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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.

This paper investigates the effect of financial development on bank profitability. The authors examine whether financial development is an important determinant of bank profitability.

The ordinary least square and the generalized method of moments regression methods were used to analyze the impact of financial development on the profitability of the Nigerian banking sector.

The authors find a significant negative relationship between the financial system deposits to GDP ratio and the non-interest income of Nigerian banks. This indicates that higher financial system deposits to GDP depresses the non-interest income of Nigerian banks. The result implies that the larger the size of the Nigerian financial system, the lower the profitability of banks in Nigeria. Also, the authors observe that bank concentration, nonperforming loans, cost efficiency and the level of inflation are significant determinants of the profitability of Nigerian banks.

It is recommended that regulators should establish market-enabling policies that encourage new banks to emerge in the banking industry. The entry of new banks can lead to increase in financial system deposits and credit supply for economic growth. Regulators also need to understand the role of Nigerian banks in promoting financial development and find ways to collaborate with banks towards financial sector development. Another implication of the findings for asset managers is that asset managers will need to take into account the prevailing level of financial development, particularly the size of the financial system, in their asset pricing and investment decisions. This will ensure that investors get value for their investments in Nigeria. The financial implication of the study is that the level of financial development in Nigeria can improve the finance-growth linkages in Nigeria through the efficient allocation of credit and capital to crucial sectors of the Nigerian economy to spur growth in those sectors.

Evidence dealing with how financial development affects the profitability of the banking sector in African countries is scarce in the literature, and is completely absent for Nigeria. This paper addresses this research gap.

The purpose of this paper is to test if building reputation capital through environmental, social and governance (ESG) investing can mitigate the negative effect of economic policy uncertainty (EPU) on firms’ valuation.

This study uses an unbalanced panel of 591 financial firms between 2005 and 2021 from Canada, France, Germany, Italy, Japan, the United Kingdom (UK) and the USA. Ordinary least square method is used in the empirical tests. To alleviate a potential endogeneity problem, robustness tests are performed using the two-stage least square approach with instrumental variables.

The results of this paper show that sustainable reporting can offset the negative effect of EPU on the valuation of financial firms. Consistent with the stakeholder-based reputation-building hypothesis, sustainability performance may have an insurance-like impact on firms’ valuation during periods of high uncertainty.

According to the findings, during high policy uncertainty periods, investors accept to pay a premium for the stocks of the firms which built social capital through environmental and social investments. Accordingly, it is suggested that regulatory bodies and governments motivate firms to increase their stakeholder orientation to attain higher reputation capital.

Managers can mitigate the negative impact of policy uncertainty on the value of their firms via building social capital, which will increase financial market stability in return, and portfolio investors may use such firms for portfolio optimization decisions.

To the best of the authors’ knowledge, this paper is one of the first to examine the mitigating role of ESG investing on EPU and firm valuation relationships for financial firms. Thus, this study provides new insights related to the impact of ESG performance on valuation during uncertain times.

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.

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.

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.

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.

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.

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.

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.

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.

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.

This analysis examines the relationship between corruption and firm productivity in Vietnam.

The authors apply the system generalized method of moments estimation approach on a panel dataset constructed from comprehensive enterprise surveys covering all the sectors over the 2011–2020 period.

The results confirm a non-linear relationship between corruption and firm productivity. Where corruption is severe, leaving corruption alone tends to benefit firm productivity because efforts to control corruption are likely to cause greater delays. In less corrupt provinces, corruption appears to harm firm productivity while efforts to control corruption provide significant productivity gains. This U-shaped relationship is confirmed for small firms and those in the private sector sub-samples. Intriguingly, this study reveals that the U-shaped relationship does not apply to micro, medium, large firms, state-owned firms and foreign-invested firms because corruption is found to have no significant impact on productivity among these sub-samples. Changes in regulations after 2014 toward promoting a transparent business environment are shown to foster the positive impact of lowering corruption on firm productivity.

This study suggests that lowering corruption is beneficial for firm productivity at the micro level. However, where corruption is severe, monitoring corruption alone is likely to cause adverse effects on productivity due to increased bureaucratic delays. Institutional reforms might play an important role in leveraging the effects of lowering corruption on productivity in highly corrupt areas.

This paper sheds new light on the relationship between corruption and firm productivity in the broad existing literature and especially in the limited number of studies for Vietnam.

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 χ2 distribution, the likelihood function can be complicated or untractable. This chapter introduces using indirect inference to estimate the SF models, where only least squares estimation is used. There is no need to derive the density or likelihood function, thus it is easier to handle a model with complicated distributions in practice. The author examines the finite sample performance of the proposed estimator and also compare it with the standard ML estimator as well as the maximum simulated likelihood (MSL) estimator using Monte Carlo simulations. The author found that the indirect inference estimator performs quite well in finite samples.

This paper aims to investigate the determinants of global interest in central bank digital currency (CBDC). It assessed whether global interest in sustainable development and cryptocurrency are determinants of global interest in CBDC.

Google Trends data were analyzed using two-stage least square regression estimation.

There is a significant positive relationship between global interest in sustainable development and global interest in CBDC. There is a significant positive relationship between global interest in cryptocurrency and global interest in the Nigeria eNaira CBDC. There is a significant negative relationship between global interest in CBDC and global interest in the eNaira CBDC. There is a significant positive relationship between global interest in CBDC and global interest in the China eCNY. There is a significant negative relationship between global interest in cryptocurrency and global interest in the Sand Dollar and DCash.

The literature has not empirically examined whether global interest in sustainable development and cryptocurrency are factors motivating global interest in CBDC. This study fills a gap in the literature by investigating whether global interest in sustainable development and cryptocurrency are factors motivating global interest in CBDC.

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.

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.

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.

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.

This study was carried out to analyze the importance of consumer preference data in forecasting demand in apparel retailing.

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.

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.

There has been a considerable amount of work done in choice-based models. There is a lot of scope in working in deterministic models.

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.

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.