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This paper aims to devise a first-of-its-kind methodology to determine the design, operating conditions and actuation strategy of pneumatic artificial muscles (PAMs) for assistive robotic applications. This requires extensive characterization, data set generation and meaningful modelling between PAM characteristics and design variables. Such a characterization should cover a wide range of design and operation parameters. This is a stepping stone towards generating a design guide for this highly popular compliant actuator, just like any conventional element of a mechanism.

Characterization of a large pool of custom fabricated PAMs of varying designs is performed to determine their static and dynamic behaviours. Metaheuristic optimizer-based artificial neural network (ANN) structures are used to determine eight different models representing PAM behaviour. The assistance of knee flexion during level walking is targeted for evaluating the applicability of the developed actuator by attaching a PAM across the joint. Accordingly, the PAM design and the actuation strategy are optimized through a tabletop emulator.

The dependence of passive length, static contraction, dynamic step response for inflation and deflation of the PAMs on their design dimensions and operating parameters is successfully modelled by the ANNs. The efficacy of these models is investigated to successfully optimize the PAM design, operation parameters and actuation strategy for using a PAM in assisting knee flexion in human gait.

Characterization of static and the dynamic behaviour of a large pool of PAMs with varying designs over a wide range of operating conditions is the novel feature in this article. A lucid customizable fabrication technique is discussed to obtain a wide variety of PAM designs. Metaheuristic-based ANNs are used for tackling high non-linearity in data while modelling the PAM behaviour. An innovative tabletop emulator is used for investigating the utility of the models in the possible application of PAMs in assistive robotics.

The purpose of this paper is to examine relationship between corporate governance (CG) and firm performance for a set of 255 foreign-funded firms in the form of foreign direct investment (FDI) and private equity (PE). The authors employ a wide range of CG measures including board size, meetings, board gender and foreign ownership which are used as the proxy of globalisation and control variables like firm age, leverage, firm size and capital expenditure to arrive at a conclusion.

Panel data set of 255 (187 companies funded by foreign capital in the form of FDI, and 68 companies having foreign capital in the form PE) companies listed on Bombay Stock Exchange, for the period of eight years (2008–2015) are analysed by using static (fixed and random effects) and dynamic (generalised method of moments (GMM)) panel data specifications to examine the relationship among CG, globalisation and firm performance.

The empirical results of static model indicate the relationship between CG and performance of foreign firms, which are not very strong in India. This is due to the fact that most of the firms are not following the guidelines and regulations strictly in the initial period of sample years. Diversity in board is found as an important variable in accessing firm performance. And the authors also found that foreign firms are very particular about the implementation of CG norms. The results of GMM model highlight the interaction term of foreign ownership with governance indicators. CG is having a positive and significant impact over performance, inferring that higher foreign ownership (in the form of FDI and PE) in firm leading to positive effect on profitability.

The investor’s preference of financing a unit is guided by the performance of a firm. Investors are more inclined towards high-performing firms, and hence higher profitability leads to higher inflow of capital. The result indicates that higher accounting and market performance may be achieved by good governance practices, in turn, leading to reduced agency costs. Countries with high governance scores attract more of foreign capital. Similar to the best governed countries, the companies having good governance practices attract more foreign inflows in the form of capital.

While previous literature considered a single measurement framework in the form of a CG index, the authors tried to incorporate a range of CG indicators to study the effect of globalisation and CG on firm performance. The authors segregated foreign-owned funds into two parts, especially FDI and PE. This paper examined heterogeneity in the form of FDI-funded and PE-funded firms, as no prior literature is available which has evaluated different sets of foreign funds simultaneously on CG.

This study investigates organizations' non-adoption intention towards the enterprise metaverse. The innovation resistance theory (IRT) is used as an underpinning theory to examine the impact of various risks on non-adoption intention towards the enterprise metaverse.

A total of 294 responses were collected to examine the proposed hypotheses. A structural equation modelling technique was used to investigate the hypotheses using SPSS AMOS and PROCESS MACRO.

The results of this study reveal that performance, security and psychological risks are significantly associated with non-adoption intention towards enterprise metaverse. Further, distrust significantly meditates the association between performance risk, social risk, technological dependence risk, security risk and psychological risk and non-adoption intention towards enterprise metaverse. Moreover, the results of moderated-mediation hypotheses indicate that the mediating effect of distrust on the association among performance risk, social risk, psychological risk and non-adoption intention towards enterprise metaverse is higher for individuals having high technostress compared to individuals having low technostress.

The study's findings will enrich the metaverse literature. Further, it provides a deeper understanding of enterprise metaverse adoption from a B2B perspective using the underpinnings of IRT. The study helps organizations understand the risks associated with the adoption of the enterprise metaverse.

