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This study investigates the mediating role of wage and workforce adjustments, along with the moderating influence of collective bargaining system and employees’ localization, in elucidating the relationship between the COVID-19 shock and workplace employee relations (ER) tension.

Survey data from 1,483 enterprises across 21 prefectural cities in China’s Guangdong Province are collected. The hypotheses are tested by logistic regression.

The study reveals a positive correlation between the COVID-19 shock and workplace ER tension across crisis-hit enterprises, irrespective of their size or industrial sector. Wage reduction and mass layoffs emerge as significant mediators, while the collective bargaining system (CBS) and employees’ localization act as moderators.

The measurement of ER is limited in a single-item scale. Representation of China is also limited since the study exclusively focuses on Guangdong province. The study offers some contributions that firm-level data reveal the pathway through which COVID-19 creates ER tension.

On the one hand, the authors recommend the establishment of an effective communication system between employers and employees. On the other hand, managers should consider the role of informal institutions. Furthermore, the authors suggest implementing tailored strategies at the enterprise level.

Intense external shocks result in widespread layoffs and increased wage reductions within workplaces, and under such circumstances, formal or informal institutions may be insufficient to alleviate ER tension. In this case, the state authorities – including governments and other public agencies or bodies – are necessary to intervene in to organize tripartite dialogue.

While numerous emerging studies on COVID-19 explore how different countries manage industrial relations tension at the national level, few focus on ER at workplace level, particularly in developing countries. Understanding how workplace ER evolve during external shocks and identifying institutional measures to mitigate their negative impact is crucial for future crisis management.

High-efficiency video coding (HEVC) is the latest video coding standard that has better coding efficiency than the H.264/advanced video coding (AVC) standard. The purpose of this paper is to design and develop an effective block search mechanism for the video compression-HEVC standard such that the developed compression standard is applied for the communication applications.

In the proposed method, an rate-distortion (RD) trade-off, named regressive RD trade-off is used based on the conditional autoregressive value at risk (CaViar) model. The motion estimation (ME) is based on the new block search mechanism, which is developed with the modification in the Ordered Tree-based Hex-Octagon (OrTHO)-search algorithm along with the chronological Salp swarm algorithm (SSA) based on deep recurrent neural network (deepRNN) for optimally deciding the shape of search, search length of the tree and dimension. The chronological SSA is developed by integrating the chronological concept in SSA, which is used for training the deep RNN for ME.

The competing methods used for the comparative analysis of the proposed OrTHO-search based RD + chronological-salp swarm algorithm (RD + C-SSA) based deep RNN are support vector machine (SVM), fast encoding framework, wavefront-based high parallel (WHP) and OrTHO-search based RD method. The proposed video compression method obtained a maximum peak signal-to-noise ratio (PSNR) of 42.9180 dB and a maximum structural similarity index measure (SSIM) of 0.9827.

In this research, an effective block search mechanism was developed with the modification in the OrTHO-search algorithm along with the chronological SSA based on deepRNN for the video compression-HEVC standard.

The purpose of this study is to investigate the pricing strategies for knowledge payment products, taking into account the quality level of pirated knowledge products, in the context of platform copyright supervision.

This study abstracts the knowledge payment transaction process and aims to maximize producer's revenue by constructing a pricing model for knowledge payment products. It discusses pricing strategies for knowledge payment products under two scenarios: traditional supervision and blockchain supervision. The analysis explores the impact of pirated knowledge products quality level and blockchain technology on pricing strategies and consumer surplus, while providing threshold conditions for effective strategies.

Deploying blockchain technology in platform operations can significantly reduce costs and increase efficiency. In both scenarios, knowledge producer needs to balance factors such as the quality of pirated knowledge products, the supervision level of platform, and consumer surplus to dynamically adjust pricing strategies in order to maximize his own revenue.

This study enriches the literature on the pricing models of knowledge payment products and has practical significance in guiding knowledge producer to develop effective pricing strategies under copyright supervision.

This paper examines the impact of founder ownership concentration (FOC) on risk disclosures. It further investigates the moderating role of risk governance in the association between FOC and risk disclosures.

We use data from the top 200 Indian listed firms as our sample and rely on ordinary least squares (OLS) for our results. In addition, we use the propensity score matching, Heckman selection model and instrumental variable estimates for robustness checks.

We find that FOC decreases the risk disclosures. However, the effectiveness of risk management committee composition (risk governance) mitigates the negative influence of FOC on risk disclosures.

