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This study explores the impact of metaverse knowledge on freelancer engagement and performance within the gig economy, drawing upon the theoretical framework of social cognitive theory. The authors investigate the mediating role of freelancer engagement in the relationship between metaverse knowledge and performance, further examining the moderating influence of freelancer experience on these relationships.

Using a convenient sampling technique, data was collected through questionnaire from 301 freelancers working on various virtual platforms in Pakistan using a five-point Likert scale. Smart PLS 4.0 was used to analyze the data.

The findings reveal positive direct effect of metaverse knowledge on both freelancer engagement and performance. In addition, freelancer engagement significantly mediates the relationship between metaverse knowledge and performance. Furthermore, the findings affirm that the freelancers experience serves as a moderating factor in the relationship between metaverse knowledge, engagement and performance by indicating positive impact.

This study contributes a novel perspective to the gig economy literature by elucidating the underlying mechanisms through which metaverse knowledge drives freelancer performance via engagement. By examining the unique role of the metaverse in the gig context, the study offers valuable theoretical and practical implications for both scholars and practitioners seeking to understand and enhance freelancer engagement and performance in this evolving digital landscape.

This study aims to offer a hybrid stand-alone system for electric vehicle (EV) charging stations (CS), an emerging power scheme due to the availability of renewable and environment-friendly energy sources. This paper presents the analysis of a photovoltaic (PV) with an adaptive neuro-fuzzy inference system (ANFIS) algorithm, solid oxide fuel cell (SOFC) and a battery storage scheme incorporated for EV CS in a stand-alone mode. In previous studies, either the hydrogen fuel of SOFC or the irradiance is controlled using artificial neural network. These parameters are not controlled simultaneously using an ANFIS-based approach. The ANFIS-based stand-alone hybrid system controlling both the fuel flow of SOFC and the irradiance of PV is discussed in this paper.

The ANFIS algorithm provides an efficient estimation of maximum power (MP) to the nonlinear voltage–current characteristics of a PV, integrated with a direct current–direct current (DC–DC) converter to boost output voltage up to 400 V. The issue of fuel starvation in SOFC due to load transients is also mitigated using an ANFIS-based fuel flow regulator, which robustly provides fuel, i.e. hydrogen per necessity. Furthermore, to ensure uninterrupted power to the CS, PV is integrated with a SOFC array, and a battery storage bank is used as a backup in the current scenario. A power management system efficiently shares power among the aforesaid sources.

A comprehensive simulation test bed for a stand-alone power system (PV cells and SOFC) is developed in MATLAB/Simulink. The adaptability and robustness of the proposed control paradigm are investigated through simulation results in a stand-alone hybrid power system test bed.

The simulation results confirm the effectiveness of the ANFIS algorithm in a stand-alone hybrid power system scheme.

This study aims to investigate the impact of climate change risk disclosure (CCRD) on Corporate Value while exploring how corporate characteristics such as size, financial performance and leverage may moderate this relationship.

This research used a sample of 50 companies from the EGX100 index on the Egypt Stock Exchange, covering the years 2018–2022 (250 observations). For the first hypothesis, the authors use ordinary least squares regression, while for the other hypotheses, the authors use the PROCESS program developed by Andrew F. Hayes to analyze moderation.

The findings indicate a negative association between CCRD and corporate value. However, the extent of this influence can vary depending on the company’s characteristics. Specifically, CCRD negatively affects the value of small-sized, less profitable and highly leveraged corporates. Conversely, it positively impacts the value of large-sized, more profitable and less leveraged corporates.

The findings have significant implications for stakeholders in Egypt and similar developing countries. This significance stems from the substantial impact of CCRD on corporate value. CCRD can heighten investors’ perception of risk as a new risk factor, potentially leading to increased corporate risk. Therefore, CCRD is vital in identifying corporate value reflected in stock prices.

As far as we know, this is the pioneering study in Egypt to offer new evidence on how CCRD affects corporate value. It investigates the moderating role of corporate characteristics, such as corporate size, financial performance and leverage, in this relationship.

The aim of this research is to examine the moderating impact of financial development (FD) on the relationship between remittance inflows and economic growth in 82 developing countries.

This research utilized dynamic panel data estimation, specifically the system generalized method of moment (GMM), on a panel data set comprised of 82 developing economies from 2000 to 2022.

