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This study aims to identify and assess the key ethical challenges associated with integrating artificial intelligence (AI) in knowledge-sharing (KS) practices and their implications for decision-making (DM) processes within organisations.

The study employs a mixed-methods approach, beginning with a comprehensive literature review to extract background information on AI and KS and to identify potential ethical challenges. Subsequently, a confirmatory factor analysis (CFA) is conducted using data collected from individuals employed in business settings to validate the challenges identified in the literature and assess their impact on DM processes.

The findings reveal that challenges related to privacy and data protection, bias and fairness and transparency and explainability are particularly significant in DM. Moreover, challenges related to accountability and responsibility and the impact of AI on employment also show relatively high coefficients, highlighting their importance in the DM process. In contrast, challenges such as intellectual property and ownership, algorithmic manipulation and global governance and regulation are found to be less central to the DM process.

This research contributes to the ongoing discourse on the ethical challenges of AI in knowledge management (KM) and DM within organisations. By providing insights and recommendations for researchers, managers and policymakers, the study emphasises the need for a holistic and collaborative approach to harness the benefits of AI technologies whilst mitigating their associated risks.

This paper aims to investigate the non-overlapped winding dual stator electrically excited flux switching machine (NOW-DSEEFSM) for wind energy applications. To reduce the cost of conventional design, several rotor pole topologies of low cost have been examined, and finally, an optimum design is compared with conventional design.

In this paper, NOW-DSEEFSM is designed, optimized, evaluated and compared with different rotor poles. Initially, the electromagnetic performance of the proposed machine is investigated on the 8, 10, 14, 20 and 22 rotor poles based on the finite element analysis by using JMAG software. From the initial results, 8-, 10- and 14-pole machines are further selected for parametric optimization to enhance the electromagnetic performance. After optimization, the result indicates that the machine with 14 poles can be considered as the overall most appropriate design for the proposed wind generator. Furthermore, analysis has been used on different armature and field current densities to study the effect on electromagnetic performances. Finally, a comparison is performed between the proposed machine and the conventional machine.

In the proposed machine to reduce the copper losses and overhang effect, the NOW topology is used against the well-known overlap winding and provides excellent flux regulation capability due to the existence of field winding, and to obtain high torque and power densities, the presented machine has two stator units and a single robust rotor.

The dual stator flux switching machine with NOW is designed, evaluated and compared to get high torque and power densities.

The study aims to investigate the influence of critical socio-cultural factors common in developing countries on the ethical behaviour of professional accountants and the extent to which their self-control strengthens or weakens the relationships.

This paper relied on the quantitative approach to collect data from 366 professional accountants and used the partial least squares structural equation modelling technique to test hypotheses in the study.

This paper established that susceptibility to positive peer influence, materialism and political trust influence the ethical behaviour of professional accountants. Also, self-control moderates the relationship between susceptibility to positive peer influence and ethical behaviour of professional accountants. However, self-control does not moderate the relationships between materialism and ethical behaviour as well as political trust and ethical behaviour of professional accountants.

The regulatory institutions should review the existing ethical codes and standards to clarify the guidelines on conflicts of interest, financial temptations, materialistic influence and other related matters. Further, training programs must integrate self-control lessons. These programs should focus on enhancing self-control skills, particularly in situations where peer influence and political mistrust are entrenched.

This paper demonstrates the novelty in how critical socio-cultural factors could be contingent on individual circumstances and personal idiosyncrasies in influencing ethical behaviour.

The purpose of this study is to examine how fluid flow and heat transfer are affected by the influence of hybrid nanofluids flowing across a stagnation zone of a stretching curved surface. Stagnation point flow has garnered considerable attention over the past few decades. This is because many technical applications, such as the cooling of nuclear reactors and rotating equipment divisions, rely on stagnation-point flow.

