Search results

In India, the operational performance of the refinery is influenced by many factors. It is important to identify those key drivers which can assist the refineries to uphold and succeed in day-to-day production activities. Therefore, the purpose of this study is to evaluate the operational efficiency of seven Indian oil refineries during the period 2010 to 2018.

In this work, a two-stage empirical analysis is proposed. In the first stage, the data envelopment analysis (DEA) – variable return to scale model is used to evaluate the operational efficiency of the Indian oil refineries. The ordinary least square (OLS), random effect generalized least square (GLS) and Tobit model are used in the second stage to identify the key determinants of efficiency and to explain the variation in refinery efficiency.

The first-stage DEA results showed that the Numaligarh Refinery Limited and Chennai Petroleum Corporation Limited are found to be more efficient than the rest of the sampled refineries and attained their efficiency scores of 0.993 and 0.981, respectively, during the study period. The second-stage regression analysis suggested three explanatory variables: refinery structure, utilization rate and distillate yield, which are found to be significant in explaining variations in refinery efficiency.

This study provides valuable information that would help policymakers to formulate policies toward improving the efficiency of underperforming Indian refineries, which reduces the excessive use of resources and gives a competitive advantage.

This study proposes the first-ever application of the profit frontier DEA model for assessing the operational efficiency of oil refineries and explains the variation in refinery’s efficiency using OLS, GLS as well as the Tobit model.

Firms are working in a complex environment in which the updated information increase the pace of precise decision making and reduce the risk of wrong decisions. Therefore, discovering firms’ performance is a major issue. The purpose of this paper is to evaluate the efficiency of Iranian plastic producing companies by using data envelopment analysis (DEA). It also discovers various drivers that significantly affect the efficiency of enterprises.

The authors studied a sample of 17 manufacturing firms to examine the relative efficiency of companies. They, then, evaluated the effects of efficiency drivers and used two methods for these purposes: DEA and bootstrapped Tobit regression model.

The study has shown that two manufacturing firms out of selected 17 are efficient under the Charnes, Cooper, and Rhodes model. Also, nine out of 17 plastic producing companies are productive under the Banker, Charnes, and Cooper model. The results of Tobit regression shows that only two efficiency drivers out of four have a significant positive influence on the efficiency of plastic producing firms.

Considering one industry and country limits the generalizability of the results provided. Besides, data availability has limited the analysis in some parts, particularly in bootstrapped Tobit regression.

The authors listed this section into benchmarking and strategical management; more importantly, the suggestions for improving the chemical industry and its future evolution are presented.

The paper is classified into two issues: the efficiency of plastic producing firms in Iran and evaluating the reason for inefficiency, apart from internal managerial procedures.

The scope of this research is to investigate performing loan efficiency for fifty European banks during the period 2008–2017.

The study is structured as a two-stage analysis of performing loan efficiency and its driving factors. In the first stage of the proposed methodology “Data Envelopment Analysis” is used to estimate performing loan efficiency for each bank included in the sample. A bootstrap statistical procedure enhances the findings. In the second stage, the impact of other factors on the efficiency scores of loan performance using tobit regression is investigated.

The results are consistent with the findings of the individual banks' financial analyses. According to the findings of DEA implementation, the evaluated banks may enhance their cost efficiency by 39% on average. In addition, the results indicate that loan efficiency performance improves after 2015, coinciding with the business cycle's upward trend. The tobit regression is employed in the second stage to examine the influence of bank-related and macroeconomic factors on banks' loan management efficiency. According to the findings of the tobit regression, three factors, namely the capital adequacy ratio, GDP per capita and managerial inefficiency, have a substantial influence on performing loan efficiency.

This research investigates the effectiveness of European economic policy in protecting the European banking system from the consequences of the sovereign debt crisis in several euro area members. The results highlight the distance of the Eurozone from the level of the ‘optimal currency area’.

The purpose of this study is to benchmark the performance of state-owned coal-fired power plants (CFPPs) and test whether plant-specific knowledge in terms of quality of coal, size, age and make of plant contribute to an improvement in plant efficiency.