The findings indicate that the interaction of remittances and FD proxies by size and depth creates a substitute effect to reduce economic growth. In contrast, the interaction of remittances and FD proxy by efficiency creates complementarity by attracting remittances that accelerate economic growth. The robustness of the findings is further checked across upper- and lower-middle-income countries, respectively.

This study assists policymakers in attracting remittance inflows through FD and spending them in sustainable, productive ways to boost economic growth in developing economies.

The policymakers should have interactive remittances–FD policies to improve not only economic growth but also the social welfare of the developing economies.

This work contributes significantly to the underexplored literature on the moderating impact of FD on the relationship between remittance inflows and economic growth in the developing countries context. This research utilizes maximum proxies of FD that not only examine the remittance but also investigate how FD various proxies shape the relationship between remittances and economic growth.

The recent meeting of G-20 world leaders, held in New Delhi, in 2023, highlighted that the physical effect of climate change has considerable macro-economic costs at the national and global levels and they have also pledged to accelerate the clean, sustainable and inclusive energy transition along a variety of pathways. Climate change could pose various emerging risks to the firm’s operational and financial activities, specifically for those which are belonging to the energy sector. Thus, this study aims to investigate the impact of climate risks on the financial performance of select energy companies from G-20 countries.

The study considered 48 energy companies from G-20 countries as the sample for the period of 2017 to 2021. To measure the climate change-related physical risks, the study has considered the ND-GAIN climate vulnerability score and the firm’s financial performance has been measured by return on assets, return on equity, return on capital used and price-to-book ratio. To examine the impact of climate risks on the financial performance of the sample companies, the authors have used pooled ordinary least squares (OLS) and fixed/random effect regression analysis and required data diagnosis tests are also performed.

The empirical results suggested that climate risks negatively impacted the financial performance of the sample companies. The market performances of the firms are also being impacted by the physical climate change. The results of panel data regression analysis also confirmed the robustness of the empirical results derived from the pooled OLS analysis suggesting that firms that operated in a less climate-risky country, financially performed better than the firms that operated in a more climate-risky country.

The paper has significant practical implications like it could be helpful for the policymakers, investors, suppliers, researchers and other stakeholders in developing deeper insights about the impact of climate risks on the energy sectors from an international perspective. This study may also help the policymakers in developing policies for the management of climate risk for the energy sector.

This study adds insights to the existing literature in the area of climate risks and firm’s financial performance. Moreover, this may be the first study that attempts to evaluate the impact of climate risks on the financial performance of select energy companies from the G-20’s perspective.

With the development of Web information systems, steel e-commerce platforms have accumulated a large number of quality objection texts. These texts reflect consumer dissatisfaction with the dimensions, appearance and performance of steel products, providing valuable insights for product improvement and consumer decision-making. Currently, mainstream solutions rely on pre-trained models, but their performance on domain-specific data sets and few-shot data sets is not satisfactory. This paper aims to address these challenges by proposing more effective methods for improving model performance on these specialized data sets.

This paper presents a method on the basis of in-domain pre-training, bidirectional encoder representation from Transformers (BERT) and prompt learning. Specifically, a domain-specific unsupervised data set is introduced into the BERT model for in-domain pre-training, enabling the model to better understand specific language patterns in the steel e-commerce industry, enhancing the model’s generalization capability; the incorporation of prompt learning into the BERT model enhances attention to sentence context, improving classification performance on few-shot data sets.

Through experimental evaluation, this method demonstrates superior performance on the quality objection data set, achieving a Macro-F1 score of 93.32%. Additionally, ablation experiments further validate the significant advantages of in-domain pre-training and prompt learning in enhancing model performance.

This study clearly demonstrates the value of the new method in improving the classification of quality objection texts for steel products. The findings of this study offer practical insights for product improvement in the steel industry and provide new directions for future research on few-shot learning and domain-specific models, with potential applications in other fields.

This study aims to explore the relationship between transformational leadership and proactive customer service performance (PCSP) in the airline industry, considering self-efficacy and emotional intelligence as mediators.

Bandura’s self-efficacy theory was used for formulating hypotheses. Data were gathered from 500 frontline employees and 30 supervisors to create subordinate dyads, resulting in a final sample size of 370 participants. Structural equation modeling was employed for the purpose of data analysis.