A thorough analysis is conducted of the impacts of several regulating parameters on fluid flow and thermal performance, including the radiation parameter, heat source parameter, mixed convection parameter, porosity parameter curvature and nanoparticle concentration. The laws governing the field of flow equations are transformed by similarity substitutions into two nonlinear ordinary differential equations, which are then solved numerically using Maple. The MR-Solve technique in the built-in Maple package was used. The MR-Solve technique was used to numerically solve highly coupled ordinary differential equation problems. This approach produced highly precise and consistent results. It also provides the best performance while using a minimum amount of CPU and the shortest phrases.

The main conclusions of this study show that axial velocity drops, while radial velocity increases as the mixed convection parameter increases. The rate of heat transmission and skin friction is higher for hybrid nanoparticles with volume fraction percentile (0.01–0.03) than for those with volume fraction percentile (0.1–0.3).

Further research on this topic could examine a broader range of parameter values, suction/injection, entropy, mass equation, micropolar fluid, ternary hybrid nanofluid and Newtonian heating flow on a curved stretching surface.

By investigating a novel physical design that combines the various effect with stagnation flow, this study adds value and offers insights and prospective improvements in the discipline of heat fluid mechanics. Mathematical modeling or experimental studies in a variety of multiphysical contexts can be used to achieve this. Heat exchangers, crystalline procedures, microelectronic machines, systems for conserving energy, integrating operations, food manufacturing, climate control, purification and other engineering domains can all benefit from the geometric configurations investigated in this study. The results of this study greatly aid in optimizing thermal performance in a variety of application domains. This study is novel because it compares several volume fraction percentiles.

A stretching curved surface’s stagnation zone is traversed by hybrid nanofluids, offering insights into how curvature affects heat transfer and fluid flow efficiency. The results aid in the design and improvement of the energy transfer efficiencies for a range of commercial and biological purposes. The results offer possibilities for increased efficiency in a range of applications by developing hybrid nanofluid flow control methods and helping to create ideal thermal systems.

This paper aims to investigate the associations between organizational justice dimensions and employees' knowledge sharing (KS) while studying the mediating role of psychological empowerment (PE) in context of the Indian hospitality industry. It is also aimed to investigate the association between KS and innovative work behavior (IWB).

A mediation model was verified utilizing three-wave survey data from 293 employees working in hotels situated in northern India. Hypotheses were tested using AMOS and PROCESS Model 4.

There are significant associations between justice dimensions and KS, and PE mediates these relationships. Additionally, employees' KS has a positive effect on their IWB.

Organizations must promote justice and psychologically empower their employees to facilitate KS. Our study also highlights the significance of employees' KS in encouraging their IWBs. HR leaders and managers have an important role in facilitating the right work environment, in which employees experience fairness and empowerment.

This paper is the first to investigate linkages between justice dimensions, PE, KS and IWB in context of the Indian hospitality industry. Furthermore, this study has made the maiden attempt of asserting the mediating role of PE in the relationship between justice dimensions and KS.

With growing evidence of financial misconduct spreading through director networks, research on financial fraud contagion has garnered significant attention. This study incorporates the regulatory enforcement perspective into existing literature to examine how regulatory penalties mitigate financial fraud contagion within director networks.

This study uses a panel dataset of A-share listed Chinese firms covering 2007–2022. Based on the nature of the dataset, we construct ordinary least squares regression models with firm- and year-fixed effects. Data are collected from the China Stock Market and Accounting Research, Wind Information Co., Ltd and China Research Data Services. We use Python to scrape the coordinates of regulators and firms and retrieve travel distances from the Baidu Maps API.

This study verifies the existence of financial fraud contagion in director networks. Our findings indicate that regulatory penalties can mitigate the contagion between director-interlocked firms, improving accounting quality. Moreover, the mitigation effects are mediated by independent directors’ dissent and auditors’ efforts at director-interlocked firms and are more pronounced when these firms have superior network centrality and internal control quality.

This study enriches the literature on financial fraud contagion by examining director networks and regulatory penalties. We propose mediating effects of auditor effort and director dissents on the relationship between regulatory penalties and financial fraud contagion. Our findings provide insights for regulators to alleviate pressures and highlight the importance for directors to consider financial risks within their networks.

This paper aims to study the elasto-dynamic behavior of additively manufactured metallic lattice implants and compare them with human lower-body bone. This work is a step toward producing implants with high similarity of material properties to bone by developing a dynamic design approach.

