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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 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 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.

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.

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.

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.

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.

This paper provides novel evidence of long-run pay-performance-governance relation using a panel co-integration approach.

This study aims to investigate the implications of natural gas rents and institutions as co-drivers of economic growth, focusing on the Gas Exporting Countries Forum (GECF) with panel data between 2001 and 2021.

This research paper uses a specialised two stage estimator, the panel instrumental variable technique (panel IV), which takes care of the potential endogeneity issues in the model.

The findings show that natural gas rent significantly impacts the economic growth of the GECF. On average, natural gas rent increases the sample’s growth rate by about 2.634% percentage points in the short run. The result indicates that the qualities of institutions (political and economic) have a significant positive long-term effect on the economies of the GECF. In addition, the study’s energy price volatility positively correlates with the countries’ growth.

There might be a need to investigate the effects of natural gas rents and institutions as co-growth drivers in each country within the GECF. The likelihood exists that the impact of natural gas rents and institutions on economic growth at the country’s level may differ from the outcome of such an experiment on the group level. Because of space and time limitations, this study could not carry out the specific country’s investigation of natural gas rents and institutions as a co-growth driver. That limitation may constitute further study to advance this study to a new height.

With good institutions, natural gas rent is likely to be an alternative growth driver for some economies that rely on fossil fuels like oil as a growth driver. By extension, the GECF has the potential to rival Organisation of Petroleum Exporting Countries (OPEC) in the global energy market, particularly in achieving Sustainable Development Goal number seven. In essence, evidence in this study suggests that natural gas rent has long-term positive effects on the growth of the GECF, conditioned on good institutions. Moreover, the drive of global energy consumption towards sustainable energy usage is an economic blessing for the GECF. By extension, the demand for natural gas would continue to rise, creating opportunities to improve natural gas rents. By implication, the GECF would continue to benefit from the pursuit of sustainability as the world shifts towards energy consumption with less CO₂.

Firstly, this study models the qualities of institutions for the GECF. Secondly, to the best of the author’s knowledge, this study is the first attempt to examine natural gas rents and the qualities of institutions as co-determinants of economic growth among the GECF (a potential cartel).

The main purpose of the study is to investigate the impact of gender diversity both at the board and workforce level on firm performance (FP) in the Indian context.

This study is based on annual data of 200 companies listed on Bombay Stock Exchange (BSE) for the period 2012–2019. The authors have used the fixed-effects (FE) regression and system generalized method of moments to estimate the impact of board gender diversity and workforce gender diversity (WGD) on FP. The authors have used Blau's Index (BI) and Shannon's Index (SI) to measure gender diversity. Further, the authors have used return on assets and Tobin's Q (TBQ) to measure FP.

The authors' panel regression results suggest that board gender diversity and WGD have a positive and statistically significant impact on FP. The authors' findings are robust across different methods of estimation and alternative measures of FP.

This paper examines the impact of gender diversity both at the board and workforce level on FP of 200 companies listed on BSE. The authors' study contributes to the literature that is sparse in the Indian context and provides new insights on the impact of board and WGD on FP. The findings have useful policy implications. To achieve better performance, it is imperative to appreciate gender diversity at the governance and workforce level in a fast-growing economy like India.

Research on the organizational ramifications of chief executive officer (CEO) greed remains scarce. This study intends to fill this gap by examining the impact of CEO greed on an important yet risky corporate strategy, corporate tax avoidance (CTA). Drawing on upper echelons theory, the authors argue that greedier CEOs tend to engage in more CTA. The relationship is weaker when CEOs experienced economic recessions in their early career and stronger when CEOs are endowed with equity ownership of their respective firms.

The authors test the hypotheses with data from US public firms from 1997 to 2008 and employ the ordinary least square regression analysis to analyze the hypothesized relationships. The authors also test the robustness of the results by performing the two-stage least square regression and propensity score matching analyses.

The findings lend broad support to all the hypotheses. The authors find that greedier CEOs tend to engage in more CTA by paying lower corporate taxes. The impact of greed on CTA is attenuated when CEOs are recession CEOs and is exacerbated when CEOs own large numbers of firm shares.

This paper contributes to the upper echelons research by investigating a novel executive personal characteristic, greed, and its negative impact on an important organizational outcome. This paper also contributes to the growing tax research that recognizes the important role executives play in shaping corporate tax strategies.

The paper empirically investigates the link between banking market structure and funding liquidity risk.

With a panel of Vietnamese commercial banks from 2007 to 2021, the system generalized method of moments (GMM) estimator is applied as the primary regression method, while the random-effect model and the corrected least square dummy variable (LSDVC) technique are also considered in robustness checks.

Competition may increase banks' funding liquidity risk. This finding holds for competition measures derived from the Boone index and concentration ratios but not in the case of the Lerner index as a proxy for market power. Further results indicate that the funding liquidity risk of banks that are larger and have better performance (less credit risk and higher return) tends to be less affected by competition. Besides, the overall impact of bank competition on funding liquidity risk is amplified by the financial crisis and the COVID-19 pandemic.

The study extends the empirical literature by exploring the relationship between bank competition and funding liquidity risk. Additionally, the paper also studies how the impact of bank competition on funding liquidity risk depends on the characteristics of the banking sector and the macroeconomic conditions of the economy, including the moderating effect of the COVID-19 pandemic.