Inefficient Companies Research Articles

A Performance Study on the Potential Improvement of Construction Sector Companies in Malaysia

The construction sector is one of the important contributors to economic growth in Malaysia through Malaysia’s GDP significantly. In this research, 35 companies from Bursa Malaysia, which are selected from construction sector under main market covered year 2006-2021 have been studied. The Construction 4.0 Strategic Plan (2021-2025) was launched by CIDB Malaysia to transform construction industry by utilising the innovative technologies to build a more effective, sustainable and integrated system. This research aims to examine the efficiency performance of each construction firm so that it can be advantaged to policymakers, investors, as well as stakeholders. Through these insights, it can assist the stockholders to make wise decisions in term of how to relocate the resource and investment opportunities. This research uses the DEA model with financial ratios, which included three inputs: debt to assets ratio (DAR), debt to equity ratio (DER), current ratio (CR) and three outputs: earning per share (EPS), return on equity (ROE) and return on asset (ROA) to fill the research gap on the effectiveness of construction sector in Malaysia as there has yet to be a comprehensive investigation by other researchers. The efficiency of the companies was analysed by DEA model and only 8 companies: DKLS, KERJAYA, MERCURY, MERGE, MGB, PRTASCO, PTARAS and ZECON are examined as efficient companies. Lastly, the potential improvement for each inefficient company also have been determined.

Analisis Risiko dan Return Menggunakan Metode CAPM Terhadap Keputusan Investasi pada Indeks LQ45 Periode 2018-2022

This study aims to analyze stock returns and risks, as well as grouping efficient and inefficient stocks to make investment decisions using the Capital Asset Pricing Model (CAPM) model. This type of research is descriptive research with a quantitative approach. The samples used were companies in the LQ45 index in the infrastructure and transportation sectors during the 2018-2022 period using the purposive sampling method so that a total sample of 2 companies was obtained. Calculations using the CAPM model result in 1 efficient company and 1 inefficient company. Shares of PT. XL Axiata Tbk. is an undervalued stock, because the value of the individual rate of return (Ri) is higher at 0.00152 or 0.15%, with the resulting investment decision for stocks in the efficient category is to consider buying these shares at the lowest price today before experiencing an increase in the future. While the shares of PT. Telkom Indonesia Tbk. is an overvalued stock, because the value of the individual rate of return (Ri) is lower at 0.00190 or 0.19% of the expected rate of return E(Ri) which is 0.00223 or 0.22%, the resulting investment decision for stocks with inefficient category is to sell the shares at the highest price now and before experiencing a price decline in the future.

Does Your Company Engage Efficiently on Social Media? Improving Performance Using DEA Model and Visualization

ABSTRACT: Despite an implosion in the use of social media, quantifying the efficiency of customer engagement continues to be a major challenge for most companies. In this study, we propose a data envelopment analysis (DEA) model to measure the relative operational efficiency of companies' customer engagement on social media based on non-financial inputs and outputs. We collected data from the Facebook and Twitter pages of 56 fast-food companies over 12 months in 2020, and used DEA, cluster analysis, and visualization techniques to perform a comparative efficiency analysis. Our study identified four distinct clusters of companies and mapped them to the corresponding stages of the social media maturity (SMM) model. By integrating the DEA model with the SMM, we provide a novel mechanism for assessing social media engagement efficiency. Furthermore, based on the results of our analysis, we offer strategic and tactical level recommendations to inefficient companies for improving their social media engagement performance.

Regularized conditional estimators of unit inefficiency in stochastic frontier analysis, with application to electricity distribution market

In stochastic frontier analysis, the conventional estimation of unit inefficiency is based on the mean/mode of the inefficiency, conditioned on the composite error. It is known that the conditional mean of inefficiency shrinks towards the mean rather than towards the unit inefficiency. In this paper, we analytically prove that the conditional mode cannot accurately estimate unit inefficiency, either. We propose regularized estimators of unit inefficiency that restrict the unit inefficiency estimators to satisfy some a priori assumptions, and derive the closed form regularized conditional mode estimators for the three most commonly used inefficiency densities. Extensive simulations show that, under common empirical situations, e.g., regarding sample size and signal-to-noise ratio, the regularized estimators outperform the conventional (unregularized) estimators when the inefficiency is greater than its mean/mode. Based on real data from the electricity distribution sector in Sweden, we demonstrate that the conventional conditional estimators and our regularized conditional estimators provide substantially different results for highly inefficient companies.