Climate change-related weather events significantly affect rice production. In this paper, we investigate the impact of and interrelationships between agriculture inputs, climate change factors and financial variables on rice production in India from 1970–2021.

This study is based on the time series analysis; the unit root test has been employed to unveil the integration order. Further, the study used various econometric techniques, including vector autoregression estimates (VAR), cointegration test, autoregressive distributed lag (ARDL) model and diagnostic test for ARDL, fully modified least squares (FMOLS), canonical cointegrating regression (CCR), impulse response functions (IRF) and the variance decomposition method (VDM) to validate the long- and short-term impacts of climate change on rice production in India of the scrutinized variables.

The study's findings revealed that the rice area, precipitation and maximum temperature have a significant and positive impact on rice production in the short run. In the long run, rice area (ß = 1.162), pesticide consumption (ß = 0.089) and domestic credit to private sector (ß = 0.068) have a positive and significant impact on rice production. The results show that minimum temperature and direct institutional credit for agriculture have a significant but negative impact on rice production in the short run. Minimum temperature, pesticide consumption, domestic credit to the private sector and direct institutional credit for agriculture have a negative and significant impact on rice production in the long run.

The present study makes valuable and original contributions to the literature by examining the short- and long-term impacts of climate change on rice production in India over 1970–2021. To the best of the authors’ knowledge, The majority of the studies examined the impact of climate change on rice production with the consideration of only “mean temperature” as one of the climatic variables, while in the present study, the authors have considered both minimum as well as maximum temperature. Furthermore, the authors also considered the financial variables in the model.

The purpose of this study is to look at how Shariah-compliant status and Shariah regulation affect the demand for initial public offerings (IPOs) in Pakistan. The Shariah-compliant status, which is seen as a method that offers a credible signal to investors, may explain the anomaly in IPO demand.

This research used multivariate and quantile regression models to assess data from 85 IPOs issued on the Pakistan Stock Exchange between 2000 and 2019.

Shariah-compliant status has a considerable negative association with IPO demand. Nevertheless, there is a considerable positive association among Shariah regulation and IPO demand. Furthermore, the interaction among regulatory quality and Shariah-compliant status has a considerable strong influence on IPO demand. As a consequence, the findings show that Shariah-compliant firms might possibly attract the attention of investors. Investors were found to concur on the amicability of rigorous rules and permissible Shariah-compliance aspects.

Future studies could analyse the financial ratio benchmark (cash and debt) to determine the Shariah-compliant status and Shariah regulation to better understand the problem of IPO demand in the context of Pakistan.

The outcomes of this research are useful for issuers and underwriters in comprehending the characteristics that influence high and early IPO success. Such knowledge may assist issuers and underwriters in responsibly planning and managing the IPO process.

The results may be useful to investors looking for critical information in prospectuses to make the best choice when subscribing to IPOs in Pakistan.

This is one of the first studies to provide empirical data on the links among Shariah-compliant status, Shariah regulation and IPO demand in Pakistan. Furthermore, this research demonstrates the interaction impact of regulatory quality and Shariah-compliant status on IPO demand.

With the emergence of COVID-19 and increased infections, organizations urged their employees to work from home. Furthermore, with the on-going pandemic, employees take measures to ensure individual safety and their families. Hence, work from home culture can result in long-term employee satisfaction. However, no study addresses the development of work from the home culture in an integrated framework. Therefore, the current research explores the role of safety during the pandemic, organizational commitment and employee motivation on work from home culture, which may influence employee satisfaction. Furthermore, job demands and home demands were also evaluated for employee satisfaction.

The study used existing scales of the factors to develop the measures and collect perceptual responses from employees working from home, supported with a pre-test. The study executed a survey with effective responses from 132 individuals spread across different sectors to validate the hypotheses. The responses were analysed using partial least squares in ADANCO 2.2.

Findings suggest safety concerns along with organization commitment enhances work from home culture. Such work from home culture enhances employee motivation and employee satisfaction. Furthermore, job demands and home demands also influence employee satisfaction.

To the best of the authors knowledge, the study is the foremost to develop an integrated empirical framework for work from home culture and its antecedents and consequences. The study has several important implications for managers.

