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1 – 10 of over 1000Taining Wang and Daniel J. Henderson
A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production…
Abstract
A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.
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To improve the position tracking efficiency of the upper-limb rehabilitation robot for stroke hemiplegia patients, the optimization Learning rate of the membership function based…
Abstract
Purpose
To improve the position tracking efficiency of the upper-limb rehabilitation robot for stroke hemiplegia patients, the optimization Learning rate of the membership function based on the fuzzy impedance controller of the rehabilitation robot is propose.
Design/methodology/approach
First, the impaired limb’s damping and stiffness parameters for evaluating its physical recovery condition are online estimated by using weighted least squares method based on recursive algorithm. Second, the fuzzy impedance control with the rule has been designed with the optimal impedance parameters. Finally, the membership function learning rate online optimization strategy based on Takagi-Sugeno (TS) fuzzy impedance model was proposed to improve the position tracking speed of fuzzy impedance control.
Findings
This method provides a solution for improving the membership function learning rate of the fuzzy impedance controller of the upper limb rehabilitation robot. Compared with traditional TS fuzzy impedance controller in position control, the improved TS fuzzy impedance controller has reduced the overshoot stability time by 0.025 s, and the position error caused by simulating the thrust interference of the impaired limb has been reduced by 8.4%. This fact is verified by simulation and test.
Originality/value
The TS fuzzy impedance controller based on membership function online optimization learning strategy can effectively optimize control parameters and improve the position tracking speed of upper limb rehabilitation robots. This controller improves the auxiliary rehabilitation efficiency of the upper limb rehabilitation robot and ensures the stability of auxiliary rehabilitation training.
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This study aims to examine how woman leadership (i.e., woman board chairperson, woman chief executive officer (CEO) and board gender diversity) affects audit fee and also…
Abstract
Purpose
This study aims to examine how woman leadership (i.e., woman board chairperson, woman chief executive officer (CEO) and board gender diversity) affects audit fee and also ascertained the interactive effect of woman leadership and gender diversity on audit committee on audit fee.
Design/methodology/approach
The study applied ordinary least square and fixed-effect estimators on the data of 21 universal banks in Ghana for the period 2010–2021 to estimate the empirical results.
Findings
It is revealed that under the leadership of women (woman CEO and board gender diversity), higher external audit quality is ensured as higher audit fee is paid. Interestingly, it was found that with the presence of women on the audit committee, the integrity of internal controls and internal audit procedures are enhanced, which leads to quality financial reporting, calls for lower audit effort, hence lower audit fee.
Practical implications
The result indicates that firms can rely on the leadership of women in ensuring quality external audit and quality financial reporting, which ultimately helps to minimize the information risk to all stakeholders.
Originality/value
The paper contributes to extant literature by establishing that, under the leadership of women in banking entities from a developing country context, external audit quality and financial reporting are achieved.
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The standard method to estimate a stochastic frontier (SF) model is the maximum likelihood (ML) approach with the distribution assumptions of a symmetric two-sided stochastic…
Abstract
The standard method to estimate a stochastic frontier (SF) model is the maximum likelihood (ML) approach with the distribution assumptions of a symmetric two-sided stochastic error v and a one-sided inefficiency random component u. When v or u has a nonstandard distribution, such as v follows a generalized t distribution or u has a
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Feifei Zhong, Guoping Liu, Zhenyu Lu, Lingyan Hu, Yangyang Han, Yusong Xiao and Xinrui Zhang
Robotic arms’ interactions with the external environment are growing more intricate, demanding higher control precision. This study aims to enhance control precision by…
Abstract
Purpose
Robotic arms’ interactions with the external environment are growing more intricate, demanding higher control precision. This study aims to enhance control precision by establishing a dynamic model through the identification of the dynamic parameters of a self-designed robotic arm.
Design/methodology/approach
This study proposes an improved particle swarm optimization (IPSO) method for parameter identification, which comprehensively improves particle initialization diversity, dynamic adjustment of inertia weight, dynamic adjustment of local and global learning factors and global search capabilities. To reduce the number of particles and improve identification accuracy, a step-by-step dynamic parameter identification method was also proposed. Simultaneously, to fully unleash the dynamic characteristics of a robotic arm, and satisfy boundary conditions, a combination of high-order differentiable natural exponential functions and traditional Fourier series is used to develop an excitation trajectory. Finally, an arbitrary verification trajectory was planned using the IPSO to verify the accuracy of the dynamical parameter identification.