Firms’ efficiency, exits and government procurement contracts

This study shows that productivity trends in Russia are similar to those in other countries where technology leaders enjoy productivity growth, with a gap increasing between them and other companies. The survival analysis suggests that the most efficient firms quit the market at a faster rate than other companies do. Survival functions of the least efficient firms do not always differ significantly from those of other enterprises. Additional financial support from government contracts helps firms in all efficiency groups survive but this effect is stronger for laggards. The shift of gains from the public procurement system towards low productive firms leads to the distorted allocation of resources in the economy and, in the long run, may undermine economic growth by allowing inefficient companies to stay in the market significantly longer than would be the case in a more competitive environment.

Evaluation of insurance companies considering uncertainty: A multi-objective network data envelopment analysis model with negative data and undesirable outputs

Uncertainty is an important issue to consider when evaluating entities in both public and private sectors. On the other hand, many operations have more than one stage process when some inputs are fed to the system to produce a number of intermediate measures. The intermediate measures are then transformed into final products in the subsequent stages. The composition method in network data envelopment analysis (NDEA) is a popular method for measuring the efficiency of a two-stage process. The composition method is fractional bi-objective programming that is solved by non-linear programming techniques such as bisection search. In this paper, the two-stage NDEA is extended with negative data and undesirable outputs. First, we propose an alternative linear model based on the goal programming technique to avoid complex non-linear calculations. Then, we use a method to transform negative data into positive and undesirable outputs into desirable ones. Finally, we develop the proposed model using the fuzzy α-cut approach in order to incorporate data uncertainty in the linear goal programming (GP) model. To validate the accuracy of the proposed model, a numerical example is solved. To show the applicability of the proposed model, a real case of 22 insurance companies is examined. We also perform a comparative analysis to specify the benchmark and inefficient companies. Comparative analysis can help managers to recognize where improvement should be investigated with priority.

Measuring the environmental efficiency of the investment for offshore wind power site in Taiwan

Many of the studies is based on parameters with the requirement of pre-estimation and huge function clause format. But in the proposed method, no pre-estimation is required and there is no need to establish a functional relationship between the described and dependent variables. This research investigates the environmental performance and the factors affecting the environmental performance of offshore wind energy companies in Taiwan using super-efficiency data envelopment analysis (SE-DEA) and Tobit regression model, respectively. SE-DEA model was theoretically used in the first step of analysis, followed by Tobit model in the second step. The study was based on 2019 cross-sectional data obtained from seven locations of Taiwan including Songshan, Taoyuan, Nantou, Zhongzheng, Xinyu, and Changhua. It was found that offshore wind power companies in Taiwan had an average environmental efficiency score of 1.8953, which was very high. Moreover, this performance score could be further increased by improving the factors that affected the efficiency. Such factors included age of company, numbers of internal boards, number of shareholders, research and development, ISO 45,001, government policy, personnel quality, and wind speed. Increase in number of shareholders resulted in higher technical efficiency by a significant level at 1%. When the number of internal boards increased 2.20 %, the efficiency score increased up to 100 %. Amount of research and development factors increased by 12.16 %, the effective score was 100 %, with a 5% prioritization with a positive coefficient front sign. This shows how much the company has research and development relating to offshore wind power. Higher amount of research and development resulted in higher technical efficiency. When I.S.O 45,001 factor increased by 41.94 %, the effective score was 100 %, with a 1% significant level and a positive coefficient front mark. The government policy increased the technical efficiency by a significant level at 1%. When government policy increased to 36.54 %, the efficient score enhanced up to 100 %. The factor quality personnel showed positive signs. Increasing number of qualified personnel enhanced the technical efficiency, with the efficiency level at 25.23 %. The factor wind speed had a positive coefficient, corresponded with the hypothesis. Increase in wind speed leaded to higher technical performance score, with a focus of 5%. When wind speed rose to 2.98 %, the efficiency score increased up to 100 %. The best environmental performance was of Wpd Taiwan Energy Co., Ltd company which had the highest score of 3.2521, while the Copenhagen Infrastructure Partners Association company had the lowest score of 0.9136, indicating the worst environmental efficiency. Inefficient companies can improve their environmental efficiency based on their efficiency scores and ratings in order to be a leading offshore wind power facility in Taiwan.

