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This study, based on social exchange theory, aims to explore the association between appreciative leadership and employees' helping behaviors by investigating the mediation role of emotional reactions (pride, anxiety), and choosing organizational trust as a boundary condition between appreciative leadership and helping behaviors.

A total sample of 285 reliable questionnaires were collected in three time lags from employees working in the Pakistani education and banking sectors. PROCESS macro using SPSS and AMOS are employed for data analyses of the proposed model.

The findings reveal that appreciative leadership has positive impacts on employees' helping behaviors and emotional reactions (pride, anxiety) mediate the relationship of appreciative leadership and employees' helping behaviors. In addition, the results show that high organizational trust strengthens the positive relationship between appreciative leadership and employees' helping behaviors.

This research has provided empirical proof between the relationship of appreciative leadership and helping behaviors and the findings are of great significance for managers, employees, and organizations. The study proposes that leaders should have appreciative behavior while treating their subordinates. Moreover, it is revealed that the role of organizational trust should be given more attention and importance because it is a factor moderating the employees' helping behaviors.

The present study, among the first empirical efforts investigating the relationship between appreciative leadership and helping behaviors, organizational trust as a moderator, enriches the existing academic literature of and provides worthy insight into the research on appreciative leadership and helping behaviors.

This study examined the influence of corporate governance (CG) in relation to venture capital (VC) investment on invested firm's corporate social responsibility (CSR) performance in the Chinese context. More specifically, this paper examined the mediation of the proportion of independent directors (INDD), management shareholding (MSH) and executives' political connections (POLC) in the above-mentioned relationship.

This empirical study performed multiple mediation testing and bootstrap mediation robustness test on data from Chinese A-class shares IPO companies between 2010 and 2018.

The results of direct relationship analysis showed that VC support is detrimental to firm' CSR performance, consistent with previous research studies. The indirect effect analysis showed that VC reduced firm' CSR through reduction of INDD on board and increased MSH. Conversely, VC contributed to firm's CSR through higher POLC, which confirmed the significance of the joint mediation model.

This study offers stakeholders the opportunity to develop a deeper understanding of the role of VC institutions, independent directors and executives, in terms of firm's CSR, as well as provides insights on control rights allocation and policy drafting on independent directors when considering accessing VC support.

By analyzing the mediation model of the VC–CSR relationship, this paper provides evidence to enrich the debate on the role of CG in the relation between VC and firm's CSR.

Value co-creation is a new initiative for enterprises to form a competitive advantage, and user engagement is the basis for achieving value co-creation; nevertheless, few studies have discussed the influence mechanisms of user engagement on value co-creation behavior. In this study, the authors aim to reveal the influence mechanisms of online fitness user engagement on value co-creation behavior by considering emotional resonance and immersive experience as mediating variables.

The authors proposed and empirically tested a research model based on a survey involving 461 Chinese respondents through partial least squares structural equation modeling (PLS-SEM).

The results of this study confirm that consumer engagement, contributing engagement and social engagement are important drives of value co-creation behavior among online fitness users. Furthermore, emotional resonance and immersive experience have been revealed as important mediating mechanisms to explain why user engagement drives value co-creation behavior.

The results of this study suggest that practitioners need to focus on the social engagement and consumer engagement of users in online fitness communities and to provide the appropriate environment and conditions for online fitness user to achieve mutual value co-creation.

This study makes two main contributions. It examines user engagement in an online fitness community context and helps to understand its applicability in other contexts. It explains the influence mechanisms of online fitness user engagement on value co-creation behavior and enriches the studies related to the drivers of value co-creation behavior.

The rise of the metaverse has brought profound changes to the economic and social operation models and injected new vitality into academic research. Although a large number of studies have emerged, there are few quantitative analyses of development frontiers and trends.

From a bibliometric perspective, this paper selects 183 pieces of metaverse-related literature in the WoS core database since 2000 as the object of analysis. This paper sums up the characteristics of the literature using the methods of descriptive statistical analysis, keywords analysis, thematic evolution analysis and summarizes the core themes and the laws of metaverse development in each stage.

The digital economy vision brought by the metaverse has led to an increasing number of researchers and achievements in this field. But the depth and breadth of research are still insufficient and unevenly distributed in the region, and the cross-fertilization fields need to be expanded. From the industry's point of view, VR games represented by Second Life and My World have contributed to the popularity of the metaverse. As technology progresses, the research hotspots in the field of metaverse gradually develop from conceptual research to artificial intelligence, blockchain, NFT and other technical applications. However, academic research has not yet caught up with the industry's pace and stays more in the concept discussion and preliminary application stage.

A systematic overview of the current status, knowledge structure and hot issues of metaverse research is shown, which provides a thematic axis for this field, enriches and improves the quantitative analysis of its literature and provides a clear picture for researchers to continuously promote the development of this field. At the same time, it is necessary to warn that technological development is a double-edged sword. The process of metaverse development should return to rationality, respect the laws of its development and guarantee the healthy development of the metaverse by strengthening legal regulation and the ethical review of science and technology.

