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.

scholarly journals Review of Life-Cycle Approaches Coupled with Data Envelopment Analysis: Launching the CFP + DEA Method for Energy Policy Making

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Ian Vázquez-Rowe ◽  
Diego Iribarren
Keyword(s):  
Life Cycle ◽  
Data Envelopment Analysis ◽  
Energy Policy ◽  
Environmental Impacts ◽  
Environmental Sustainability ◽  
Policy Making ◽  
Energy Systems ◽  
Data Envelopment ◽  
Dea Method ◽  
Operating Points

Life-cycle (LC) approaches play a significant role in energy policy making to determine the environmental impacts associated with the choice of energy source. Data envelopment analysis (DEA) can be combined with LC approaches to provide quantitative benchmarks that orientate the performance of energy systems towards environmental sustainability, with different implications depending on the selected LC + DEA method. The present paper examines currently available LC + DEA methods and develops a novel method combining carbon footprinting (CFP) and DEA. Thus, the CFP + DEA method is proposed, a five-step structure including data collection for multiple homogenous entities, calculation of target operating points, evaluation of current and target carbon footprints, and result interpretation. As the current context for energy policy implies an anthropocentric perspective with focus on the global warming impact of energy systems, the CFP + DEA method is foreseen to be the most consistent LC + DEA approach to provide benchmarks for energy policy making. The fact that this method relies on the definition of operating points with optimised resource intensity helps to moderate the concerns about the omission of other environmental impacts. Moreover, the CFP + DEA method benefits from CFP specifications in terms of flexibility, understanding, and reporting.

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.

Benchmarking of Airbnb listings: How competitive is the sharing economy sector of European cities?

2018 ◽  
Vol 25 (7) ◽  
pp. 1029-1046 ◽  
Author(s):  
Bozana Zekan ◽  
Irem Önder ◽  
Ulrich Gunter
Keyword(s):  
Data Envelopment Analysis ◽  
Hospitality Industry ◽  
Sharing Economy ◽  
Data Envelopment ◽  
Demand And Supply ◽  
On Demand ◽  
European Cities ◽  
Tourism And Hospitality Industry ◽  
Tourism And Hospitality

Airbnb is arguably the world’s most popular accommodation sharing platform. Its impact on demand and supply within the tourism and hospitality industry is nowadays unquestionable. The present study delves into inspecting the efficiency of Airbnb listings of European cities, as, in spite of the success of Airbnb as a whole, it cannot be presupposed that all listings are equally successful. More specifically, data envelopment analysis (DEA) is employed in this first comprehensive benchmarking attempt within the domain of the sharing economy to date. This article also makes a contribution to robustness by introducing an interactivity note to the base model, thus, inspecting the results for corroboration/discrepancies and going beyond the static analyses that are common in DEA modeling. Ultimately, this is done with the goal of highlighting opportunities for inefficient Airbnb listings to properly utilize their inputs and therefore become more competitive.

scholarly journals Data Envelopment Analysis (DEA) for Measuring the Efficiency of the Hotel Industry in Ecuador

2020 ◽  
Vol 12 (4) ◽  
pp. 1590 ◽  
Author(s):  
Angel Higuerey ◽  
Christian Viñan-Merecí ◽  
Zulema Malo-Montoya ◽  
Valentín-Alejandro Martínez-Fernández
Keyword(s):  
Data Envelopment Analysis ◽  
Panel Data ◽  
Hotel Industry ◽  
Geographic Location ◽  
Data Envelopment ◽  
Market Position ◽  
Tourist Attractions ◽  
The Third ◽  
Factors Of Production ◽  
Efficiency And Productivity

The level of contribution of the hotel industry depends on different factors of production that they use in the provision of their services The way they use these factors of production will allow them to act efficiently, in order to improve profitability and market position. Ecuador, in recent years, has directed public policies betting on the development of this industry. In this sense, this research seeks to measure the efficiency and productivity of the Ecuadorian hotel industry. For this purpose, a significant sample has been selected; it consists of 147 businesses that provided hotel services during the period 2013–2017. These businesses are classified according to their quality and geographic location. This information has been useful to make a balanced panel data with one output (Revenue) and three inputs (Total_personnel, the non-current assets, and Consumption) by using the Data Envelopment Analysis (DEA). The results, which proved to be solid and accurate, indicate that the most efficient businesses are the ones in the third class, whereas those hotels located in zones with tourist attractions and activities have a better optimization of those resources. This situation has an effect on the significant improvement of their productivity.

A measure of sustainability of Brazilian agribusiness using directional distance functions and data envelopment analysis

2014 ◽  
Vol 21 (3) ◽  
pp. 210-222 ◽  
Author(s):  
Carlos Rosano-Peña ◽  
Patricia Guarnieri ◽  
Vinicius Amorim Sobreiro ◽  
André Luiz Marques Serrano ◽  
Herbert Kimura
Keyword(s):  
Data Envelopment Analysis ◽  
Distance Functions ◽  
Data Envelopment ◽  
Directional Distance Functions

scholarly journals Evaluation of Efficiency and Its Determinants in Croatian Hotel Industry

2021 ◽  
Vol 9 ◽  
pp. 108-115
Author(s):  
Maja Pervan ◽  
Petra Babic
Keyword(s):  
Regression Analysis ◽  
Hotel Industry ◽  
Medium Size ◽  
Data Envelopment ◽  
Truncated Regression ◽  
Independent Variables ◽  
Second Stage ◽  
Using Data ◽  
Stage Analysis ◽  
First Time

The main objective of this research was to evaluate the efficiency of Croatian hotels and provide insights into the sources of their efficiency. In order to obtain set goals, a two-stage analysis was performed on a sample of 69 large and medium-size hotels that were operating in Croatia in 2019. In the first stage of analysis, the efficiency scores of hotels were obtained by using Data envelopment analysis (DEA), whereas in the second stage of analysis, achieved efficiency coefficients were served as dependent variable in a truncated regression model in which hotel’s ownership, age, location, size and star rating were applied as independent variables. As this is the first time that efficiency of Croatian hotel industry is investigated with the application of truncated regression analysis, this research contributes to the existing literature by shading new lights on the sources of hotels’ efficiency from the perspective of a country heavily relaying on seasonal seaside tourism. Results of the research showed that all analysed variables (except age) play significant and important role in determining the achieved level of efficiency.

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