The methodology that is utilized in the study follows a nonparametric approach of data envelopment analysis (DEA) with sensitivity analysis and Tobit regression model. The input-oriented DEA models are applied to evaluate the overall, pure technical and scale efficiencies of the CFPPs. Further, slack analysis is conducted to identify modes to improve the efficiency of the inefficient plants. Sensitivity analysis based on peer count and the removal of variables is carried out to identify the benchmark power plant. Through Tobit and bootstrap-truncated regression model, the paper investigates whether a plant's specific knowledge influences its efficiency.

The DEA analysis demonstrates that nine plants are technically purely efficient.The slack analysis reveals that reducing the consumption of oil is the most effective way to improve the efficiency of inefficient plants. Mattur plant is the benchmark for most of the inefficient plants. Regression result suggests that quality of coal and size of plant significantly affect the inefficiency of the sample plants. Bharat Heavy Electrical Limited MAKE plant achieved higher efficiency in comparison to mixed MAKE.

This study is one of the few published studies that benchmark the performance of state-owned CFPPs. This research carried out taking some new uncontrollable parameters of power plant utilities of India. Research work also identifies the possible causes of inefficiency and provides measures to improve the efficiency of the inefficient power plant.

The purpose of this paper is to analyze the performance of Indian retailers in recent past and derive meaningful insight for practicing managers in this area.

This paper analyses the economic efficiencies of select Indian retailers using three related methodologies: Data Envelopment Analysis (DEA), Malmquist Productivity Index (MPI) and Bootstrapped Tobit Regression.

DEA analysis has shown that five retail firms out of selected 18 are found as efficient under the CCR model of DEA and seven out of 18 retail firms are efficient under the BCC model of DEA. MPI results indicate that 61 percent of the firms have progressed in terms of the MPI during the period under consideration. The Bootstrapped Tobit Regression shows that number of retail outlets and mergers and acquisitions can be considered as the driving forces influencing efficiency of retailers in India.

The paper has a limitation with reference to the availability of data for a few retail outlets, especially in the modeling through the Bootstrapped Tobit Regression.

This study seems to be the first in applying productivity analysis using DEA, MPI and Bootstrapped Tobit Regression for the Indian retail sector.

Effectively evaluating the efficiency of individual e-markets for better understanding the efficiency-oriented critical drivers for individual e-markets is of great significance to the development of electronic business. The purpose of this paper is to develop an approach through adequately integrating data envelopment analysis (DEA) and bootstrapped Tobit regression analysis for identifying the efficiency-oriented critical drivers on the development of e-market in electronic business.

A review of the related literature is conducted for adequately formulating the e-market evaluation problem. DEA is appropriately used for assessing the efficiency of available e-markets, leading to the identification of the efficient e-market. Tobit regression analysis is then employed to examine the outcome of the DEA analysis for identifying the efficiency-oriented critical drivers in the development of e-markets in electronic business.

A better understanding of the operations of individual e-markets with respect to their overall efficiency in electronic business can be achieved with the use of the developed approach. Such understanding is built on the identification of the efficiency-oriented critical drivers on the development of e-market in electronic business.

This paper develops a novel approach for better understanding of the operations of individual e-markets with respect to their overall efficiency in electronic business. The adoption of this approach helps existing e-markets improve their efficiency by focussing on the efficiency-oriented critical drivers and provide new players in e-markets with guidelines for developing their efficient e-markets.

Comparative shop efficiency within a chain is a key factor in strategic management decisions such as evaluation, promotion and development of shop personnel. Furthermore, overall efficiency of the firm depends on the efficiency of individual shops within the chain. The purpose of this paper is to assess operational efficiency of a bookshop chain in Turkey, and identify efficiency drivers.

The sample includes 79 bookshops within a bookshop chain. The study uses two‐step procedure. In the first stage, data envelopment analysis (DEA) is utilised to evaluate the comparative efficiency of bookshops. The second stage attempts to determine what drives efficiency by using Tobit regression.

After assessment of shops’ efficiency by DEA, the results of Tobit regression revealed that shop age has positive significant influence on bookshop efficiency, whereas manager experience, staff experience, and education level of the shop manager do not.

The findings of the paper are based on a single bookshop chain. Thus, one should be cautious while interpreting results.

