doi:10.3390/math11112439

Cited by 4 publications

(3 citation statements)

References 63 publications

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“…This study proposed a new bank performance model under the assumption of country risk, based on a multi-analytical effort that included PCA in a fuzzy-ANP model. Fuzzy applications [70,71,73,87] and ANPs [41,43,[45][46][47][48][49][50][51] are valuable techniques due to their decision-making abilities. Nonetheless, certain authors have highlighted the need to address inconsistency issues associated with the complexity of the method [45,46], showcasing a gap in research.…”

Section: Discussionmentioning

confidence: 99%

“…Fuzzy-ANP has been applied in an expansive set of contexts, ranging from supplier choice [45], to the selection of an outsourcing provider [47], to engineering decisions [48]. Moreover, fuzzy-ANP provides a flexible method that has been extended in different frameworks, including DEMATEL [45,47,[49][50][51], TOPSIS [45], and ELECTRE-IS [52]. Considering these validated extensions, in this research, we propose a new framework based on fuzzy-ANP with PCA to assess bank performance and country risk.…”

Section: Bank Performance and Country Riskmentioning

confidence: 99%

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Fuzzy Analytic Network Process with Principal Component Analysis to Establish a Bank Performance Model under the Assumption of Country Risk

et al. 2023

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In recent years, bank-related decision analysis has reflected a relevant research area due to key factors that affect the operating environment of banks. This study’s aim is to develop a model based on the linkages between the performance of banks and their operating context, determined by country risk. For this aim, we propose a multi-analytic methodology using fuzzy analytic network process (fuzzy-ANP) with principal component analysis (PCA) that extends existing mathematical methodologies and decision-making approaches. This method was examined in two studies. The first study focused on determining a model for country risk assessment based on the data extracted from 172 countries. Considering the first study’s scores, the second study established a bank performance model under the assumption of country risk, based on data from 496 banks. Our findings show the importance of country risk as a relevant bank performance dimension for decision makers in establishing efficient strategies with a positive impact on long-term performance. The study offers various contributions. From a mathematic methodology perspective, this research advances an original approach that integrates fuzzy-ANP with PCA, providing a consistent and unbiased framework that overcomes human judgement. From a business and economic analysis perspective, this research establishes novelty based on the performance evaluation of banks considering the operating country’s risk.

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Section: Discussionmentioning

confidence: 99%

Section: Bank Performance and Country Riskmentioning

confidence: 99%

Fuzzy Analytic Network Process with Principal Component Analysis to Establish a Bank Performance Model under the Assumption of Country Risk

et al. 2023

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“…For example, the literature [11][12][13] used PCA or regression analysis to identify key influencing factors in different scenarios, and the datasets used only contained 5, 10, and 15 variables, respectively. The experimental simulation method and DEMATEL method are considered the two main methods used to identify the key factors influencing complex systems [14][15][16], but each has its own shortcomings, which will be discussed in detail in Section 2.…”

Section: Introductionmentioning

confidence: 99%

Key Influencing Factors Identification in Complex Systems Based on Heuristic Causal Inference

Wu,

Lu,

et al. 2023

Applied Sciences

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In complex systems constrained by multiple factors, it is very important to identify the key influencing factors for mastering the evolution and development law of a system and for obtaining scientific decision-making suggestions or schemes. At present, the method based on experimental simulation is limited by the difficulty of system model construction; DEMATEL (Factual Decision Trial and Evaluation Laboratory) is inevitably influenced by subjective factors. In view of this, we propose a novel model based on heuristic causal inference. By combining the network analysis in complex network science, the model defines the global/local causal pathway and the causal pathway’s length in the causal network and takes the causal pathway contribution degree as an indicator to measure the approximate causal effects. The model includes steps such as causal network learning, causal pathway contribution degree calculation, and key influencing factor identification. The model uses the Fast Causal Inference (FCI) algorithm with prior knowledge to learn the global causal network of the complex system and uses the heuristic causal inference to calculate the causal pathway contribution degree. The heuristic method draws on the idea of complex network topology analysis and measures the influence degree between variables by the number and distance of causal pathways. The key influencing factors are finally identified according to the causal pathway contribution degree. Based on the SECOM dataset, we carried out simulation experiments and demonstrated the feasibility and effectiveness of the proposed method.

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