Findings
Experiments conducted on a self-designed robotic arm validate the proposed parameter identification method. By comparing it with IPSO1, IPSO2, IPSOd and least-square algorithms using the criteria of torque error and root mean square for each joint, the superiority of the IPSO algorithm in parameter identification becomes evident. In this case, the dynamic parameter results of each link are significantly improved.
Originality/value
A new parameter identification model was proposed and validated. Based on the experimental results, the stability of the identification results was improved, providing more accurate parameter identification for further applications.
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Banumathy Sundararaman and Neelakandan Ramalingam
This study was carried out to analyze the importance of consumer preference data in forecasting demand in apparel retailing.
Abstract
Purpose
This study was carried out to analyze the importance of consumer preference data in forecasting demand in apparel retailing.
Methodology
To collect preference data, 729 hypothetical stock keeping units (SKU) were derived using a full factorial design, from a combination of six attributes and three levels each. From the hypothetical SKU's, 63 practical SKU's were selected for further analysis. Two hundred two responses were collected from a store intercept survey. Respondents' utility scores for all 63 SKUs were calculated using conjoint analysis. In estimating aggregate demand, to allow for consumer substitution and to make the SKU available when a consumer wishes to buy more than one item in the same SKU, top three highly preferred SKU's utility scores of each individual were selected and classified using a decision tree and was aggregated. A choice rule was modeled to include substitution; by applying this choice rule, aggregate demand was estimated.
Findings
The respondents' utility scores were calculated. The value of Kendall's tau is 0.88, the value of Pearson's R is 0.98 and internal predictive validity using Kendall's tau is 1.00, and this shows the high quality of data obtained. The proposed model was used to estimate the demand for 63 SKUs. The demand was estimated at 6.04 per cent for the SKU cotton, regular style, half sleeve, medium priced, private label. The proposed model for estimating demand using consumer preference data gave better estimates close to actual sales than expert opinion data. The Spearman's rank correlation between actual sales and consumer preference data is 0.338 and is significant at 5 per cent level. The Spearman's rank correlation between actual sales and expert opinion is −0.059, and there is no significant relation between expert opinion data and actual sales. Thus, consumer preference model proves to be better in estimating demand than expert opinion data.
Research implications
There has been a considerable amount of work done in choice-based models. There is a lot of scope in working in deterministic models.
Practical implication
The proposed consumer preference-based demand estimation model can be beneficial to the apparel retailers in increasing their profit by reducing stock-out and overstocking situations. Though conjoint analysis is used in demand estimation in other industries, it is not used in apparel for demand estimations and can be greater use in its simplest form.
Originality/value
This research is the first one to model consumer preferences-based data to estimate demand in apparel. This research was practically tested in an apparel retail store. It is original.
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Madhur Bhatia and Rachita Gulati
The purpose of the paper is to explore the long-run impact of board governance and bank performance on executive remuneration. More specifically, the study addresses two…
Abstract
Purpose
The purpose of the paper is to explore the long-run impact of board governance and bank performance on executive remuneration. More specifically, the study addresses two objectives. First, the authors investigate the long-run relationship between pay and performance hold for the Indian banking industry. Second, the authors explore the moderating role of the board in explaining the relationship between executive pay and performance.
Design/methodology/approach
The study uses multivariate panel co-integration approaches, i.e. fully modified and dynamic ordinary least square, to explain the co-integrating relationship between executive pay, governance and performance of Indian banks. The analysis is conducted for the period from 2005 to 2018.
Findings
The results of co-integration tests reveal a long-run relationship between executive pay, board governance and bank performance. The long-run estimates produce evidence in favour of the dynamic agency theory, suggesting that the implications of asymmetric information can be mitigated by associating the current executive pay with the bank performance in the previous periods. The finding of this study reveals that improvements in the board quality serve as a monitoring tool to constrain excessive pay and moderate the executives’ pay. Furthermore, the interaction of performance and board governance negatively impacts pay, supporting a substitution approach. It implies that setting optimal pay packages for executives necessitates enhanced and efficient board governance practices.