The contribution of the paper to the literature is of great importance, since no prior Turkish study has dealt with the subject to this extent. Furthermore, although there are studies conducted on various subsectors of retail industry in other countries, there seems to be no study at all conducted on bookshop chains.

The purpose of this paper is not only to gauge the extent of technical efficiency in 31 state road transport undertakings (SRTUs) operating in India but also to explore the most influential factors explaining its variations across SRTUs.

Three popular data envelopment analysis (DEA) models, namely CCR, BCC and Andersen and Petersen's super‐efficiency models, have been utilized to compute various efficiency scores for individual SRTUs. A censored Tobit analysis is conducted to see which factors significantly explain the inter‐SRTU variations in efficiency.

The key findings of the DEA analysis are only five SRTUs define the efficient frontier, and the remaining 26 inefficient undertakings have a scope of inputs reduction, albeit by the different magnitude; the extent of average overall technical inefficiency (OTIE) in these SRTUs is to the tune of 22.8 percent, indicating that the sample SRTUs are wasting about one‐fourth of their resources in the production operations; managerial inefficiency (as captured by the pure technical inefficiency) is a relatively more dominant source of OTIE; and operation in the zone of increasing returns‐to‐scale is a common feature for most of the undertakings. The multivariate regression analysis using Tobit analysis highlights that the occupancy ratio is the most significant determinant for all the efficiency measures, and bears a positive relationship with overall technical, pure technical and scale efficiencies. Further, scale efficiency is also impacted positively by the staff productivity.

The results of this paper can be applied from management's perspective. The managers can assess the relative efficiency of their SRTUs in the industry and take corrective measures to improve efficiency by altering input‐output mix.

This paper provides more robust estimates of relative efficiency of the SRTUs and highlights the key determinants of overall technical efficiency.

The main purpose of this paper is to examine cost efficiencies of the selected Islamic and conventional commercial banks over the period of 2006 to 2009 in Malaysia.

Data envelopment analysis (DEA) was initially used, to investigate the cost efficiency of the Malaysian banking sector and followed by Tobit regression analysis determine factors influencing the efficiency of Islamic and conventional banks in Malaysia.

The DEA results reveal technical efficiency as the main contributor of cost efficiency for conventional commercial banks and allocative efficiency as the main contributor for cost efficiency of Islamic commercial banks. This indicates conventional commercial banks have been efficient in utilizing information technology and electronics. Islamic commercial banks conversely have been efficient in allocating and utilizing their resources. Additionally, scale efficiency is found to be the main source of technical efficiency for both Islamic and conventional commercial banks, denoting that size is important in improving bank efficiency. The results of Tobit regression analysis are twofold. First, it documents capitalization and bank sizes are positively and significantly associated to efficiency. Secondly, loan quality is found to be negatively and significantly associated to efficiency.

This paper contributes to the body of knowledge through its literature discussions on the efficiency of both Islamic and conventional banks and the effect of banks' specific characteristics on their efficiency.

The purpose of this research is to explain the financing dilemma of China's strategic emerging industries and improve their financing efficiency, seize the commanding heights of economic science and technology to provide theoretical support.

This paper selects the companies listed under strategic emerging industry during the period of 2010–2017 as the research object and used the data envelopment analysis method (DEA) to evaluate the financing efficiency of China's strategic emerging industries and selects the tobit analysis method to find out the factors affecting its financing efficiency.

The results show that the average financing efficiency of listed companies in strategic emerging industries between 2010 and 2017 is 0.7792, and the level of financing efficiency of strategic emerging industries is still at a low level. Among them, the bio-pharmaceutical industry and the energy-saving and environmental protection industry have the highest comprehensive level, and the high-end equipment manufacturing industry and the new energy industry have the lowest level of financing efficiency. Among the factors affecting the financing efficiency of strategic emerging industries, the asset-liability ratio, financial expenses and cash ratio and financing efficiency are negatively correlated, and the net asset income is positively correlated with the growth rate of the main business income.

This paper measures the financing efficiency of China's strategic emerging industries, then explores the influencing factors of the financing efficiency of strategic emerging industries and tries to provide important reference values for the improvement of the financing efficiency of China's strategic emerging industries at a practical level.