Performance Evaluation of Food and Beverage Listed Companies in Vietnam

During the last decade, the food and beverage industry has been one of the most significant and prioritized industries that contributed to the economic growth in Vietnam. Therefore, how to enhance the performance of food and beverage firms has become a critical factor for Vietnam’s economic development. This research aims to use the data envelopment analysis (DEA) and the Malmquist productivity index (MPI) to assess changes in operational performance and productivity of listed lead food and beverage firms in Vietnam during the period between 2015 and 2020. The obtained results reveal that Vietnamese food and beverage firms were generally inefficient between 2015 and 2020 because the number of relatively inefficient companies was higher than the relatively efficient ones in each year between 2015 and 2020. The MPI findings indicate the growth in productivity during the study period as a result of technological progress. By integrating the findings of the relative efficiency and productivity change, this study creates five decision-making matrixes for five periods, respectively, from 2015 to 2020 to position the food and beverage firms in each sector. The analytical results provide instructions to senior managers on developing strategy for increasing efficiency of food and beverage listed companies in Vietnam.

ЭКОНОМИЧЕСКИЕ МЕХАНИЗМЫ ОЦЕНКИ ЭНЕРГОЭФФЕКТИВНОСТИ КРУПНЫХ ЭНЕРГЕТИЧЕСКИХ КОМПАНИЙ КАК ФАКТОР ИХ ИНВЕСТИЦИОННОЙ ПРИВЛЕКАТЕЛЬНОСТИ

Today, institutional and private investors face the problem of an objective investment assessment of companies when investing their funds. Fundamental financial indicators of companies do not always fully reflect their efficiency, including when using fuel and energy resources. This problem is especially relevant for energy companies that consume a significant amount of fuel and where excess fuel losses are often included in the tariff to the consumer. In this work, the object of the research is various energy companies in Russia, whose shares are traded on the Moscow Exchange. The subject of the research is the investment attractiveness of large energy companies with an emphasis on their energy efficiency. The purpose of the research is to develop a new way to assess the investment attractiveness of large energy companies, taking into account their actual energy efficiency. The main research tasks are to analyze existing methods for assessing the financial efficiency of heat generating companies, as well as to develop a new method for assessing the investment attractiveness of heat generating companies, taking into account their actual energy efficiency. For the analysis of key fundamental financial indicators, three heat generating companies located in different regions of Russia and using different energy production cycles, as well as using different types of fuel have been selected. The methodology and research methods are based on a fundamental financial analysis of the indicators of heat generating companies. A comparative analysis of the profitability of three heat generating companies, as well as the average indicators for the industry, revealed the most inefficient company. For this company, an analysis of other financial indicators for the last ten reporting years has been performed. A comparative analysis of the specific fuel consumption for the generation of heat and electricity in these companies has also been performed. Based on the results of the results obtained, it is proposed for an objective investment assessment of heat generating companies to adjust their revenue and profitability, taking into account the indicators of their actual energy efficiency

Financial performance of the textile industry in India: the case of Haryana

PurposeThis study aims to evaluate the financial performance of the textile industry in Haryana located in the northern part of India.Design/methodology/approachInput-oriented Cooper, Charnes and Rhodes (CCR) and Banker, Charnes and Cooper (BCC) techniques of data envelopment analysis, as well as the return to scale (RTS) technique, were used to conduct the analysis.FindingsThe findings show that textile units in Haryana have hugely underperformed financially with a consolidated technical efficiency score of only 0.35. Both private and public limited textile companies with respective scores of 0.46 and 0.24 are technically efficient. Public limited textile companies are more efficient than private limited companies. Private limited textile companies need to increase their input scale because they are operating at an increasing return to scale while public limited textile companies have to lower their input scale because most companies are operating at a decreasing return to scale to enhance their efficiency.Originality/valueThe study can assist in decision-making to all key stakeholders (Shareholders, management, government, tax authorities, debtors and creditors, among others) by identifying efficient and inefficient companies. Appropriate policies can be framed based on that knowledge.

