Multi-period Data Envelopment Analysis Research Articles

Comparing efficiency in all-inclusive and bed and breakfast hotel businesses: a multi-period data envelopment analysis in Turkey

PurposeThis paper aims to understand the most efficient hotel system and why efficiency varies across years and between the two differing types of hotel businesses in Turkey.Design/methodology/approachA data envelopment analysis (DEA) analysis was used to characterise the efficiency of all-inclusive (AI) and bed and breakfast (B&B) hotel businesses with one output (total revenue) and three inputs (labour, food and capital costs). The Malmquist approach is then used to discern changes in total efficiency (TTE) and intertemporal shifts in the efficiency frontier (technological change (Tch)).FindingsThe results reveal that the AI hotel operates at 100% efficiency in the summer and year-round. The B&B hotel business operates at 89.6% with variable constant returns to scale during the summer and with 100% efficiency. The results of the Malmquist approach indicate that the total factor productivity grew in the years 2015, 2016, 2018 and 2019, while the other years were marked by inefficiency. Such increases were due to technical efficiency change (TEch) and Tch, which means that managerial and allocative efficiency (AE) were barely achieved. Slight differences were noted in the two time periods (all year and summer), suggesting that the scale of hotel businesses is prepared to operate all year round, and this calls for strategies to mitigate seasonality.Research limitations/implicationsAs to avenues for future research, the limitations of this study are threefold. First, the hotel businesses are not parallel in terms of the duration of their service offerings. Future research may consider including an AI hotel business that is in operation for the whole year. Second, businesses in Turkey are sceptical about sharing their data as it is considered confidential. However, to better generalise the results and encourage hoteliers to consider the positive outcomes of such analysis, the number of observations could be increased by considering more hotel businesses in both categories. Third, a mixture of data representing businesses operating in various countries may reflect if the efficiency scores vary internationally.Practical implicationsOverall, AI hotel businesses are more attractive but less efficient than B&B. Furthermore, the external crisis impacts the efficiency of hotel businesses meaning that hotel managers could keep on exploring AI, perhaps educating their hosts not to waste or not offer huge quantities. Hotel managers may also need to enlarge their seasonal activities to ensure more efficiency.Social implicationsDespite the intentions of AI hotel businesses to increase their profitability with a lower level of service quality, this study shows that the AI hotel business is very attractive but not so efficient due to the higher propensity of guests to consume food and beverages in excess that compromises the definition of efficiency as zero waste. AI is very attractive for family groups or those seeking the pleasure of relaxation at seaside resorts and is also very popular in Turkey. On the other hand, the B&B hotel business is more efficient but less attractive.Originality/valueThe contributions of this paper are threefold. First, the authors analysed the efficiency and inefficiency of hotel businesses within nine years of operations. During this period, Turkey experienced first a tourism boom (2011–2014) followed by stagnation and subsequently a sharp decline due to political instability resulting in an (in)direct impact on tourism (2015–2019). Second, the authors compared the efficiency and inefficiency of AI and B&B hotel businesses. Third, the authors examined the effects of hotel management factors to ensure efficiency.

Global multi-period performance evaluation – New model and productivity index

In this paper, we introduce a novel multi-period data envelopment analysis (MDEA) model that attempts to circumvent the limitations of the existing MDEA models. The proposed global MDEA model is essentially based on major modifications of fundamental DEA axioms to enable a decision making unit (DMU), defined with inputs and outputs of period t, to be evaluated within the production possibility set (PPS) of another period l, t ≠ l. Building on the properties of the global MDEA model, we also introduce a global productivity index, identified as Global Progress and Regress index (GPRI), that render possible the evaluation of a DMU’s extent of progress or regress over multi-period time horizons under variable returns to scale (VRS) production technologies. This lifts the restrictions to two successive periods and constant returns to scale (CRS) of existing productivity indices. The most salient features of the new MDEA model as well as the GPRI are highlighted using an application that involves a real-life sample of 25 bank branches considered over 4 years.

Analysis on the Efficiency Change in Electric Vehicle Charging Stations Using Multi-Period Data Envelopment Analysis

It is highly challenging to measure the efficiency of electric vehicle charging stations (EVCSs) because factors affecting operational characteristics of EVCSs are time-varying in practice. For the efficiency measurement, environmental factors around the EVCSs can be considered because such factors affect charging behaviors of electric vehicle drivers, resulting in variations of accessibility and attractiveness for the EVCSs. Considering dynamics of the factors, this paper examines the technical efficiency of 622 electric vehicle charging stations in Seoul using data envelopment analysis (DEA). The DEA is formulated as a multi-period output-oriented constant return to scale model. Five inputs including floating population, number of nearby EVCSs, average distance of nearby EVCSs, traffic volume and traffic congestion are considered and the charging frequency of EVCSs is used as the output. The result of efficiency measurement shows that not many EVCSs has most of charging demand at certain periods of time, while the others are facing with anemic charging demand. Tobit regression analyses show that the traffic congestion negatively affects the efficiency of EVCSs, while the traffic volume and the number of nearby EVCSs are positive factors improving the efficiency around EVCSs. We draw some notable characteristics of efficient EVCSs by comparing means of the inputs related to the groups classified by K-means clustering algorithm. This analysis presents that efficient EVCSs can be generally characterized with the high number of nearby EVCSs and low level of the traffic congestion.

