Input and output reconsidered in supplier selection DEA model

Dobos, Imre; Vörösmarty, Gyöngyi

doi:10.1007/s10100-023-00845-5

Cited by 5 publications

(2 citation statements)

References 40 publications

(43 reference statements)

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“…Local search procedures improve the quality of the solutions. Dobos and Vörösmarty (2024) focus on data transformation in several models of Data Envelopment Analysis (DEA, (see, e.g. in Hosseinzadeh et al (2023); Wu et al (2023); Zýková (2022))) and draw the attention to the possibility of biases.…”

Section: The 34th Hungarian Operations Research Conferencementioning

confidence: 99%

Optimization methods and algorithms

Biró,

Bozóki,

Király

et al. 2024

Cent Eur J Oper Res

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Recent results of three areas, pickup and delivery, optimal mass transportation, matching under preferences are highlighted. The topics themselves have been selected from the active research fields of Hungarian Operations Research. We also provide a short summary of selected research results from the 34th Hungarian Operations Research Conference, held in Cegléd, Hungary, August 31–September 2, 2021.

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Section: The 34th Hungarian Operations Research Conferencementioning

confidence: 99%

Optimization methods and algorithms

Biró,

Bozóki,

Király

et al. 2024

Cent Eur J Oper Res

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“…However, Denmark and Luxembourg were eliminated, which was attributed to insufficient statistical data. DEA is a numerical technique utilised to assess the relative efficiency of units known as decision-making units (DMUs) (Dobos & Vörösmarty, 2023). Within the framework of DEA, it is crucial to define the inputs and outputs pertinent to the assessment.…”

Section: Standard Dea Modelmentioning

confidence: 99%

Do Cultural Activities and Happiness Matter to the Tourism Sector?

Astike,

Borges,

Skvarciany

et al. 2024

Economics and Culture

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Research purpose. The primary goal of this research is to determine the impact of the efficiency of the tourism sector in the European Union (EU). Design / Methodology / Approach. The analysis involved 25 selected EU countries using Data Envelopment Analysis (DEA) and the Tobit regression modelling. For DEA expenses according to the stays per visit (1 night or over) when going to a foreign country, individuals actively working in the tourism industry and tangible heritage inscribed on the United Nations Educational, Scientific and Cultural Organisation (UNESCO) are used as inputs and inbound tourism as an output. Variables for the Tobit model were as follows: cultural activities, Happiness index and tourist expenditure of Gross Domestic Product (GDP). Findings. In nations characterised by lower efficiency levels, policymakers ought to enhance their strategies and encourage the advancement of their country's appeal to tourists. Additionally, there should be an increased emphasis on enhancing the well-being and happiness of the local population. Originality / Value / Practical implications. Theoretical research on the efficiency of the tourism sector in the EU lacks comprehensive studies that systematically examine the factors influencing efficiency across diverse EU member states. Although some research exists on specific aspects of tourism efficiency, such as environmental sustainability or economic impacts, there is a scarcity of holistic investigations that encompass various dimensions of efficiency, including resource utilization, infrastructure development, policy effectiveness, and socio-cultural impacts, within the EU's overarching context. Moreover, there is a limited exploration of potential variations in tourism efficiency among different EU regions and the underlying factors driving these differences. Bridging this research gap could offer valuable insights for policymakers, industry stakeholders, and academics seeking to enhance the overall efficiency and sustainability of the tourism sector in the EU. The study’s constraint lies in the exclusion of Denmark and Luxembourg, which is attributed to insufficient statistical data.

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Measuring digital development: ranking using data envelopment analysis (DEA) and network readiness index (NRI)

Bánhidi,

Dobos

2024

Cent Eur J Oper Res

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The Network Readiness Index (NRI) is one of the most prominent indicators that shows the digital development of countries. In contrast to the International Digital Economy and Social Index (I-DESI) of the European Union (EU), in 2020, it showed the development of 134 countries compared to 45 countries in I-DESI of EU, which measures only the most developed countries. The aim of this paper is to provide a viable alternative framework to the equal weights scheme of the original NRI scoring model using the Data Envelopment Analysis (DEA) Without Explicit Input (WEI) method and Common Weight Analysis (CWA) method. After determining the common weights, we compare the digital development of the countries in the NRI dataset based on the results obtained, focusing on the countries of the Central and Eastern European (CEE) region and the former Soviet Union.

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Input and output reconsidered in supplier selection DEA model

Cited by 5 publications

References 40 publications

Optimization methods and algorithms

Optimization methods and algorithms

Do Cultural Activities and Happiness Matter to the Tourism Sector?

Measuring digital development: ranking using data envelopment analysis (DEA) and network readiness index (NRI)

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