An Integrated DEA and PROMETHEE II Method for Complete Ranking: An Application in Life-Insurance Sector

Globally, insurance and takaful companies play a crucial role in economic and financial development of a country. Malaysia’s insurance sector has undergone substantial changes compared to two decades ago. Due to intense competition observed in Malaysian insurance industry, efficiency measurement and a complete efficiency ranking of insurance companies are very important for the decision makers so that necessary changes and improvement can be made. Data Envelopment Analysis (DEA) is a non-parametric method that has been acknowledged as an effective method to measure the efficiency of homogeneous decision-making units (DMUs). The main advantage of DEA is its ability to handle multiple inputs and outputs. However, standard DEA has poor discrimination power since it generates too many efficient units especially when the number of DMUs under study is insufficient in comparison to number of inputs and outputs. It cannot discriminate efficient units and therefore unable to give a complete ranking of DMUs. This study aimed to overcome the ranking problem found in standard DEA by integrating DEA and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) II. The hybrid method was applied to evaluate the efficiency and ranking of 22 life insurance and takaful companies in Malaysia from the period of 2017 to 2018. Input variables used in this study comprised of fees and commission, and management expenses. Meanwhile, output variables were net premium and generated investment income. The proposed method involved two stages. In the first stage, DEA was applied to obtain the efficient scores for the DMUs. In the second stage, PROMETHEE II was implemented to rank the efficient units. This hybrid method has successfully obtained full ranking of all the insurance companies under study. The inefficient companies can learn strategies and practices from efficient companies to enhance their services. It is recommended for future research to integrate DEA with other Multi Criteria Decision Making (MCDM) methods such as TOPSIS and VIKOR to fully rank the DMUs.

An Assessment of the Efficiency of Canadian Power Generation Companies with Bootstrap DEA

Power generation companies play an important role in the Canadian economy, as most of the economic activities in the manufacturing and service sectors are powered by electricity. The significance of the Canadian power generation industry shows that efficiency analysis is essential for efficiently managing power generation and distribution in Canada. However, there have been few attempts to study the relative efficiencies of the Canadian power generation companies. This study fills in this gap by assessing the overall technical, managerial, and scale efficiencies of a sample of Canadian power generation companies via the non-parametric bootstrap DEA methodology, with firm-level annual inputs and outputs data over an 18-year horizon. The results of our investigation indicate low levels of overall technical and managerial efficiencies but relatively high levels of scale efficiencies of the Canadian power generation companies over the entire study period. We also found that the 2007–2009 financial crisis impacted the relative performance of the Canadian power generation companies. Our results also allowed us to identify the benchmark power generation companies for each type of efficiency that the inefficient companies should target toward improving their efficiency.

Application of Data Envelopment Analysis Method Based on E-commerce Internet Supply Chain Management

Objectives: The department mainly studies the application of data envelopment analysis method based on e-commerce Internet supply chain management in China’s economic management. Methods: Taking the number of state-owned shares of the listed company and the size of the company as external environmental factors, firstly, the three-stage data packet analysis (DEA) is used to measure the operational efficiency of 312 e-commerce companies, thus a DEA model is established. Results: Secondly, the DEA projection analysis is used to quantify the output of the inefficient company, and the improvement plan of the company’s factor input is proposed according to the size of the projection value. Conclusion: Finally, the sample is selected for empirical analysis. The results show that projection analysis can effectively improve the economic operation efficiency of enterprises.