Auditing the efficiency of the nation-funded social science research with data envelopment analysis

The efficiency of the nation-funded social science research has become one of the main interests in university administration and performance auditing organization, as a large amount of money has been devoted to supporting the development of social science research in the form of project funding. This paper aims to address the efficiency evaluation difficulties in a performance auditing by means of multi-inputs and multi-outputs, together with an improved multi-period data envelopment analysis model with feedback effect based on the facts that parts of the outputs of the previous year act as important inputs of the current year. Then with the help of this performance auditing method, the efficiencies and their changes of the nation-funded social science research implemented by universities in Jiangsu Province for the period 2011–2015 are examined via a two-stage production process that separates development and innovation. The application conclusion can shed light on the respective operation to detect the lower efficiency stages or years for each university.

Multi-period efficiency measurement of network production systems

Multi-period data envelopment analysis, DEA models have been introduced recently for measuring aggregative efficiency of multiple time period production systems. This paper extends multi-period DEA models by considering more complex internal relations for the sub-processes of each decision making unit, DMU. This work presents novel multi-period network DEA models that are developed for performance evaluation of overall and specific time period efficiencies with parallel and series internal structures in the sub-processes for each time period. Two examples are given to illustrate the proposed approach. Additionally, another illustrative example is employed to conduct an empirical study of the proposed model to evaluate the performance of ten branches of a university college system in Iran.

A novel common set of weights method for multi-period efficiency measurement using mean-variance criteria

Data envelopment analysis (DEA) is a popular method for evaluating a set of homogeneous decision-making units (DMUs). One of the main shortcomings of DEA is the weights flexibility where each unit can take its desirable weights. Several methods have been developed for finding a common set of weights (CSWs) and overcoming this drawback. The CSWs methods are used to evaluate the relative efficiency of the DMUs in a single time-period. However, single period DEA models cannot handle organizational units performing in a continuum of time. We propose a novel method for determining the CSWs in a multi-period DEA. Initially, the CSWs problem is formulated as a multi-objective fractional programming problem. Subsequently, a multi-period form of the problem is formulated and the mean efficiency of the DMUs is maximized while their efficiency variances is minimized. A fuzzy set-based approach is used to solve the multi-period CSWs problem. We present a real-world case study to demonstrate applicability and exhibit the efficacy of the proposed method. The results indicate a significant improvement in the discrimination power of the proposed multi-period method.

Multi-period data envelopment analysis models and resource allocation: A case study

Data envelopment analysis is a general tool for efficiency and performance measurement of the set of decision making units based on solving linear optimization problems. The paper deals with using this group of models for resource allocation analysis among Czech economic faculties within 7 years period from 2008 until 2014. The resources in the period t are divided among faculties according to their teaching and research performance in the period (t - 1). The teaching performance is measured by the number of students and number of graduated in the given period. The research performance is defined according to the national rules by the number of so called RIV points. The overall performance level in the given period is estimated using data envelopment analysis model with constant returns to scale assumption. The input in the period t is partly based on the efficiency or super-efficiency scores from the preceding period. Super-efficiency score given by Andersen and Petersen model is used for discrimination among efficient units. The results of the allocation in the last period are compared to the ones calculated to the standard methodology. Numerical experiments are realized using own MS Excel add-ins and LINGO modelling and optimization system.

Multi-period data envelopment analysis based on Chebyshev inequality bounds

Data envelopment analysis is a cross-sectional approach to evaluate the relative efficiency of a set of homogeneous units in a single time point; nonetheless, organizational units have been performing continuously over a period of time; hence, their performances are considered within this period. Cumulating inputs and outputs over the time periods provide an unnecessary compensating impact, making the efficiency appraisal unrealistic. To avoid this negative impact of data accumulation, a two-stage approach on the basis of Chebyshev inequality bounds is proposed to find interval efficiency of decision making units (henceforth DMUs). The proposed method is applied in a real case encompassing 115 bank branches over 6 periods of time. This application indicated the significant cautious approach of the proposed method in multi-period data envelopment analysis (hereafter DEA).

Multi-period efficiency analysis in data envelopment analysis

The problem of multi-period data envelopment analysis (DEA) has been studied. We consider the performance of a production unit in the course of T periods. The proposed DEA models not only measure the efficiency across all periods, but also they provide the efficiency measures for each of the periods. It is proven that the aggregate efficiency is a convex combination of the efficiency of each period. The applicability of the models developed is illustrated in the context of the analysis of gas companies performance.

Determinants of internal audit efficiency

This paper describes a case study in which Data Envelopment Analysis (DEA) methodology was combined with regression analysis to evaluate the efficiency of an Internal Audit (IA) department over twelve consecutive months. Efficiency of audit projects was first estimated using DEA. These results were then used as one of the outputs to perform a multi-period DEA study with a choice of other inputs and outputs specific to the Internal Audit department under review. The efficiency of audit projects is viewed as one of the key outputs of an IA department and an explanation of these efficiencies would therefore be useful (necessary) to enhance insights gained from the DEA model applied to the twelve months. To assist in this explanation a multiple regression model was employed in which the efficiency score obtained from the DEA computations for the audit projects was used as the dependent variable. Following a description of the models and data, the results are discussed and notes are made of certain aspects pertaining to the department reviewed.

A Data Envelopment Analysis of the Operational Efficiency of Bank Branches

We performed a multiperiod data envelopment analysis (DEA) study of the efficiencies of selected branches of a large US bank (which we will call Big Bank) over six consecutive quarters (second quarter of 1992 to the third quarter of 1993). We developed a number of innovative application tools, including budgeting and target-setting modules, within a DEA framework that we subsequently customized for Big Bank. Within the performance-evaluation framework, we developed the ability to identify different potential groupings of branches that supported the multiple user views of the system. We also developed procedures to evaluate trends over time and differences in performance across user-defined aggregations of branches. We paid attention to the interface with the end users and, in particular, developed presentation tools to make the outcomes of the analysis available to managers at different levels of the bank.