A suitable topology was selected and admissible design space was established. Implants were fabricated by selective laser melting. Material dynamics, including elastic modulus, damping and natural frequency, were analyzed with experimental and finite element method methodology.

Generally, porosity improves dynamic properties up to an optimum point, which depends on printability, that is, ∼70%. Regarding elastic modulus and natural frequency, it is possible to achieve enough similarity with bone. But, considering damping, the similarity is <23% and <12% with dry and fresh bone, respectively. Damping and strain rate sensitivity increase with porosity. The natural frequency decreases with porosity. Bone ingrowth into lattice implants improves damping substantially while increasing elastic modulus.

Designers, dominantly had quasi-static approach, which considered only elastic modulus. But, the human body is a dynamic structure and experiences dynamic loads; meanwhile, bone, with its damping and natural frequency, regulates dynamic events like shock absorption and elastic wave filtering. Importantly, bone cells sense no load in quasi-static loading and must receive impact loads near their natural frequencies and special accelerations to conduct optimum mechanotransduction. So, it is necessary to develop a dynamic strategy which is comprehensive and describes bone duties.

This study aims to investigate whether public attention influences corporate decisions on environmental disclosure, thereby revealing how society perceives and understands environmental issues and how corporations respond to these expectations.

We selected publicly listed Chinese firms as our sample. An “Environmental Disclosure Greenwashing” (EDG) Index was developed through textual analysis of their annual reports using natural language processing. Financial data were obtained from the CSMAR database, and multivariate regression was used for analysis.

The impact of public attention on EDG primarily manifests as an oversight pressure effect rather than a legitimacy incentive effect. As public attention intensifies, firms tend to adopt more substantial environmental actions instead of merely symbolic environmental disclosures. Formal regulatory frameworks might inadvertently trigger corporate EDG, but public attention can correct the adverse effects possibly introduced by formal regulations. Notably, in firms facing lower institutional pressure, the influence of public attention is more pronounced.

The evidence suggests that public attention reduces corporate EDG. These findings have significant implications for the regulation of environmental disclosures among firms in emerging economies.

The study integrates research in environmental disclosure with the concept of “greenwashing”, unveiling the limitations of the “disclosure as governance” viewpoint. It elucidates the impact of an informal external oversight mechanism (i.e. public attention) on complex corporate environmental disclosure decisions.

Accurately predicting the installed capacity of wind energy is essential for energy strategic planning, given the growing need for environmental protection worldwide and the quick development of renewable energy. In order to provide an unprecedented high-precision scheme for wind energy installed capacity prediction and to further become the primary driving force in the process of energy planning and decision-making, this research focuses on overcoming the limitations of conventional prediction models and creatively proposes a multi-parameter collaborative optimization GM(1,1) power model. This will help the energy field advance in a more efficient and scientific direction.

The theoretical framework of the fundamental GM(1,1) power model is thoroughly examined in this study and serves as the basis for further optimizations. To unlock the potential of each parameter optimization, single-parameter optimization investigations of the model are conducted from the viewpoints of the fractional optimization, background value optimization and grey action optimization, respectively. Conversely, an inventive multi-parameter collaborative optimization power model is built. The model is given dynamic flexibility by adding time-varying parameters. The sine function and interpolation technique are used to further optimize the background value. The model’s meaning is enhanced by the inclusion of a power exponent. Furthermore, several parameters are cooperatively tuned with the aid of the sophisticated Firefly algorithm, giving the model stronger predictive powers. A multi-dimensional and multi-regional model comparison analysis is formed by selecting the wind energy installed capacity data of North America, Italy, Japan and South Korea for in-depth empirical analysis in order to confirm the model’s validity.