Kanpur nagar district- a msmes cluster hotbed for sustainable economic growth

Research Purpose/Scope: A dichotomy flows between the traditional Micro Small and Medium Enterprises (MSMEs henceforth) cluster and newly established cluster in the context of their emergence, evolution and growth. Now, following the path of big industries, MSME have linked itself with the economies of scale, entrepreneurship, venture capital and knowledge management Here, Kanpur Nagar District has been taken as resource region where the research reflected that this region has the potentiality to become the driver of cluster based economic growth. The research paper dealt with the economic fundamentals of region, comparative advantages of innovation and market technology factors which are responsible for the growth and sustainability of the MSME cluster in Kanpur Nagar District. Materials and Methods: The empirical study of selected MSME’s which have mutual competitiveness & repulsion has been done. The tabular data has interpreted the composition, size, situation and association of MSME’s in the region. The linear diagrams has reflected the dooming share of MSME’s specially the leather and bristle-based clusters. A need and opportunity-based model will set a functional approach for the complementarity of the market (vertically and horizontally) and capacity building of firms. Findings of the Research Paper: A declining trend has been witnessed in the MSME’s because of sick firms and factories, unavailability of skilled workers and trainers, low capital assets of firms and inefficient companies. Clustering can create an effective supply chain for the viability of the market economy. It can improve the condition of firms and pool the money for the required infrastructure and investment for further upgradation. Therefore, it becomes imperative to go for clusterization based directive policies because there is a huge scope for profit sharing between government and local artisans. Establishment of Kanpur Nagar as a MSMEs cluster hotbed, will be a new challenge with major risks and responsibilities.

Modeling innovation efficiency, its micro-level drivers, and its impact on stock returns

Motivated by the COVID-19 pandemic and ensuing challenges to human and economic welfare, this research seeks to evaluate innovation efficiency, its micro-level drivers, and its impact on stock returns. This study considers innovation-related activities during two growth phases experienced by 138 listed pharmaceutical manufacturing companies in China: research and development (R&D) and marketing. A dynamic two-phase network data envelopment analysis (DEA) model measured R&D efficiency, marketing efficiency, and dynamic integrated innovation efficiency. A projection difference analysis was presented to offer a feasible solution to address inefficient companies. Then, panel regression methods were adopted to examine micro-level drivers of innovation efficiency. Additionally, a portfolio formation test was used to investigate innovation efficiency as a characteristic impacting stock returns. The results indicate that the listed companies are generally innovation inefficient. Only two are DEA innovation efficient. Low marketing and R&D efficiencies are impeding innovation efficiency amelioration, with the most losses attributed to the marketing phase. Most companies lack good conditions for R&D and the commercialization of scientific and technological breakthroughs. Institutional investors and financial analysts' coverage have a positive and significant impact on innovation efficiency. Foreign institutional ownership and stock market overvaluation impact listed companies with high innovation efficiency positively. There is a negative and significant effect of size on innovation efficiency. However, growth in pharmaceutical manufacturing company size positively influences innovation efficiency at higher levels. The portfolio formation test results reveal that investors consider companies with high innovation efficiency as high-profile targets that provide high stock returns. The findings of this study offer stakeholders avenues to shape the initiation, process, and outcome of innovation.

Evaluation and Improvement of the Efficiency of Logistics Companies with Data Envelopment Analysis Model

The performance of global trade depends on the logistics industry to move products, information, finances, technology and human resources along the supply chain. The current situation during the pandemic relies on the logistics industry particularly in the courier, parcel and express service providers to deliver daily essentials. Product customization, customer demand, technological sophistication, threat of new entrants, border closure, compliance to Covid-19 regulations and global economic crisis have taken the logistics industry by storm. For the sustainment and growth of these companies, strategic decision making shall take place. A huge determinant of these decisions is the financial efficiency of the companies. Therefore, this paper aims to determine the efficiency of the logistics companies in Malaysia by analyzing their financial performances using current ratio, debt to assets ratio, debt to equity ratio, earnings per share, return on assets and return on equity with data envelopment analysis model. The results of this study found that five companies, COMPLET, GDEX, MISC, SURIA and WPRTS are efficient. This study fills the research gap by determining the efficiency scores of these companies and suggesting potential improvements for inefficient companies to enhance and optimize their financial positions.

Sustainability efficiency assessment of listed companies in China: a super-efficiency SBM-DEA model considering undesirable output.

