perfomance management
Sharmineh Safarpour; Alireza Amirteimoori; Sohrab Kordrostami; Leila Khoshandam
Abstract
Since the healthcare system is one of the most important pillars of community health, and considering that providing healthcare services to the people is one of the elements of individual development in any country, attention and supervision of this sector can lead to development and social welfare. ...
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Since the healthcare system is one of the most important pillars of community health, and considering that providing healthcare services to the people is one of the elements of individual development in any country, attention and supervision of this sector can lead to development and social welfare. To ensure better and higher quality healthcare services, performance evaluation in the health sector plays a crucial role. In order to achieve this, proper and proportional use of existing facilities and assets is inevitable. In this study, by introducing an application in the field of healthcare systems, the educational hospitals of the country have been measured in terms of performance and their managerial ability has been calculated. Additionally, by identifying and introducing the impact of contextual variables on the performance of decision-making units, their efficiency has been assessed. For this purpose, data related to educational hospitals in 31 provinces of the country was collected, and then by identifying contextual variables and with the presence of undesirable factors, the efficiency was evaluated and the managerial ability of each was calculated. To reach this goal, in the first step, technical efficiency with the presence of undesirable factors was calculated using data envelopment analysis technique, and then the logarithm of technical efficiency obtained from the first stage was regressed on a set of contextual variables that affect hospital performance. In the next stage, managerial ability was extracted from the residual of the regression obtained from the previous stage. Finally, a unique ranking based on the managerial ability of each unit was provided. Ultimately, the results obtained were analyzed and examined in order to provide valuable suggestions for managers and more efficient management of the country's hospitals to maintain public health. According to the study, without considering contextual variables, 25 effective units were evaluated, but by applying the effect of contextual variables on the efficiency index, no unit becomes effective, proving the high impact of such indices on the performance of units. Additionally, in the ranking of units based on managerial ability, Lorestan province ranked first and Golestan province ranked last.IntroductionThe issue of increasing productivity and efficiency in healthcare costs is important for all countries. The health sector, by identifying the factors that affect community health precisely, influences national macroeconomic planning and minimizes their adverse effects on health. By utilizing the best practices in healthcare, significant improvements in the health of individuals and communities can be achieved. Therefore, proper investment in healthcare facilities and health centers, as well as improving the quality and efficiency of their services, is essential for sustainable development. In order to increase efficiency and productivity, understanding the current status and measuring the performance of hospitals in the healthcare system is of paramount importance. Ensuring the provision of better and higher quality health services requires evaluating the performance of the healthcare system. Therefore, it seems that employing efficiency measurement techniques and improving performance and productivity in this sector can improve processes and optimize the use of resources and the fair distribution of resources for the provision of desirable services. In recent years, various studies and methods have been proposed by researchers to measure the efficiency of decision-making units, which can be divided into two categories: parametric and non-parametric methods. Farrell (1957) first introduced the non-parametric method, and then Charnes et al. (1978) extended the initial analysis by Farrell from multi-input and single-output to multi-input and multi-output. The model developed by them was named the Charnes-Cooper-Rhodes model. Then, Banker et al. (1984) introduced the model. The non-parametric method is a linear programming-based method in which a linear programming problem is solved for each decision unit. This branch of operations research has rapidly advanced and is called data envelopment analysis. Data envelopment analysis is a mathematical programming technique for evaluating decision-making units and plays a fundamental role in identifying efficient boundaries and measuring the relative efficiency of units under scrutiny. Data envelopment analysis allows for the comparison of units with each other. Considering the importance of the health sector in improving the quality of life for individuals in society, we felt it necessary to examine the performance level and calculate the managerial capacity of hospitals in all 31 provinces of the country to ensure the proper functioning of this sector and take even small steps towards improving the quality of this sector. The aim of this research is to analyze and evaluate the performance of health sector hospitals in Iran in the presence of contextual variables and provide a ranking method based on managerial capacity. For this purpose, data related to educational hospitals in all 31 provinces of the country were collected, and then, by identifying contextual variables and the presence of undesirable factors, an attempt was made to evaluate the efficiency and calculate the managerial capacity of each hospital unit. To achieve this goal, in the first step, technical efficiency with the presence of undesirable factors was calculated using data envelopment analysis technique, and then the logarithm of technical efficiency resulting from the first step was regressed on a set of contextual variables that affect hospital performance. In the next step, managerial ability was extracted from the residual of the regression from the previous step. Finally, a unique ranking based on the managerial ability of each hospital was presented.MethodologyIn this article, based on studies conducted by Demerjian et al. (2020) and Banker et al. (2020), we examine the performance analysis and managerial abilities of 31 hospitals in the country through a three-stage process. Firstly, considering the presence of undesirable outputs, the