Search results

The aim of the present paper is to model the vendor selection process in construction projects in Greece. The applied approach involves a multicriteria rating technique used for calculating a normalized economic bid for the alternatives (i.e. vendors) by determining a proposed multiplier coefficient. This technique uses two stage compromise programming procurement criteria and weights according to the relevant legislative framework for government procurement to provide a multicriteria value score for each vendor. The application of the approach is demonstrated by an illustrative example concerning the procurement of earthmoving equipment (i.e. bulldozer). The primary advantage of this approach is that it incorporates a degree of subjectivity into the evaluation process in compliance with the existing legislative framework.

The purpose of this paper is to assess the efficiency of individual branches of a large Greek bank through the application of both goal programming (GP) and data envelopment analysis (DEA).

The paper employs a particular least absolute deviations (LAD) technique (i.e. a special case of GP/constrained regression) and DEA as two performance measurement methods. The performance evaluation by means of GP is assessed utilizing two alternative conceptual (parametric functional form‐loglinear) models: one focusing on transaction and one on production efficiency. The DEA assessment using the transaction efficiency model is performed under the specifications of constant or variable returns to scale.

The two methods do provide confirmation of each other's findings. The results support the main claim that there is a strong relationship between the rankings obtained by GP and DEA. Moreover, the GP results indicate that there is a relationship between bank branch transaction and production efficiency.

The results may be of interest to stakeholder groups such as bank shareholders, managers, and regulatory authorities.

The paper is believed to be the first to examine the application of GP and DEA to measure the efficient use of resources of bank branches in Greece in terms of location (urban‐rural).

The purpose of the research presented in this paper is to provide a new approach related to the definition of variables in disaggregating branch expenses and income for evaluating the overall technical efficiency of bank branches by means of data envelopment analysis (DEA).

By applying an input minimization DEA model to a sample of bank branches of a large commercial bank in Greece, this study identifies pure technical and scale inefficiencies, efficiency and size relationship and returns to scale patterns. Moreover, it explores target setting strategies for inefficient branches.

Results indicate that branch size has an important influence on efficiency and that superior insights can be obtained by pure technical efficiency (PTE) and scale efficiency (SE), as constituent components of global technical efficiency (TE) than the information obtained from the analysis based on selected key performance indicators (KPIs) used by the bank under study.

A direction of future research would be to extend the analysis to incorporate environmental factors, such as branch location, local competitive environment, investment portfolio risk, among others, into the DEA assessment.

The study shows that DEA which provides an overall summary measure with respect to global TE and its constituent components PTE and SE can be used to complement the in‐house performance management system of the bank under study for the evaluation of its branch network.

From a policy perspective, this study highlights the use of DEA combined with statistical analysis to support the reduction of burden by means of either expense reduction or revenue enhancement.

The purpose of this paper is to assess the performance of Greek fossil fuel‐fired power stations employing a data envelopment analysis (DEA) model combined with bootstrapping.

DEA is used to derive aggregate performance indicators using data on inputs and desirable and undesirable outputs for a sample of fossil fuel‐fired power stations. The statistical significance of the derived aggregate performance indicators is assessed via the bootstrapping approach.

The results suggest that the power stations in the sample are considerably more inefficient than revealed by the initial point estimates of inefficiency. Moreover, the non‐lignite‐fired stations of the sample are on an average more efficient than the lignite‐fired stations.

DEA represents a useful framework for exploring the current state to derive aggregate performance indicators of power stations, and moreover, the statistical properties of these metrics can be assessed via the bootstrapping approach.

The bootstrapping approach in DEA shows its superiority over DEA models that do not address the uncertainty surrounding point estimates. The DEA bootstrapping model used in this study to model environmental performance in the power station electricity production setting provides bias correction and confidence intervals for the point estimates and it is therefore more preferable.

The derivation of aggregate performance indicators of Greek fossil fuel‐fired power stations is an important addition to the existing literature on energy economics. The paper is also innovated in providing the statistical properties of the derived performance metrics.

This paper aims to assess two distinct aspects of performance in terms of technical (sales) efficiency and efficiency in market value generation of a sample of Greek metallurgical firms listed on the Athens Exchange by using data envelopment analysis (DEA).

Both aspects of performance are measured by employing the DEA BCC model, combined with bootstrap and generalized proportional distance function (GPDF). Statistical analysis is performed to investigate whether there is a positive link between the two examined performance dimensions.

Inefficiency is uncovered in both performance dimensions, but there is a lower level of performance in market value generation than in technical efficiency. Correlation analysis results do not point out positive links between performance measures for the sample firms.

The derived performance measures allow firm managers to set their own priorities and to seek out improvements along the two dimensions of performance; moreover, they may contribute to the reduction of information asymmetry among investors.

This paper is one of a few that investigate the link between DEA-based sales performance and performance in market value generation. It contributes methodologically through the adoption of fundamental analysis principles in estimating efficiency in the two performance dimensions and the development of a DEA efficiency model in the presence of negative data.

The purpose of this paper is to provide a framework for evaluating the overall performance of bank branches in terms of profitability efficiency and effectiveness.

Applying a two‐stage DEA model to a sample of bank branches of a large commercial bank in Greece this study disaggregates overall performance into profitability efficiency and effectiveness.

The results indicate that superior insights can be obtained by employing the proposed two‐stage DEA model compared to the outcomes from the analysis based on selected key performance indicators (KPIs). Some relations between profitability efficiency and effectiveness are also investigated.

The study highlights the importance of encouraging increased profitability and efficiency throughout the branch network of the commercial bank under study.

This study focuses on the banking systems evaluation in Portugal, Italy, Ireland, Greece and Spain (known as the PIIGS) during the financial and post-financial crisis period from 2009 to 2018.

A conditional robust nonparametric frontier analysis (order-m estimators) is used to measure banking efficiency combined with variables highlighting the effects of Non-Performing Loans. Next, a truncated regression is used to examine if institutional, macroeconomic, and financial variables affect bank performance differently. Unlike earlier studies, we use the Corruption Perception Index (CPI) as an institutional variable that affects banking sector efficiency.

This research shows that the PIIGS crisis affects each bank/country differently due to their various efficiency levels. Most of the study variables — CPI, government debt to GDP ratio, inflation, bank size — significantly affect banking efficiency measures.

The contribution of this article to the relevant banking literature is two-fold. First, it analyses the efficiency of the PIIGS banking system from 2009 to 2018, focusing on NPLs. Second, this is the first empirical study to use probabilistic frontier analysis (order-m estimators) to evaluate PIIGS banking systems.