The findings show that the multi-parameter collaborative optimization model (Model 5) has an exceptional in-sample and out-of-sample prediction effect. The relative prediction error MAPEs are 0.41% and 0.31%. It has a clear advantage over the simple GM(1,1) power model and other single optimization models in applications in North America, South Korea, Japan, and Italy. Its seven variable parameters are the reason for this. These factors help create a very accurate prediction effect through joint optimization from multiple perspectives. It is noteworthy that Model 4’s nonlinear optimization of the grey action is impressive. It performs better than background value optimization and fractional-order optimization. Furthermore, according to the model’s prognosis, North America’s installed wind energy capacity is expected to develop linearly and reach 513.214 bn kilowatts in 2035. This gives the planning for energy development in this area a vital foundation.

The novel idea of the multi-parameter collaborative optimization GM(1,1) power model and its clever integration with the firefly method to accomplish parameter optimization constitute the fundamental value of this study. The substantial benefits of multi-parameter optimization in the stability of the prediction effect have been firmly validated by a thorough comparison with the basic and single-optimization models. Like a lighthouse, this novel model illuminates a more accurate path for wind energy installed capacity prediction and offers high-value reference bases for a variety of aspects, including government energy planning, enterprise strategic layout, investor decision-making direction, fostering technological innovation, advancing academic research and developing energy transformation strategies. As a result, it becomes a significant impetus for the growth of the energy sector.

• (1)

  This study proposes a new gray prediction model. Compared with the traditional grey prediction model, the modeling mechanism of this model is optimized.

• (2)

  This study is based on multi-parameter collaborative optimization to achieve the improvement of model prediction effect. The traditional grey model is two-parameter, while the model proposed in this study is seven-parameter collaborative optimization;

• (3)

  In this study, swarm intelligence algorithm-firefly algorithm is used to optimize the hyperparameters, so as to obtain the best cooperative optimization multi-parameter values;

• (4)

  The application of the model is divided into two parts: empirical and application. In the empirical stage, 5 kinds of prediction models are used to predict, which proves that the model proposed in this paper is effective and improves the prediction accuracy. The application part uses the model to forecast the installed wind power capacity in North America, and the future development trend is linear growth, which is expected to double the installed capacity by 2035.

This study proposes a new gray prediction model. Compared with the traditional grey prediction model, the modeling mechanism of this model is optimized.

This study is based on multi-parameter collaborative optimization to achieve the improvement of model prediction effect. The traditional grey model is two-parameter, while the model proposed in this study is seven-parameter collaborative optimization;

In this study, swarm intelligence algorithm-firefly algorithm is used to optimize the hyperparameters, so as to obtain the best cooperative optimization multi-parameter values;

The application of the model is divided into two parts: empirical and application. In the empirical stage, 5 kinds of prediction models are used to predict, which proves that the model proposed in this paper is effective and improves the prediction accuracy. The application part uses the model to forecast the installed wind power capacity in North America, and the future development trend is linear growth, which is expected to double the installed capacity by 2035.

Green innovation in human-centric smart manufacturing (HSM-GI) has emerged as a new paradigm in innovation management for Industry 5.0. The evaluation analysis method is crucial for measuring the development progress and guiding continual improvements of HSM-GI. Since this process of HSM-GI can be regarded as complex and interactive, a holistic picture is often required to describe the interrelations of its antecedents and consequences. In this respect, this study aims to construct a causality network indicator system and proposes a synergy evaluation method for HSM-GI.

Firstly, based on the Driver force-State-Response (DSR) causal-effect framework, this study constructs a holistic indicator system to analyze the interactions between environmental and human concerns of HSM-GI. Secondly, owing to the imprecision of human cognition and synergy interaction in the evaluation process, a flexible hesitant fuzzy (HF) superiority-inferiority synergetic evaluation method is presented. This method quantifies the strengths of causal relationships and expresses the incentives and constraints attitudes of humans. Finally, the proposed framework is applied to six HSMs in the electronic technology industry.

The driving force and state of the HSM-GI system exhibit an upward trend, while the response continues to decline due to changing market demands. The order and synergy degree have shown an increasing trend during 2021–2023, particularly significant for BOE and Haier Smart Home. HSM-GI systems with higher scores mostly have functional coordination and a coherent synergy structure.

This study demonstrates the proposed approach’s applicability and assists policymakers in formulating targeted strategies for green innovation systems.