Search results

This paper sets out to explore the effects that the setting‐up of an independent director system has on the operating efficiency of information electronics companies in China.

This paper uses 87 Chinese listed electronics companies during the initial stages of the independent directors system from 1999 to 2002 as sample subjects, and employs a two‐stage procedure for empirical investigation.

The non‐parametric test results verify that there is no significant difference in the operating efficiency of Chinese electronics companies following the establishment of an independent director system. The Tobit regression results show that the establishment of an independent director system in the Chinese electronics industry does not influence overall technical efficiency (TE), pure technical efficiency (PE), or scale efficiency (SE).

Whether the related schemes of the current corporate governance structure practised in China can achieve their expected results, as well as the possible future development direction of the governance structure, is of the utmost importance, and is a research subject worth examining in greater depth.

It is of the utmost urgency for such corporate governance to improve the selection mechanism for independent directors, to establish incentives and responsibility‐taking mechanisms for independent directors, and to amend the company law and securities law to perfect the rules of an independent director system.

By using DEA and the Tobit regression model, this study attempts to investigate whether China, in addition to fraud prevention, has improved corporate operating efficiency by introducing a system of independent directors.

This chapter proposes M-estimators of a fractional response model with an endogenous count variable under the presence of time-constant unobserved heterogeneity. To address the endogeneity of the right-hand-side count variable, I use instrumental variables and a two-step procedure estimation approach. Two methods of estimation are employed: quasi-maximum likelihood (QML) and nonlinear least squares (NLS). Using these methods, I estimate the average partial effects, which are shown to be comparable across linear and nonlinear models. Monte Carlo simulations verify that the QML and NLS estimators perform better than other standard estimators. For illustration, these estimators are used in a model of female labor supply with an endogenous number of children. The results show that the marginal reduction in women's working hours per week is less as women have one additional kid. In addition, the effect of the number of children on the fraction of hours that a woman spends working per week is statistically significant and more significant than the estimates in all other linear and nonlinear models considered in the chapter.

This paper aims to develop and utilize a conceptual framework to examine the impact of liquidity and solvency on cost efficiency for a sample of Kansas farms.

A standard cost-efficiency model is modified to incorporate liquidity and solvency ratios. Tobit regressions are used to determine the impact of farm characteristics on improvements in efficiency.

Results confirm that liquidity and solvency measures have a significant impact on improving cost efficiency. Farms with larger expenditures on purchased inputs relative to capital were less likely to improve efficiency when liquidity and solvency were considered.

To the authors' knowledge, the paper is the first to add liquidity and solvency ratios to the cost-efficiency model developed by Färe et al. for the analysis of farms.

The purpose of this paper is to examine the relationship between firms' value drivers and their intellectual capital (IC).

The health care sector (GICS 35) firms listed in the S&P500 were used to build a research censoring Tobit model by adopting financial data to determine value drivers.

The results of the study show that innovation capital, customer capital and human capital are significant positive drive factors for firms to create more IC and hence more intangible value. Process capital exerts moderating effects on IC; organizations with greater process capital must raise customer capital to enhance intellectual value.

This is the first empirical study that uses a censoring Tobit model and tests of the association between competitive advantage and the value drivers of firms. This research successfully combines management perspectives with financial data to describe the value drivers of firms.

This study used Data Envelopment Analysis (DEA) to measure and evaluate the operational efficiency of 26 isolation hospitals in Egypt during the COVID-19 pandemic, as well as identifying the most important inputs affecting their efficiency.

To measure the operational efficiency of isolation hospitals, this paper combined three interrelated methodologies including DEA, sensitivity analysis and Tobit regression, as well as three inputs (number of physicians, number of nurses and number of beds) and three outputs (number of infections, number of recoveries and number of deaths). Available data were analyzed through R v.4.0.1 software to achieve the study purpose.

Based on DEA analysis, out of 26 isolation hospitals, only 4 were found efficient according to CCR model and 12 out of 26 hospitals achieved efficiency under the BCC model, Tobit regression results confirmed that the number of nurses and the number of beds are common factors impacted the operational efficiency of isolation hospitals, while the number of physicians had no significant effect on efficiency.

The limits of this study related to measuring the operational efficiency of isolation hospitals in Egypt considering the available data for the period from February to August 2020. DEA analysis can also be an important benchmarking tool for measuring the operational efficiency of isolation hospitals, for identifying their ability to utilize and allocate their resources in an optimal manner (Demand vs Capacity Dilemma), which in turn, encountering this pandemic and protect citizens' health.

Despite the intensity of studies that dealt with measuring hospital efficiency, this study to the best of our knowledge is one of the first attempts to measure the efficiency of hospitals in Egypt in times of health' crisis, especially, during the COVID-19 pandemic, to identify the best allocation of resources to achieve the highest level of efficiency during this pandemic.