Practical implications
The study recommends significant policy implications for regulators and the board of directors that executive pay significantly responds to the bank’s performance and good board governance practices in the long run.
Originality/value
This paper provides novel evidence of long-run pay-performance-governance relation using a panel co-integration approach.
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Saida Belhouchet and Jamel Chouaibi
This paper aims to shed light on the relationship between audit committee attributes and integrated reporting quality (IRQ).
Abstract
Purpose
This paper aims to shed light on the relationship between audit committee attributes and integrated reporting quality (IRQ).
Design/methodology/approach
Data on a sample of 360 European firms selected from the STOXX Europe 600 index between 2010 and 2021 were used to test the model based on multiple regression for panel data to analyze the effect of audit committee attributes on IRQ. This paper considers generalized least squares (GLS) estimation for panel data models.
Findings
The findings of this study confirm expectations concerning the impact of audit committee attributes on the IRQ. Indeed, audit committee independence and meetings have a significant positive impact on IRQ. However, no significant association is found between financial expertise and IRQ.
Practical implications
The findings of this paper have significant implications for policymakers, who, through proper legislation, should encourage the formation of larger audit committees and ones with a higher percentage of independent members. They should also establish a minimum number of audit committee meetings per year. These regulations, which aim to increase the efficacy of audit committees’ supervisory and monitoring tasks, would promote corporate transparency and improve IRQ.
Originality/value
This study supports the existing literature. First, it expands the scientific debate on IRQ. Second, unlike previous studies, which used more subjective methods to measure the degree of integrated reporting (IR), this study relied on the CGVS variable from the DataStream ASSET 4 Database. Third, the research is novel because it indicates the crucial role of internal assurance mechanisms in wide managerial reporting practices in European companies. The sample consisted of European firms only, whereas previous studies used a global sample. Finally, this study is based on recent data (2010–2021), while other studies covered the period between 2008 and 2013.
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Nafisa Ahmad and Md. Abul Kalam Azad
Besides the extensive research on managerial efficiency in the financial sector worldwide, emerging economies in Europe remain untapped. This research scrutinises the impact of…
Abstract
Purpose
Besides the extensive research on managerial efficiency in the financial sector worldwide, emerging economies in Europe remain untapped. This research scrutinises the impact of managerial performance and competitive structures on their financial industry growth in terms of services they offer and ability to liquefy stock in capital markets.
Design/methodology/approach
This study contains data from selected emerging European countries' during the period of 2010–2020. This study uses data from the Heritage Foundation's Index of Economic Freedom to control for firm-level indicators. The fixed-effects (FE) method was used to explore the nexus between financial sector growth and management performance as well as competitive firm structure.
Findings
The findings provide evidence of the existing impact of firm indicators on the financial sector's growth. Two-step system the generalized method of moments (GMM) estimations are used for the robustness check of the authors' model. Whilst on a scavenger hunt through existing literature, the authors realise that there is an overwhelming lack of enthusiasm in this field.
Originality/value
With the intention of better assessment, the authors use regulatory contextual variables to look for any possible impacts and surprisingly discover a pattern in the financial growth nexus.
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Toan Khanh Tran Pham and Quyen Hoang Thuy To Nguyen Le
The purpose of this study is to explore the relationship between government spending, public debt and the informal economy. In addition, this paper investigates the moderating…
Abstract
Purpose
The purpose of this study is to explore the relationship between government spending, public debt and the informal economy. In addition, this paper investigates the moderating role of public debt in government spending and the informal economy nexus.
Design/methodology/approach
By utilizing a data set spanning from 2000 to 2017 of 32 Asian economies, the study has employed the dynamic ordinary least squares (DOLS) and fully modified ordinary least squares (FMOLS). The study is also extended to consider the marginal effects of government spending on the informal economy at different degrees of public debt.
Findings
The results indicate that an increase in government spending and public debt leads to an expansion of the informal economy in the region. Interestingly, the positive effect of government spending on the informal economy will increase with a rise in public debt.
Originality/value
This study stresses the role of government spending and public debt on the informal economy in Asian nations. To the best of the authors' knowledge, this study pioneers to explore the moderating effect of public debt in the public spending-informal economy nexus.
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