efficiency analysis of the units of interest is obtained using the efficiency model proposed by Kuosmanen (2005) with the (3) technology. Then, using the least squares method, the impact of each of the contextual variables in this study, including "asset base", "density", and "number of physicians", on the efficiency scores obtained from the first stage is regressed. Subsequently, managerial ability is obtained from the residuals of the previous least squares method. Finally, a unique ranking based on the managerial ability of each hospital is presented.ResultsIn this study, which was conducted on the performance of the health care in Iran, a new ranking based on managerial ability was provided for comparing units. Based on calculations performed on a number of hospitals in 31 provinces of the country without considering contextual variables, 25 efficient units were evaluated. However, by applying the effect of contextual variables on the efficiency index, no unit appears to be efficient, proving the significant impact of contextual variables on the performance of units. Furthermore, the relationship between contextual variables and efficiency index was determined. For example, an increase in the amount of the contextual variable "number of physicians" will lead to an increase in managerial ability. This means that an increase in the number of physicians will benefit the improvement of the system's efficiency and managerial ability.ConclusionWithout a doubt, studying and investing in the healthcare industry is one of the most profitable and best areas for investment. In this regard, government hospitals in each country are one of the main and most important components of the healthcare sector. The hospitals studied in this research are considered as 3 government hospitals per province. Based on past efficiency studies, we find that each decision-making unit had its own specific inputs and outputs. The aim of this study is to analyze and examine the managerial ability of public hospitals in Iran. In this study, the performance of selected hospital units is analyzed in terms of managerial efficiency, considering the impact of other variables known as contextual variables on the performance of a decision-making unit. In this study, the performance of government hospitals in Iran is analyzed from a managerial perspective. The first step involves calculating the efficiency of units using basic models and considering undesirable outputs. Then, in the second step, the logarithm of technical efficiency obtained from the first step is regressed on a set of contextual variables that affect hospital performance. Furthermore, the impact of contextual variables, including total assets, physician density, and number of physicians, on the size of unit efficiency is measured in this study. Based on the results, 25 efficient units were evaluated, but with the application of contextual variables on efficiency indicators, no unit becomes efficient, proving the high impact of such indicators on unit performance. Additionally, based on the calculations performed, in the ranking of units with a managerial approach, Lorestan province ranks first and Golestan province, which has the weakest performance among the units under study, ranks last. The impact of contextual variables on efficiency indicators has been examined. For example, the impact of the "number of physicians" indicator on efficiency is direct, and a one-unit increase in it will lead to an increase in managerial efficiency. This means that an increase in the number of physicians will benefit the system's efficiency and managerial ability. However, the impact of the density variable, unlike the number of physicians, has an inverse effect on managerial ability. To provide suggestions for future studies, one can refer to generalizing the problem to the uncertainty space and studying different applications by bringing the problem into random spaces, providing more predictive predictions. Furthermore, this study can be implemented in analyzing performance and calculating managerial ability in various industries such as power plants, insurance industry, banks, etc., and based on the applications and the type of technology used, different approaches can be provided for calculating managerial
Maryeh Nematizadeh; Alireza Amirteimoori; Sohrab Kordrostami; Leila Khoshandam
Abstract
The electricity industry plays a pivotal role in a country's economic growth and development. Therefore, it is imperative to assess its performance and identify the strengths and weaknesses of its different sectors, such as production, transmission, and distribution, to enhance economic growth in diverse ...
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The electricity industry plays a pivotal role in a country's economic growth and development. Therefore, it is imperative to assess its performance and identify the strengths and weaknesses of its different sectors, such as production, transmission, and distribution, to enhance economic growth in diverse areas. Given the significance of the transmission sector, this research focuses on analyzing and evaluating the performance of 16 regional electricity companies in Iran from 1390 to 1398, with the aim of comprehending the impact of contextual variables on efficiency. To achieve this, the study will utilize two techniques - Data Envelopment Analysis (DEA) and Ordinary Least Squares (OLS) - to determine the efficiency score and estimate the effect of contextual variables on efficiency, respectively. In the first stage, the DEA technique is employed to calculate the technical efficiency of each company, considering their specific inputs and outputs. In the second stage, the logarithm of the efficiency scores obtained is regressed on contextual variables to establish their effect on efficiency. The residual derived from the regression is referred to as managerial ability. Finally, the companies are ranked based on their modified efficiency after removing the impact of contextual variables.
Introduction
The electricity industry comprises three key sectors: production, transmission, and distribution. It stands as one of the most crucial economic infrastructures in the country, exerting significant influence on industrial, agricultural, service, and other sectors. Undoubtedly, the growth of the electricity industry drives the nation's economic development and progress, contributing to the prosperity and comfort of its citizens (Tavassoli et al., 2020). Consequently, analyzing and examining the growth trajectory of each sector across different years becomes pivotal in mitigating adverse effects and fostering progress within this domain.