The COVID-19 pandemic, starting in early 2020, has significantly compromised global commitment to the 2030 Agenda for Sustainable Development Goals, notably affecting areas like food security (SDG 2) and the economy (SDG 8). Informal economy platform employees have been among the most impacted. In India alone, 7.7 million workers in the informal economy have suffered, with nearly 90% of unskilled and semi-skilled workers experiencing income loss. The widespread income loss among a significant portion of the workforce has led to disruptions in demand and supply mechanisms, thereby worsening food insecurity. This study investigates the determinants of the food consumption score (FCS) to serve as an indicator of food security within informal-economy households. A longitudinal survey of 2,830 unskilled and semi-skilled employees, including drivers, domestic workers, delivery personnel, beauticians, street vendors, small business owners, and self-employed individuals, was conducted. The findings show a significant shift towards borderline household FCS during the pandemic, with a sharp decline in daily consumption of dairy products and non-vegetarian items, indicating reduced protein intake. Consuming two or fewer meals per day increases the likelihood of poor FCS, highlighting the need for systematic interventions to ensure three regular meals per day. Moreover, insufficient government support for adequate food intake in informal economy households calls for redesigned assistance programs. Policymakers should prioritize practical solutions, such as community-based food distribution centers and mobile food vans, to ensure the delivery of nutritious food to vulnerable populations in Bangalore.

Bank creditworthiness refers to the evaluation of a bank’s ability to meet its financial obligations. It is an assessment of the bank’s financial health, stability and capacity to manage risks. This paper aims to investigate the credit rating patterns that are crucial for assessing creditworthiness of the Islamic banks, thereby evaluating the stability of their industry.

Three distinct machine learning algorithms are exploited and evaluated for the desired objective. This research initially uses the decision tree machine learning algorithm as a base learner conducting an in-depth comparison with the ensemble decision tree and Random Forest. Subsequently, the Apriori algorithm is deployed to uncover the most significant attributes impacting a bank’s credit rating. To appraise the previously elucidated models, a ten-fold cross-validation method is applied. This method involves segmenting the data sets into ten folds, with nine used for training and one for testing alternatively ten times changeable. This approach aims to mitigate any potential biases that could arise during the learning and training phases. Following this process, the accuracy is assessed and depicted in a confusion matrix as outlined in the methodology section.

The findings of this investigation reveal that the Random Forest machine learning algorithm superperforms others, achieving an impressive 90.5% accuracy in predicting credit ratings. Notably, our research sheds light on the significance of the loan-to-deposit ratio as a primary attribute affecting credit rating predictions. Moreover, this study uncovers additional pivotal banking features that intensely impact the measurements under study. This paper’s findings provide evidence that the loan-to-deposit ratio looks to be the purest bank attribute that affects credit rating prediction. In addition, deposit-to-assets ratio and profit sharing investment account ratio criteria are found to be effective in credit rating prediction and the ownership structure criterion came to be viewed as one of the essential bank attributes in credit rating prediction.

These findings contribute significant evidence to the understanding of attributes that strongly influence credit rating predictions within the banking sector. This study uniquely contributes by uncovering patterns that have not been previously documented in the literature, broadening our understanding in this field.

This study aims to uncover the determinants of Islamic financial inclusion in Indonesia.

This study uses the analytic network process (ANP) to gather expert opinions and responses from academics, regulators and practitioners.

The ANP analysis discovered that the level of Islamic financial inclusion in Indonesia is influenced by two main drivers: the supply and the demand. The demand factors for Islamic financial inclusion, ranked based on their level of significance, are as follows: financial literacy (0.27), religious commitment (0.22), socioeconomic factor (0.19) and social influence (0.17), respectively. From the supply side, primary catalysts for Islamic financial inclusion based on their level of importance are human capital (0.32), product and services (0.24), infrastructure (0.18) and policies and regulation (0.17), respectively.

The present study does not include the Islamic insurance sector in its determinant framework of Islamic financial inclusion in Indonesia.

This study serves as a reference for regulators in formulating appropriate policy strategies to strengthen the Islamic financial inclusion in Indonesia.

This study is a pioneer attempt to identify distinctive factors that influence the level of Islamic financial inclusion in Indonesia by analyzing expert opinions from diverse groups of Islamic finance stakeholders.

Based on the unfolding theory of voluntary turnover, the purpose of this paper is to investigate the linkage between abusive supervision (a shock) and subordinate’s intention to quit (withdrawal cognition). The study also explores the multi-mediation routes by testing the abusive supervision-intention to quit relationship via psychological contract breach and via burnout.

To test the proposed hypotheses, the study draws cross-sectional data from Indian employees working in various MNCs in the country. Data were collected using an electronic data collection method. The online form link was send to 600 employees, out of which 246 valid and complete responses were received (n=246). Partial least square (PLS–SEM) was used for the analysis.

Results showed that abusive supervision is positively related to intention to quit. Similarly, psychological contract breach and burnout partially mediates the abusive supervision-intention to quit linkage.

First, the current study has conceptualized and tested abusive supervision as a shock that triggers various adverse cognitions including withdrawal cognition (intention to quit). Second, the study also empirically investigated multi-mediational routes via psychological contract breach and burnout that explained the indirect effect between abusive supervision and intention to quit.