The paper is built on the agency theory. Based on the agency theory, the ownership concentration has two implications: first, it reduces the conflicts between managers and shareholders. Here, the managers act in favour of shareholders and therefore, brings more risk disclosers. Second, it invites conflicts between controlling and minority shareholders. The study is, therefore, interesting to see the cost and benefits of FOC on risk disclosures.

The study has practical implications for the regulatory bodies to encourage risk disclosures and benefit the outsiders of the firm. It also has implications for the companies to see the benefits of risk management committee as improved risk governance.

It contributes to the literature of risk disclosures and risk governance in emerging economies. It is the first study to investigate the role of risk governance in mitigating the adverse effects of founder’s ownership on risk disclosures in developing economies. It also contributes to the theory of agency cost and information asymmetry.

This study empirically demonstrates a contradiction between pillar 3 of Basel norms III and the designation of Systemically Important Banks (SIBs), also known as Too Big to Fail (TBTF). The objective of this study is threefold, which has been approached in a phased manner. The first is to determine the systemic importance of the banks under study; second, to examine if market discipline exists at different levels of systemic importance of banks and lastly, to examine if the strength of market discipline varies at different levels of systemic importance.

This study is based on all the public and private sector banks operating in the Indian banking sector. The Gaussian Mixture Model algorithm has been utilized to classify banks into distinct levels of systemic importance. Thereafter, market discipline has been observed by analyzing depositors' sentiments toward banks' risk (CAMEL indicators). The analysis has been performed by employing the system Generalized Method of Moments (GMM) to estimate models with different dependent variables.

The findings affirm the existence of market discipline across all levels of systemic importance. However, the strength of market discipline varies with the systemic importance of the banks, with weak market discipline being a negative externality of the SIBs designation.

By employing the Gaussian Mixture Model algorithm to develop a framework for categorizing banks on the basis of their systemic importance, this study is the first to go beyond the conventional method as outlined by the Reserve Bank of India (RBI).

This paper aims to Solar photovoltaic (PV) power can significantly impact the power system because of its intermittent nature. Hence, an accurate solar PV power forecasting model is required for appropriate power system planning.

In this paper, a long short-term memory (LSTM)-based double deep Q-learning (DDQL) neural network (NN) is proposed for forecasting solar PV power indirectly over the long-term horizon. The past solar irradiance, temperature and wind speed are used for forecasting the solar PV power for a place using the proposed forecasting model.

The LSTM-based DDQL NN reduces over- and underestimation and avoids gradient vanishing. Thus, the proposed model improves the forecasting accuracy of solar PV power using deep learning techniques (DLTs). In addition, the proposed model requires less training time and forecasts solar PV power with improved stability.

The proposed model is trained and validated for several places with different climatic patterns and seasons. The proposed model is also tested for a place with a temperate climatic pattern by constructing an experimental solar PV system. The training, validation and testing results have confirmed the practicality of the proposed solar PV power forecasting model using LSTM-based DDQL NN.

The study examines the IPO resilience grounded on the firm’s intrinsic factors.

We examine the association of IPO performance and post-listing firm’s performance with issuers' pre-listing financial and qualitative traits using panel data regression.

IPOs floated in the Indian market from July 2009 to March 31, 2022, evince the notable influence of issuers' pre-IPO fundamentals and legitimacy traits on IPO returns and post-listing earning power. Where the pandemic’s favorable impact is discerned on the post-listing year earning power of the issuer firms, the loss-making issuers appear to be adversely affected by the Covid disruption. Perhaps, the successful listing equipped the issuers with the financial flexibility to combat market challenges vis-à-vis failed issuers deprived of desired IPO proceeds.

High initial returns followed by a declining pattern substantiate the retail investors to be less informed vis-à-vis initial investors, valuers and underwriters, who exit post-listing after profit booking. Investing in the shares of the newly listed ventures post-listing in the secondary market can shield retail investors from the uncertainty losses of being uninformed. The IPO market needs stringent regulations ensuring the verification of the listing valuation, the firm’s credentials and the intent of utilizing IPO proceeds. Healthy development of the IPO market merits reconsidering the listing of ventures with weak fundamentals suspected to withstand the market challenges.

Given the tremendous rise in the new firm venturing into the primary market and the spike in IPOs countering the losses immediately post-opening, the study examines the loss-making and young firms IPOs separately, adding novelty to the study.

We examine the association between conditional conservatism in initial public offering (IPO) underpricing and post-issue stock market survival in the U.S.

