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The global economy has been affected by the COVID-19 pandemic, which has placed greater responsibility on companies to fulfill their obligations to Corporate Social Responsibility (CSR) amid the crisis. This chapter investigates the role of a Chief Executive Officer (CEO) attributes in improving a firm's CSR in the emerging economy of Jordan and how the COVID-19 pandemic modifies this relationship. Using a Jordanian sample of 655 firm-year observations during the 2014–2021 period, the research results show that older CEOs, well-educated CEOs, CEOs' remuneration, and CEOs' ownership positively correlate with CSR reporting. However, long-tenured CEOs are associated with lower CSR initiatives. The subsample analysis findings also validate the significance of CEO attributes in improving CSR practice during the COVID-19 pandemic compared to the prepandemic period. These findings are beneficial for the regulatory setters to understand better whether CEO attributes are linked to engagement in CSR-related information. This research is among the limited number of studies that have explored how CEO attributes impact CSR reporting for the stakeholder's welfare. Moreover, it uniquely concentrated on contrasting the findings before and during the COVID-19 pandemic.

Although extant studies have investigated the antecedents of negative electronic word of mouth (eWOM), they treated it as a unidimensional concept without classification. To bridge this knowledge gap, this paper distinguishes rational negative eWOM (RNW) from emotional negative eWOM (ENW) and leverages the consumer value framework to investigate their drivers in the context of peer-to-peer accommodation platforms (PPAPs).

This study collected data through an online survey of 437 PPAP users. Partial least squares (PLS) were used to validate the proposed hypotheses. Further, the path coefficients comparison method was adopted to distinguish the different impacts of consumer values on RNW and ENW.

This research showed that self-presentation exerted a positive impact on RNW, but its relationship with ENW was insignificant. Anger and regret were, respectively, positively related to ENW and RNW. Besides, altruism exerted a positive effect on RNW, whereas it had a negative effect on ENW.

First, this paper makes a fresh attempt to categorize negative eWOM into RNW and ENW. Second, this paper draws upon the consumer value framework to dissect varied motivations for posting RNW versus ENW on PPAPs. Third, this paper empirically verifies the differential influences that consumer values exert on RNW and ENW.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.

This study proposes a predictive neural network model reference decoupling control method for the coupling problem between the leg joints of hydraulic quadruped robots, and verifies its decoupling effect..

The machine–hydraulic cross-linking coupling is studied as the coupling behavior of the hydraulically driven quadruped robot, and the mechanical dynamics coupling force of the robot system is controlled as the disturbance force of the hydraulic system through the Jacobian matrix transformation. According to the principle of multivariable decoupling, a prediction-based neural network model reference decoupling control method is proposed; each module of the control algorithm is designed one by one, and the stability of the system is analyzed by the Lyapunov stability theorem.

The simulation and experimental research on the robot joint decoupling control method is carried out, and the prediction-based neural network model reference decoupling control method is compared with the decoupling control method without any decoupling control method. The results show that taking the coupling effect experiment between the hip joint and knee joint as an example, after using the predictive neural network model reference decoupling control method, the phase lag of the hip joint response line was reduced from 20.3° to 14.8°, the amplitude attenuation was reduced from 1.82% to 0.21%, the maximum error of the knee joint coupling line was reduced from 0.67 mm to 0.16 mm and the coupling effect between the hip joint and knee joint was reduced from 1.9% to 0.48%, achieving good decoupling.

The prediction-based neural network model reference decoupling control method proposed in this paper can use the neural network model to predict the next output of the system according to the input and output. Finally, the weights of the neural network are corrected online according to the predicted output and the given reference output, so that the optimization index of the neural network decoupling controller is extremely small, and the purpose of decoupling control is achieved.

This study examines the impact of China’s mixed-ownership reform on the innovation of non-state-owned acquirers, with a particular focus on the impact on firms with high financing constraints, low-quality accounting information or less tangible assets.

We use a proprietary dataset of firms listed on the Shanghai and Shenzhen Stock Exchanges to investigate the impact of mixed ownership reform on non-state-owned enterprise (non-SOE) innovation. We employ regression analysis to examine the association between mixed ownership reform and firm innovation.

The study finds that non-state-owned firms can improve innovation by acquiring equity in state-owned enterprises (SOEs) under the reform. Eased financing constraints, lowered financing costs, better access to tax incentives or government subsidies, lowered agency costs, better accounting information quality and more credit loans are underlying the impact. Additionally, cross-ownership connections amongst non-SOE executives and government intervention strengthen the impact, whilst regional marketisation weakens it.

This study adds to the literature on the association between mixed ownership reform and firm innovation by focussing on the conditions under which this impact is stronger. It also sheds light on the policy implications for SOE reforms in emerging economies.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

This study aims to investigate the relationship between strategy intent (product-service innovation intention) and outcome (product-service innovation outcome), and the role that external sources of innovation play in influencing this relationship.

Using data obtained from the community innovation survey, we apply a logit regression to a sample of 1,419 Portuguese firms. By examining the moderating effect of open innovation breadth, we assess how the relationship between differentiation intent and outcome is contingent upon the involvement of external stakeholders.

Our findings reveal that the relationship between differentiation intent and outcome is contingent upon the moderating effect of open innovation breadth. Our analysis suggests that the negative influence of different sources of innovation can be addressed by adopting a paradox lens.

This research provides valuable insights for managers. By simultaneously pursuing a differentiation strategy and engaging in collaboration with external sources, firms may compromise their ability to effectively differentiate their offer. Managers should consider the potential tensions arising from internal and external stakeholder relationships to optimize their innovation strategies.

This study contributes to the existing literature by shedding light on the role of external innovation sources in influencing the relationship between differentiation intent and outcome and the importance that information systems may have in this relationship. By exploring the moderating effect of open innovation breadth, we provide a nuanced understanding of how firms can navigate organizational tensions and leverage innovation for competitive advantage.

Green practices are essential for sustainability. However, it is challenging due to the socioeconomic and environmental concerns. Similarly, after the induced SDG-12 and SDG-13 by United Nations, the pressure groups forced manufacturers to consider sustainability. Therefore, this research aims to examine the sustainability through multifaceted green functions in manufacturing is examined.

Data were collected from 293 supply chain professionals of manufacturers from a developing economy. Hypotheses were tested through a quantitative method using partial least squares-structural equation modeling with the help of SmartPLS version 4 to validate the measurement model.

The findings revealed that all six direct hypotheses were supported. However, out of four hypotheses of mediation, one was not supported. Besides, a sequential mediation of green supply chain environmental cooperation and green human resource management was supported. The findings illustrated that green supply chain practices positively influence all used variables.

This research provides practical insight to practitioners to implement green practices in their supply chain networks for social, economic and environmental sustainability and compliance with SDG-12 and SDG-13. The sustainability was validated in a higher-order construct (HOC) (formative), including sequential mediation in the model with the support of resource dependency theory. Therefore, this study adds substantial literature to the existing body of knowledge.

This research provides an interdisciplinary framework by adding knowledge to the Resource Dependency Theory to address Sustainable Development Goals-12 (SDGs) and SDG-13. Likewise, this research provides an extension towards the body of knowledge on the issue, which can be used in future research and critical examinations for cleaner and sustainable production. So far, in Pakistan, no research has looked at the function of these integrated variables in the manufacturing industry with a diligent focus on sustainability as it was validated in a higher-order construct (formative) with one sequential mediation, which makes this research unique.