Chinese listed companies play a vital role in national economy, which has contributed more than half of the gross domestic product. But the environmental pollution existed in the production and operation of companies has been ignored, so an objective and comprehensive efficiency assessment of Chinese listed companies were urgent. This paper constructed an index system based on green perspective, employed a super-efficiency slacks-based measure DEA (SBM-DEA) model considering undesirable outputs to assess the sustainability efficiency of Chinese listed companies. The results indicated that (1) the green sustainability efficiency of listed companies needed to be greatly improved. The average efficiency score of samples was 0.461 over 3 years, so the improvement potential was about 53.9%, only 23 companies were relatively effective. (2) The sustainability efficiency of companies has shown a slow upward trend in volatility since 2017, the non-daily consumer goods sector was the most efficient, while utilities were relatively inefficient. (3) When efficient and inefficient companies were compared, the latter were found to have significant input surplus, especially in water consumption. (4) The analysis of sensitivity on inputs and outputs showed that attention should be paid to water consumption and greenhouse gas emissions. (5) Spearman non-parametric test verified that company size and debt-paying ability were the implicit factors affecting company sustainability efficiency. The results of performance evaluation can not only provide a potential reference for the operation and management of listed companies in China, but also have guiding significance for local governments to strengthen the supervision of companies.

A DEA-Based Decision Support Framework for Organizations' Performance Evaluation considering TQM and Knowledge Management.

Total quality management (TQM) and knowledge management (KM) play a significant role in improving the situation of the organization and creating and maintaining competitive advantage (CA). Therefore, harmonizing TQM and KM with the organizational goals and performance leads to the organization's greater capabilities. The current paper presents a decision support framework to evaluate the performance of organizations based on the success factors (SFs) of TQM and KM using the BCC model of data envelopment analysis (DEA). The SFs of KM are considered as input and the SFs of TQM are considered as output of the BCC model, and the efficiency scores of five pharmaceutical companies in Esfahan, Iran, are obtained. The results show that, among the aforementioned companies, three companies were recognized as efficient, while two of them were introduced as inefficient companies. Furthermore, in order to provide more recommendations for the managers of these companies, two area charts according to SFs of TQM and KM were drawn, and the results were analyzed and discussed.

Efficiency Analysis of Chinese Blockchain Concept Stock Listed Companies

With the continuous development and application of Internet technology, in recent years, new technologies such as cloud computing, big data, the Internet of Things, and AI are becoming more and more familiar to the general public. The development of a digital society has entered a new period of development. In this paper, we used on the 2018 annual data of 50 listed companies with blockchain concept stocks in China. Using data envelopment analysis (DEA) to study and analyze the input-output efficiency, it can be concluded that the input-output efficiency of 50 listed companies is very different. Inefficient companies are as high as 62%. Most companies have a large room for improvement in input-output efficiency due to uneconomical scale or inefficient technology. In order to better improve the company's input-output efficiency, one must improve the efficiency of resource utilization, optimize the company's research and development costs and the input and management of technical personnel; the second is to increase technological innovation and business innovation.

Особливості та пріоритети розвитку ринку страхових послуг в умовах пандемії COVID-19

The article analyzes the features, trends and priorities in the development of the insurance market in Ukraine in the context of the COVID-19 pandemic. In particular, the main indicators of the development of the insurance market during the pandemic 2020 were analyzed and the comparison of these indicators from 2019 was made. The main factors that in today’s realities will encourage consumers to buy insurance protection are allocated, the peculiarities of interaction of insurers with insurance companies when concluding insurance contracts during the quarantine period are highlighted. It is determined that development of the insurance market in Ukraine in the context of the global coronavirus pandemic and in the novel regulatory conditions was characterized by the active implementation of innovative technologies in the activities of insurance companies and the «purification» of the market from weak and inefficient companies. It is proved that according to the main indicators of development, the market has suffered a slight recession, and therefore, the impact of the pandemic on the insurance market in Ukraine can be considered moderate. It is detected that the key factors when choosing an insurance company to buy insurance protection today are: opportunity to purchase an insurance service online; individual tariffs for each consumer based on their specific needs; presence of an easy-to-use mobile application. Thus, insurance companies that will be able to quickly reorganize their IT infrastructure to simplify the customers’ access to insurance services, providing the most convenient way (channel) of interaction, and offering products that best meet their lifestyle and needs, will strengthen consumer confidence and develop their business within those consumer ecosystems that are being formed in today’s turbulent conditions of development.