Average revenue efficiency and optimal scale sizes in stochastic data envelopment analysis: A case study of post offices

Estimating the revenue efficiency of entities being evaluated is crucial as it provides valuable information about organizations, assuming that the output prices are known. This research introduces a new definition of optimal scale size (OSS) based on maximizing the average revenue efficiency (ARE). Additionally, the ARE is defined for both convex and non-convex sets, independent of returns to scale and the assumption that the vector of input-output prices of units is uniform. Moreover, to address the presence of uncertain data in real-world applications, the introduced ARE model is extended to evaluate systems with random inputs and outputs, along with approaches for its calculation. Finally, the proposed method is applied in an experimental example, calculating the ARE for a dataset of postal areas in Iran.IntroductionThe concept of optimal scale size has been extensively studied in the field of data envelopment analysis. Cesaroni and Giovannola's research on non-convex FDH technology reveals that the optimal scale size is a point in the production possibility set that minimizes average cost efficiency. Average cost efficiency, a new measure combining scale and allocation efficiencies, provides a more accurate performance assessment compared to cost and scale efficiencies. When evaluating units with known output prices instead of input prices, assessing revenue efficiency can offer more valuable insights. This paper extends the research on cost evaluation to revenue evaluation. It introduces the concepts of average revenue efficiency and optimal scale size based on revenue maximization. The optimal scale size based on revenue maximization is defined as the point in the production possibility set that maximizes the average radial income for the unit under investigation. Average revenue efficiency serves as an evaluation measure of unit revenue, surpassing revenue and scale efficiencies in accuracy. The paper examines methods for calculating average revenue efficiency in both convex and non-convex technologies. It demonstrates that the average revenue efficiency model in convex technology with variable returns to scale is equivalent to the revenue model with constant returns to scale. Furthermore, the relationship between optimal scale size points based on revenue maximization and the most productive scale size is determined. Next, the paper presents the average revenue efficiency model for stochastic sets with the presence of stochastic data. An experimental example is used to calculate the average revenue efficiency and obtain the optimal scale size for a set of postal areas in Iran.Materials and MethodsThe study builds upon Cesaroni and Giovannola's method for calculating average cost efficiency and optimal scale size to develop models for average revenue efficiency and optimal scale size based on revenue. It also utilizes chance-constrained probabilistic models with a deterministic objective function in DEA literature to present average revenue efficiency for stochastic sets. The model is transformed from stochastic to deterministic and then converted into a linear model using the error structure method.Discussion and ResultsThis paper introduces average revenue efficiency and optimal revenue scale size, demonstrating the equivalence between the average revenue efficiency models in convex technology with variable returns to scale and those with constant returns to scale. It also presents the average revenue efficiency model for stochastic sets, enabling the calculation of average revenue efficiency and optimal revenue scale size for units with random inputs and outputs.ConclusionIn many real-world scenarios, particularly when output prices are known, evaluating revenue efficiency holds greater significance than cost efficiency. This study develops the concepts of average cost efficiency and optimal scale size for revenue evaluation, expanding upon the existing literature on data envelopment analysis. The paper demonstrates how average revenue efficiency can be calculated as a valuable and accurate measure of efficiency in convex and non-convex technologies, without making assumptions about returns to scale. By assuming the randomness of input and output variables and employing chance-constrained models, a quadratic deterministic model is presented to calculate average revenue efficiency. It is then transformed into a linear model assuming uncorrelated variables, enabling the determination of average revenue efficiency and optimal scale size based on revenue maximization for random units. The proposed models are applied to a real-world sample, evaluating the average revenue efficiency of twelve postal units. The results highlight the models' ability to provide a more accurate evaluation of revenue efficiency and identify the best revenue scale size as the reference for inefficient units

A novel hybrid fuzzy DEA-Fuzzy MADM method for airlines safety evaluation

Safety is a critical element in the air transport industry. Although fatal air accidents are rare compared to other transport industries, the rapid growth in air travel demands has resulted in a growing aviation risk exposure and new challenges in the aviation sector. Although the issue of airline safety is of serious public concern, notably few studies have investigated the safety efficiency of airlines. This paper aims to propose a novel hybrid method using fuzzy data envelopment analysis (DEA) and fuzzy multi-attribute decision making (F-MADM) for ranking the airlines’ safety. In this study, fuzzy DEA is utilized to calculate criteria weights, in contrast to the conventional approach of using DEA for measuring the efficiency of alternatives. A ranking of each airline (DMU) on the basis of obtained weights is then assessed using MADM methods. Six MADM methods including Fuzzy SAW, Fuzzy TOPSIS, Fuzzy VIKOR, ARAS-F, COPRAS-F and Fuzzy MULTIMOORA are implemented to rank the alternatives, and finally, the results are compounded with the utility interval technique. This new hybrid method can efficiently overcome the pitfalls of traditional hybrid DEA-MADM models. The method proposed in this study is used to evaluate the safety levels of seven Iranian airlines and to select the safest one. © 2018 Elsevier Lt

The Relationship between Nonprofit Organizations and Cloud Adoption Concerns

Many leaders of nonprofit organizations (NPOs) in the United States do not have plans to adopt cloud computing. However, the factors accounting for their decisions is not known. This correlational study used the extended unified theory of acceptance and use of technology (UTAUT2) to examine whether performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit can predict behavioral intention (BI) and use behavior (UB) of NPO information technology (IT) managers towards adopting cloud computing within the Phoenix metropolitan area of Arizona of the U.S. An existing UTAUT2 survey instrument was used with a sample of IT managers (N = 106) from NPOs. A multiple regression analysis confirmed a positive statistically significant relationship between predictors and the dependent variables of BI and UB. The first model significantly predicted BI, F (7,99) =54.239, p -?¤ .001, R^2=.795. Performance expectancy (β = .295, p = .004), social influence (β = .148, p = .033), facilitating conditions (β = .246, p = .007), and habit (β = .245, p = .002) were statistically significant predictors of BI at the .05 level. The second model significantly predicted UB, F (3,103) = 37.845, p -?¤ .001, R^2 = .527. Habit (β = .430, p = .001) was a statistically significant predictor for UB at a .05 level. Using the study results, NPO IT managers may be able to develop strategies to improve the adoption of cloud computing within their organization. The implication for positive social change is that, by using the study results, NPO leaders may be able to improve their IT infrastructure and services for those in need, while also reducing their organization\u27s carbon footprint through use of shared data centers for processing