How financial technology (fintech) can improve the business performance of securities firms by using the dynamic data envelopment analysis modified model

2021 ◽  
Author(s):  
Hong‐Jing Lin ◽  
Che‐Chien Chen ◽  
Yung‐ho Chiu ◽  
Tai‐Yu Lin
Keyword(s):  
Data Envelopment Analysis ◽  
Business Performance ◽  
Data Envelopment ◽  
Dynamic Data ◽  
Modified Model ◽  
Dynamic Data Envelopment Analysis ◽  
Financial Technology

Evaluating and ranking sustainable suppliers by robust dynamic data envelopment analysis

Measurement ◽  
2016 ◽  
Vol 83 ◽  
pp. 72-85 ◽  
Author(s):  
Saeed Yousefi ◽  
Hadi Shabanpour ◽  
Ron Fisher ◽  
Reza Farzipoor Saen
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Dynamic Data ◽  
Dynamic Data Envelopment Analysis

Measuring cost efficiency in the presence of quasi-fixed inputs using dynamic Data Envelopment Analysis: The case of port infrastructure

2014 ◽  
Vol 16 (2) ◽  
pp. 111-126 ◽  
Author(s):  
Juan José Díaz-Hernández ◽  
Eduardo Martínez-Budría ◽  
Juan José Salazar-González
Keyword(s):  
Data Envelopment Analysis ◽  
Cost Efficiency ◽  
Data Envelopment ◽  
Dynamic Data ◽  
Dynamic Data Envelopment Analysis ◽  
Port Infrastructure

scholarly journals The efficiency of private pension companies using dynamic data envelopment analysis

2020 ◽  
Vol 4 (2) ◽  
pp. 204-219
Author(s):  
Yonca Erdem Demirtaş ◽  
◽  
Neslihan Fidan Keçeci
Keyword(s):  
Data Envelopment Analysis ◽  
Private Pension ◽  
Data Envelopment ◽  
Dynamic Data ◽  
Dynamic Data Envelopment Analysis

scholarly journals Hesitant Fuzzy-Stochastic Data Envelopment Analysis (HF-SDEA) Model for Benchmarking

2021 ◽  
Vol 5 (1) ◽  
pp. 94
Author(s):  
Dahlan Abdullah ◽  
Hartono ◽  
Cut Ita Erliana
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Fuzzy Model ◽  
Assessment Process ◽  
Data Envelopment ◽  
Dynamic Data ◽  
Decision Making Unit ◽  
Dynamic Data Envelopment Analysis ◽  
Dea Method ◽  
Stochastic Data

The Data Envelopment Analysis (DEA) method is a method commonly used in benchmarking. The Dynamic Data Envelopment Analysis (DDEA) method was proposed to improve the DEA method in the benchmarking process. The DDEA method proposed can determine the effectiveness of the Decision Making Unit (DMU). The disadvantage of the DDEA model is that it cannot handle problems that involve benchmarking for stochastic data. To improve the DDEA method, the Stochastic Data Envelopment Analysis (SDEA) method is proposed which can be used for benchmarking involving stochastic data. The SDEA method itself has weaknesses in dealing with noise and uncertainty problems that will appear in the assessment process. The purpose of the research conducted by the researcher was to use the Hesitant Fuzzy method in optimizing the SDEA method so that the Hesitant Fuzzy model - Stochastic Data Envelopment Analysis (HF-SDEA) could be carried out benchmarking process in a situation where the assessment contained many elements of uncertainty. The results of this study are benchmarking methods that can do benchmarking for stochastic data on conditions that contain elements of uncertainty.

Inverse Dynamic Data Envelopment Analysis for Evaluating Faculties of University with Quasi-Fixed Inputs

2019 ◽  
Vol 148 (1) ◽  
pp. 323-347
Author(s):  
Samira Foladi ◽  
Maghsud Solimanpur ◽  
Mustafa Jahangoshai Rezaee
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Inverse Dynamic ◽  
Dynamic Data ◽  
Dynamic Data Envelopment Analysis

scholarly journals A longitudinal Perspective on Efficiency of Airlines in Europe and the U.S

2021 ◽  
Vol 4 (2) ◽  
pp. 11-24
Author(s):  
Minh-Anh Nguyen Thi
Keyword(s):  
Data Envelopment Analysis ◽  
Marketing Strategies ◽  
Early Age ◽  
Employee Productivity ◽  
Data Envelopment ◽  
Publicly Traded ◽  
Dynamic Data ◽  
The Us ◽  
Dynamic Data Envelopment Analysis ◽  
The U.S

The aviation industries in Europe and the US have been well-established since a very early age and have attracted great attention from both industry practitioners and academics. To derive a different perspective on the efficiency levels of airlines operating in the two matured markets, we adopted dynamic data envelopment analysis (DEA). Using the data of the period 2014 – 2016 of 7 European airlines and 9 US airlines that are publicly traded, the study offers an overall picture of airlines' efficiency in the two regions. Notably, the resource flow between the consecutive periods is incorporated into the measure to yield a longitudinal perspective on airlines' efficiency. The study reveals the two major findings. First, most publicly traded airlines in Europe and the US are efficient, except for Hawaiian airline headquartered in the US. Second, Hawaiian airline's inefficiency is majorly contributed by the overuse of the number of employees, consumed fuel, and the deficit of revenue seat-miles in 2014 and 2015. To improve the efficiency level, Hawaiian airlines could consider increasing employee productivity, using more fuel-efficient aircraft, and implementing new marketing strategies to boost sales.

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