scholarly journals The Efficiency of Employment Relationship: Through Data Envelopment Analysis

2020 ◽  
Author(s):  
Kunyi Wang ◽  
Ping Liu ◽  
Yujiang Liu
Keyword(s):  
Data Envelopment Analysis ◽  
Social Exchange ◽  
Exchange Process ◽  
Employment Relationship ◽  
Data Envelopment ◽  
Exchange Relationship ◽  
Simple Description ◽  
Importance Ranking ◽  
Actual Exchange ◽  
First Time

Employment relationship (ER) is a social exchange relationship in nature with uncertainty and incongruence during the exchange process. Previous studies has been stuck on regression-based methods, examining and exploring related issue by studying the exchanges of both parties and even more stakeholders, but the nature of the exchange process itself is ignored. This study jumps out of the causality study represented by the regression analysis method, and uses the FCE that reflects the essence of object better and the Data Envelopment Analysis (DEA) to measure the efficiency of the exchange process, which can help enterprises to improve the evaluation of ER. DEA is used to evaluate the technical effectiveness of decision units (DMUs) with the same type of inputs and outputs. The result suggests that the phenomenon of inefficient exchange is still widespread. We make the importance ranking of selected inducement and find that labors in China attach importance to the correctness of management process rather than the additional benefits. For the first time, the quantitative efficiency value replaces the simple description “uncertainty” in research of the exchange process. Meanwhile, the framework independently measures the input of enterprises and the output of employees which has high adaptability. It can adjust, modify and accurately evaluate the exchange efficiency of two parties according to the actual exchange situation.

scholarly journals Seasonality and Efficiency of the Hotel Industry in the Balearic Islands: Implications for Economic and Environmental Sustainability

2020 ◽  
Vol 12 (9) ◽  
pp. 3506 ◽  
Author(s):  
Francisco Javier Sáez-Fernández ◽  
Ignacio Jiménez-Hernández ◽  
María del Sol Ostos-Rey
Keyword(s):  
Data Envelopment Analysis ◽  
Environmental Sustainability ◽  
Hospitality Industry ◽  
Hotel Industry ◽  
Balearic Islands ◽  
Distance Functions ◽  
Economic Sustainability ◽  
Data Envelopment ◽  
Form Part ◽  
First Time

Tourism seasonality generates negative environmental and economic impacts. This paper analyzes the effects of seasonality on the efficiency of the hotel industry in the Balearic Islands (Spain). To that end, a sample of hotel establishments is divided into two groups (those that close down during the off-season and those that do not). Data envelopment analysis (DEA) is applied to assess the radial efficiency of each of the selected hotels; then, directional distance functions (DDFs) are used to measure the degree of efficiency with which these hotels use each of the inputs that form part of their production process. To the best of our knowledge, this is the first time that the said technique has been applied to the hospitality industry to examine the effects of seasonality. The results of this study suggest that those establishments that do not close down their operations are markedly more efficient than the ones that do. Moreover, they are more efficient in the use of each input. Therefore, a reduction in the levels of tourism seasonality would improve the economic sustainability of the hotels and reduce the environmental pressure at peak times. Finally, in line with the theoretical hypotheses formulated, the results regarding the specific efficiency levels for each input show that the greater the degree of flexibility with which these inputs are used, the higher the efficiency.

Forecasting sustainability of supply chains in the circular economy context: a dynamic network data envelopment analysis and artificial neural network approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hadi Shabanpour ◽  
Saeed Yousefi ◽  
Reza Farzipoor Saen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Envelopment Analysis ◽  
Supply Chains ◽  
Circular Economy ◽  
Real Life ◽  
Network Data ◽  
Data Envelopment ◽  
Content Type ◽  
First Time

PurposeThe objective of this research is to put forward a novel closed-loop circular economy (CE) approach to forecast the sustainability of supply chains (SCs). We provide a practical and real-world CE framework to improve and fill the current knowledge gap in evaluating sustainability of SCs. Besides, we aim to propose a real-life managerial forecasting approach to alert the decision-makers on the future unsustainability of SCs.Design/methodology/approachIt is needed to develop an integrated mathematical model to deal with the complexity of sustainability and CE criteria. To address this necessity, for the first time, network data envelopment analysis (NDEA) is incorporated into the dynamic data envelopment analysis (DEA) and artificial neural network (ANN). In general, methodologically, the paper uses a novel hybrid decision-making approach based on a combination of dynamic and network DEA and ANN models to evaluate sustainability of supply chains using environmental, social, and economic criteria based on real life data and experiences of knowledge-based companies so that the study has a good adaptation with the scope of the journal.FindingsA practical CE evaluation framework is proposed by incorporating recyclable undesirable outputs into the models and developing a new hybrid “dynamic NDEA” and “ANN” model. Using ANN, the sustainability trend of supply chains for future periods is forecasted, and the benchmarks are proposed. We deal with the undesirable recycling outputs, inputs, desirable outputs and carry-overs simultaneously.Originality/valueWe propose a novel hybrid dynamic NDEA and ANN approach for forecasting the sustainability of SCs. To do so, for the first time, we incorporate a practical CE concept into the NDEA. Applying the hybrid framework provides us a new ranking approach based on the sustainability trend of SCs, so that we can forecast unsustainable supply chains and recommend preventive solutions (benchmarks) to avoid future losses. A practicable case study is given to demonstrate the real-life applications of the proposed method.

How to use fuzzy screening system and data envelopment analysis for clustering sustainable suppliers? A case study in Iran

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Izadikhah ◽  
Reza Farzipoor Saen ◽  
Kourosh Ahmadi ◽  
Mohadeseh Shamsi
Keyword(s):  
Data Envelopment Analysis ◽  
Clustering Algorithm ◽  
Data Envelopment ◽  
Screening System ◽  
Content Type ◽  
Efficient Frontiers ◽  
Dea Models ◽  
Clustering Approach ◽  
First Time

PurposeThe aim of this paper is to classify suppliers into some clusters based on sustainability factors. However, there might be some unqualified suppliers and we should identify and remove those suppliers before clustering.Design/methodology/approachFirst, using fuzzy screening system, the authors identify and remove the unqualified suppliers. Then, the authors run their proposed clustering method. This paper proposes a data envelopment analysis (DEA) algorithm to cluster suppliers.FindingsThis paper presents a two-aspect DEA-based algorithm for clustering suppliers into clusters. The first aspect applied DEA to consider efficient frontiers and the second aspect applied DEA to consider inefficient frontiers. The authors examine their proposed clustering approach by a numerical example. The results confirmed that their method can cluster DMUs into clusters.Originality/valueThe main contributions of this paper are as follows: This paper develops a new clustering algorithm based on DEA models. This paper presents a new DEA model in inefficiency aspect. For the first time, the authors’ proposed algorithm uses fuzzy screening system and DEA to select suppliers. Our proposed method clusters suppliers of MPASR based on sustainability factors.

Data Envelopment Analysis Theory, Methodology and Applications

1997 ◽  
Vol 48 (3) ◽  
pp. 332-333 ◽  
Author(s):  
A Charnes ◽  
W Cooper ◽  
A Y Lewin ◽  
L M Seiford
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Analysis Theory
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