In recent years, numerous researchers have conducted studies in this field. Some have independently evaluated each production, transmission, and distribution sector, while others have adopted a comprehensive approach by considering the integrated three-stage network structure. The research background highlights that the transmission sector has received less attention from researchers than other sectors. This is noteworthy because, following electricity production, the transmission process and energy accessibility to consumers are paramount. The absence of proper energy transfer can result in consumer dissatisfaction, financial losses, and stagnation within the competitive economic market. Therefore, identifying the strengths and weaknesses of the transmission sector's performance and comparing regional electricity transmission companies can effectively help enhance the performance level of each.
One technique that has captured researchers' attention for evaluating the electricity industry's performance is the data envelopment analysis (DEA) technique. DEA is a non-parametric method used to assess the performance of homogeneous units, considering multiple inputs and outputs. It was initially introduced in 1978 by Charnes et al. The initial model was built upon the assumption of constant returns to scale. Subsequently, Banker et al. (1984) extended it by presenting a model under the assumption of returns to a variable scale.
Importantly, traditional DEA models evaluate a system's performance based on specific inputs and outputs consumed and produced by the unit. However, various factors, such as contextual variables, managerial ability, and skill, can significantly influence performance and productivity. A crucial point to consider is that managerial abilities are not always overtly visible. This lack of direct visibility can impede accurate measurement. Hence, recognizing these variables among the existing indicators and assessing their influence on the performance and efficiency of each unit holds particular significance. This procedure enhances the precision of evaluation and opens avenues for delivering enhanced solutions aimed at improving the system's overall performance.
Methodology
The objective of this study is to analyze and evaluate the performance of Iran's regional electricity transmission sector while considering contextual variables and establishing a ranking methodology based on managerial ability. This perspective enables the identification of strengths and weaknesses in the system's structure from various angles and offers appropriate solutions for enhancement. To accomplish this, the first step involves identifying all variables within the transmission section, encompassing inputs, outputs, and contextual factors. Subsequently, we determine the technical efficiency of each regional power transmission company, taking into account specific inputs and outputs, using meta-frontier technology. The concept of meta-frontier in DEA measures the gap or distance between decision-making units (DMUs) across different boundaries. This approach assumes a unified boundary for all subgroups, enabling efficiency estimation based on a single boundary (Battese, 2004; O'Donnell, 2008). Its primary advantage lies in resolving the challenge of evaluating efficiency at varying levels. As a result, meta-frontier technology enhances the precision of evaluating regional power companies over multiple periods. After assessing the efficiency of each regional electricity transmission company, we employ the linear regression method to estimate the impact of contextual variables on efficiency, subsequently yielding a measure of managerial ability. Ultimately, we introduce a method for ranking each company based on managerial ability. The advantage of the proposed method is that, in addition to reviewing and analyzing the technical efficiency of each of the companies in the regional electricity transmission sector during different periods, it will be possible to evaluate the managerial ability of each of the companies. Such a perspective allows for companies to be compared from different dimensions. Moreover, providing a new ranking criterion based on managerial ability also facilitates a better and more accurate comparison.
Results
In this study, the performance of Iran's regional power companies was analyzed and evaluated from two systems and management perspectives during the years 1390-1398. Additionally, a new rating criterion based on managerial ability was presented to compare the performance of companies during 9 time periods. In this regard, firstly, the technical efficiency of 16 regional electricity companies during 9 time periods was calculated based on the inputs of the number of employees and receiving energy from neighboring companies and the outputs of sending energy to neighboring companies and delivering energy to distribution companies, using meta-frontier technology and the DEA approach. Then, the effect of contextual variables, such as line length, transformer capacity, and loss magnitude, on the efficiency score of each company was estimated using the ordinary least squares method (OLS). Furthermore, the managerial ability of each company was determined during different periods. Ultimately, a ranking criterion was established based on the results of technical efficiency after removing the effect of contextual variables.
Conclusion
The results of efficiency measurements over 9 time periods indicate that the highest and lowest average efficiencies were observed in the years 1390 and 1398, respectively. Furthermore, it's evident that, in general, the performance of Iran's 16 regional electricity companies exhibited a consistent upward trend from 1390 to 1398. Among the 16 evaluated companies, the Guilan regional electricity company consistently achieved the highest level of efficiency across all 9 time periods, reflecting its strong performance. Conversely, the Fars regional electricity company consistently had the lowest efficiency, indicating its weaker performance compared to other companies. When analyzing the companies' performance by year, it's noteworthy that the Tehran regional electricity company secured the highest rank in 1390, 1391, and 1394, while the Fars regional electricity company held the top spot in the remaining years. In contrast, the Sistan regional electricity company consistently displayed the lowest performance throughout all periods. The assessment of management performance over the 9 time periods indicates that the Kerman regional electricity company demonstrated superior performance from 1390 to 1393, whereas the Guilan regional electricity company excelled from 1394 to 1398, outperforming other companies. Conversely, the Gharb regional electricity company exhibited weaker performance compared to its counterparts. Additionally, the results of the regression analysis highlight a positive relationship between the efficiency score and two variables: line length and transformer capacity. Conversely, the relationship with loss magnitude is observed to be inversely correlated.