The results indicated that supervisors’ transformational leadership impacted the self-efficacy and emotional intelligence of service and frontline employees, thereby influencing PCSP. Self-efficacy and emotional intelligence of frontline employees were positively correlated with PCSP. These findings add to the existing literature by providing empirical proof of a positive association between transformational leadership and perceived customer service performance. Furthermore, this study supported the mediating roles of self-efficacy and emotional intelligence of frontline employees as mediators, showing their importance in translating transformational leadership behaviors into enhanced customer service performance.

The airline sector will benefit significantly from the findings of this research. It highlights the significance of transformational leadership in improving PCSP. Organizations should emphasize the creation and growth of transformational leaders capable of inspiring and motivating staff to provide excellent customer service. In addition, the research highlights the significance of self-efficacy and emotional intelligence as factors that mediate the relationship between transformational leadership and PCSP. Organizations may foster PCSP by investing in training and development programs that seek to improve workers’ self-efficacy and emotional intelligence. These programs may help participants gain confidence in their ability to deliver excellent customer service as well as improve their knowledge.

This research paper presents novel theoretical and practical developments in the airline industry. This research paper collected data in two phases from supervisor–subordinate dyads.

The paper uses foundation models to integrate the green approach in Logistics 5.0. Such integration is innovative in logistics and leads to a more sustainable and prosperous future. By harnessing the power of foundation models and incorporating sustainable principles, this paper can systematize the logistics industry’s environmental framework, increase its social responsibility and ensure its long-term economic viability.

Generalizing environmental sustainability goals requires a multi-layered innovation approach incorporating corporate philosophy, products, processes and business models. In this paper, this comprehensive approach is not just a strategy but a necessity in the current global context. This paper uses the sustainability-oriented innovation (SOI) method, crucial for achieving explicit environmental, social and economic impacts.

Artificial intelligence, especially foundation models, can contribute to green logistics by optimizing routes, reducing packaging waste, improving warehouse layouts and other functions presented in the paper. At the same time, they can also consider social, economic and governance goals.

Artificial intelligence algorithms present challenges such as high initial investment, regulatory compliance and technological integration.

The paper contains implications for developing environmentally sustainable logistics, which is currently one of the most significant challenges. The framework presented can apply to logistics companies.

This paper fulfills an identified need to study sustainability in logistics. The framework is entirely original and not present in the literature. It is essential to help design and implement innovative logistics approaches.

Financial distress (FD) is an unfavorable situation that can have severe negative consequences on a firm. Within the range of multiple micro and macro factors, firm’s dividend policy can impact FD. However, this relationship is yet to be explored. Since shareholder yield (SHY) is a major component of the dividend policy, this study aims to explore the effect of SHY on a firm’s FD. Taking insights from stakeholder theory and dividend signaling theory, we also examined if this relationship is moderated by competition and firm size.

The data from Fortune 500 companies over thirteen years (2010–2022) was subjected to panel data analysis (PDA). The analysis particularly takes the quantile panel data model to have a deeper understanding of variable’s association in different scenarios of FD.

The findings revealed that the SHY does not directly influence a firm’s FD. However, it is negatively moderated by competition at a lower quantile of financial stability and positively moderated by firm size at all quantiles of financial stability (reverse of FD). It means when competition increases, the shareholder’s yield reduces the financial stability. However, it improves financial stability when firm size increases.

The findings deliver significant implications for all the stakeholders to consider dividend policy in form of SHY as a crucial element for a firm’s financial soundness. It is very situational to improve or detriment the financial health of the firm when it combines with other factors particularly competition or firm size. Hence, it is important to understand its sensitivity for FD.

In this study, we evidenced competition and firm size as moderators to SHY and FD relationship thus presenting novel insights. The findings are integrated to stakeholder theory and dividend signaling theory, and thus offer theoretical advancements.

Drawing on the conservation of resources (COR) theory, this study aims to examine the effects of servant leadership and despotic leadership on employees’ happiness at work (HAW) through job crafting.

To test the hypothesized relationships, the data were collected from 309 Pakistani employees. Structural equation modeling was used to analyze the data.

The findings showed that servant leadership is an optimal leadership style for creating employees’ HAW. In addition, job crafting was found to mediate the effects of servant leadership on employees’ broad-based positive attitudinal outcome (HAW). Moreover, results showed that despotic leadership negatively influences employees’ HAW through job crafting.

This study is novel as it investigates how newer forms of positive (servant) and negative (despotic) leadership styles influence employees’ multidimensional attitudinal outcome (HAW) via job crafting. By doing so, this research extends the nomological network of servant leadership, despotic leadership, job crafting and HAW.