Degradable mulch film (DMF) is a potential alternate to polyethylene (PE) mulching. In this regard, the purpose of this paper is to explore the effects and paths of natural disaster shock and risk aversion influencing farmers’ DMF adoption.

This research is conducted by collecting cross-sectional data of corn farmers in Zhangye, China. First, by using the Tobit model, the paper attempts to explore the effects of natural disaster shock and risk aversion influencing farmers’ DMF adoption. Second, IV-Tobit model is applied to deal with endogenous problems between risk aversion and DMF adoption. Additionally, the researchers used a moderating model to analyze feasible paths of natural disaster shock and risk aversion impacting farmers’ DMF adoption.

The outcomes show that natural disaster shock and risk aversion significantly and positively affect farmers’ DMF adoption. Though risk aversion plays a significant moderating effect in influencing farmers’ DMF adoption by natural disaster shock, the moderating effect has a serious disguising effect. By considering the heterogeneity of risk aversion, the paper further confirms that if the intensity of natural disaster shock is increased by one unit, the intensity of MDF adoption by farmers with high-risk aversion also tends to increase by 15.85%.

This study is the pioneer one, which is evaluating the intensity of farmers’ DMF adoption from adoption ratio, investment amount, labor input and adoption time. Additionally, the research provides important guidelines for policymakers to motivate medium and low-risk aversion farmers to adopt DMF.

The aim of this paper is to provide an approach to analyze the performance of TV programs and to identify what can be done to improve them.

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the Ng-model, Grey relational analysis (GRA), and principal component analysis (PCA) were applied to evaluate the programs, using audience, share, and duration as the performance criteria.

By comparing TOPSIS to the Ng-model, PCA, and GRA, we verified that SVD and bootstrap SVD TOPSIS provide a good balance between equal-weights TOPSIS and the other models. This is because SVD and bootstrap SVD TOPSIS break down the data to a higher degree, but are less impacted by outliers compared to the long tail models.

To determine which TV programs should be replaced or modified is a complex decision that has not been addressed in the literature. The advantage of using a multi-criteria decision-making (MCDM) approach is that analysts can choose as many criteria as they want to rank TV programs, rather than relying on a single criterion (e.g., audience, share, target rating point).

This work represents the first time that robust MCDM methodology is applied to an audience data set to analyze the performance of TV programs and to identify what can be done to improve them. This study shows the application of a detailed methodology that is useful for the improvement of TV programs and other entertainment industry content.

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.

This paper aims to analyze bank efficiency into a number of bank‐specific, industry‐specific and macroeconomic determinants.

The authors follow a semi‐parametric two‐stage methodology, where productive efficiency is derived via a non‐parametric technique in the first stage and then the scores obtained are linked to a series of determinants of bank efficiency, using a double bootstrapping procedure.

Overall, it is found that the banking sectors of almost all the sample countries show a gradual improvement in their efficiency levels. The model used shows that a number of determinants like bank size, industry concentration and the investment environment have a positive impact on bank efficiency, which is not the case when standard Tobit models are employed.

The findings have important implications for the relevance of well‐known hypotheses that refer to the performance of the banking sectors, like the structure‐conduct‐performance and the efficient structure hypotheses. These implications are not necessarily verified when past conventional econometric methodologies are used.

The paper offers new insights to policy makers, bank managers and practitioners on the relevance of a number of driving factors of bank efficiency that might help them to improve the performance of the banking system and enhance the quality of services provided.

This is the first paper in the bank efficiency literature that employs a semi‐parametric two‐stage model, which relaxes several deficiencies of previous two‐stage empirical approaches thus, offering a solution to the many problematic features of standard censored regressions.

The purpose of this paper is to contribute to the determinants of export performance literature. The authors investigate the effect of firm characteristics on the Douro region wine firms export performance. The authors consider the Douro region, as it has the Portugal highest wine classification, the appellation d’origine contrôlée and undertakes the first position in the Portuguese wine production and export.

The authors apply a pooling cross-sectional data set that includes 427 observations. The authors pooled two cross sections consisting of 214 and 213 firms for the years 2014 and 215, respectively. The firm export intensity and propensity are the dependent variables. Moreover, the firm size, age and productive efficiency are accounted as the firm characteristics. The authors use the ordinary least squares regression and the tobit and probit models for estimations.

First, size is an influential factor to improve the export performance, and the importance of size is higher for younger firms. Second, there is a positive response from export intensity to age and this response is higher for smaller firms. However, there is a negative response from the export propensity to age and this negative response is higher for bigger firms. Third, there is weak evidence to support a relationship between efficiency and export performance.

This research and the presented results are undoubtedly under some limitations. The main limitation is about the data availability for all characteristics of a firm. For example, it will enrich the result if the authors add some other important variables such as production cost, research and development expenditure and the quality of produced wine by each firm to our analysis.

This research reveals that the influence of firm characteristics on the export performance of Portuguese wine firms is missing in the literature. The results provide a basis to Portuguese wineries to improve their export performance by applying the relevant strategies.