We adopt an archival approach by collecting data for 1,761 U.S. IPO issuers for the period 1990–2017. Regression analyses are conducted to evaluate the association between conditional conservatism in initial public offerings with underpricing and post-issue stock market survival. We identify firms that went public in the period 1990–2012. These firms are then followed for five years after the IPO to assess their stock market survival.

We find that pre-issue conditional conservatism is significantly associated with less IPO underpricing. We also detect that IPO firms with higher levels of conditional conservative reporting are more likely to survive in the post-IPO stock market in the three-, four-, and five-year periods after the IPO. Our main findings are robust after controlling for other factors in our models, such as IPO cycles, venture capitalists, research and development investment, and pre-IPO accounting performance.

We extend research by demonstrating that conditional conservative reporting practices help firms reduce their indirect costs of raising their initial public capital. Additionally, our research introduces new evidence on the association between pre-IPO conditional conservatism and after-issue stock market survival. Our findings empirically support the International Accounting Standards Board’s (IASB) decision to reintroduce the concept of prudence into the conceptual framework, by showing how conservative reporting can reduce information asymmetry in IPO firms.

The purpose of this paper is to study and analyze the effects of fly ash (FA) as a mineral admixture on compressive strength (CS), carbonation resistance and corrosion resistance of reinforced concrete (RC). In addition, the utilization of inexpensive and abundantly available FA as a cement replacement in concrete has several benefits including reduced OPC usage and elimination of the FA disposal problem.

Reinforcement corrosion and carbonation significantly affect the strength and durability of the RC structures. Also, the utilization of FA as green corrosion inhibitors, which are nontoxic and environmentally friendly alternatives. This review discusses the effects of FA on the mechanical characteristics of concrete. Also, this review analyzes the impact of FA as a partial replacement of cement in concrete and its effect on the depth of carbonation in concrete elements and the corrosion rate of embedded steel as well as the chemical composition and microstructure (X-ray diffraction analysis and scanning electron microscopy) of FA concrete were also reviewed.

This review provides a clear analysis of the available study, providing a thorough overview of the current state of knowledge on this topic. Regarding concrete CS, the findings indicate that the incorporation of FA often leads to a loss in early-age strength. However, as the curing period increased, the strength of fly ash concrete (FAC) increased with or even surpassed that of conventional concrete. Analysis of the accelerated carbonation test revealed that incorporating FA into the concrete mix led to a shallower carbonation depth and slower diffusion of carbon dioxide (CO₂) into the concrete. Furthermore, the half-cell potential test shows that the inclusion of FA increases the durability of RC by slowing the rate of steel-reinforcement corrosion.

This systematic review analyzes a wide range of existing studies on the topic, providing a comprehensive overview of the research conducted so far. This review intends to critically assess the enhancements in mechanical and durability attributes (such as CS, carbonation and corrosion resistance) of FAC and FA-RC. This systematic review has practical implications for the construction and engineering industries. This can support engineers and designers in making informed decisions regarding the use of FA in concrete mixtures, considering both its benefits and potential drawbacks.

The purpose of this paper is to investigate, from a thorough review of the literature, the role of metaverse-based quality 4.0 (MV-based Q4.0) in achieving manufacturing resilience (MFGRES). Based on a categorization of MV-based Q4.0 enabler technologies and MFGRES antecedents, the paper provides a conceptual framework depicting the relationship between both areas while exploring existing knowledge in current literature.

The paper is structured as a comprehensive systematic literature review (SLR) at the intersection of MV-based Q4.0 and MFGRES fields. From the Scopus database up to 2023, a final sample of 182 papers is selected based on the inclusion/exclusion criteria that shape the knowledge base of the research.

In light of the classification of reviewed papers, the findings show that artificial intelligence is especially well-suited to enhancing MFGRES. Transparency and flexibility are the resilience enablers that gain most from the implementation of MV-based Q4.0. Through analysis and synthesis of the literature, the study reveals the lack of an integrated approach combining both MV-based Q4.0 and MFGRES. This is particularly clear during disruptions.

This study has a significant impact on managers and businesses. It also advances knowledge of the importance of MV-based Q4.0 in achieving MFGRES and gaining its full rewards.

This paper makes significant recommendations for academics, particularly those who are interested in the metaverse concept within MFGRES. The study also helps managers by illuminating a key area to concentrate on for the improvement of MFGRES within their organizations. In light of this, future research directions are suggested.