Risk factors related delay hinder the schedule performance of most construction projects in the world. It is a critical challenge to realize the advantages of prefabricated construction projects (PCPs) under the negative effect of schedule delay. This paper aims to propose an exhaustive list of risk factors impeding the progress of PCPs and evaluate the collected risk factors based on the cause–effect relations. The ultimate goal is to improve the understanding of the complex relations among various risk factors related delay in PCPs, and also offer managers a reference on aspect of schedule risk management.

This paper proposes a hybrid method of GT–DEMATEL–ISM, that is combing grounded theory, DEMATEL (decision-making trial and evaluation laboratory) and ISM (Interpretative Structural Modeling), to collect, evaluate and structure risk factors related delay for PCPs. The research procedure of this methodology is divided into three stages systematically involving qualitative and quantitative analysis. In the first stage, GT is utilized to implement qualitative analysis to collect the risk factors leading to schedule delay in PCPs. While, the quantitative analysis is to analyze and evaluate the collected risk factors based on the cause–effect relations in the next two stages evaluation by the DEMATEL focuses on quantifying the priority and intensity of the relations between factors. Additionally, ISM is employed to construct the hierarchical structure and graphically represent the pairwise relations between factors.

The outcome of qualitative investigation by grounded theory proposes a theoretical framework of risk factors related delay for PCPs. The framework contains three levels of category, namely, core category, main category and initial category and provides a list of risk factors related delay. Following this finding, evaluation results by the DEMATEL classify factors into cause and effect groups and determine 11 critical delay risk factors. Meanwhile, the findings show that risks referring to organizational management issue foremost impact the progress of PCPs. Furthermore, a systemic multilevel hierarchical structure model is visually constructed by ISM to present the pairwise linkages of critical factors. The model provides the risk transmission chains to map the spread path of delay impact in the system.

The contribution of the study involves twofold issues. Methodologically, this research proposes a hybrid method GT–DEMATEL–ISM used to identify and analyze factors for a complex system. It is also applicable to other fields facing similar problems that require collecting, evaluating and structuring certain elements as a whole in a comprehensive perspective. The theoretical contribution is to fill the relevant research gap of the existing body of knowledge. To the best knowledge of the authors, this paper is the first attempt to integrate qualitative and quantitative research for risk analysis related delay and take the insight into the whole process of PCPs covering off-site manufacture and on-site construction. Furthermore, the analysis of findings provided both a micro view focusing on individual risk factor and a managerial view from a systematic level. The findings also contribute the effective information to improve the risk management related schedule delay in PCPs.

Since 2008, bitcoin has continued to attract investors due to its growing capitalization and opportunity for speculation. The purpose of this paper is to analyze the impact of bitcoin (BTC) on gold, the volatility index (VIX) and the dollar index (USDX).

The series used are weekly and cover the period from January 2016 to November 2022. To generate the results, the unrestricted vector autoregression (VAR), structural vector autoregression (SVAR) and wavelet coherence were performed.

The findings are mixed as not all tests show the exact effects of BTC in the three asset classes. However, common to all the tests is the significant influence that BTC maintains on gold and vice versa. The positive shock in BTC significantly increases the gold prices, confirmed in three different tests. The effects on the VIX and USDX are still being determined, where in some tests, it appears to be influential while in others not.

BTC’s diversification potential with equity stocks and USDX makes it a valuable security for portfolio managers. Furthermore, regulatory authorities should consider that BTC is not an isolated phenomenon and can significantly influence other asset classes such as gold.

Owing to highly volatile and chaotic external events, predicting future movements of cryptocurrencies is a challenging task. This paper advances a granular hybrid predictive modeling framework for predicting the future figures of Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Stellar (XLM) and Tether (USDT) during normal and pandemic regimes.

Initially, the major temporal characteristics of the price series are examined. In the second stage, ensemble empirical mode decomposition (EEMD) and maximal overlap discrete wavelet transformation (MODWT) are used to decompose the original time series into two distinct sets of granular subseries. In the third stage, long- and short-term memory network (LSTM) and extreme gradient boosting (XGB) are applied to the decomposed subseries to estimate the initial forecasts. Lastly, sequential quadratic programming (SQP) is used to fetch the forecast by combining the initial forecasts.

Rigorous performance assessment and the outcome of the Diebold-Mariano’s pairwise statistical test demonstrate the efficacy of the suggested predictive framework. The framework yields commendable predictive performance during the COVID-19 pandemic timeline explicitly as well. Future trends of BTC and ETH are found to be relatively easier to predict, while USDT is relatively difficult to predict.

The robustness of the proposed framework can be leveraged for practical trading and managing investment in crypto market. Empirical properties of the temporal dynamics of chosen cryptocurrencies provide deeper insights.

This study aims to focus on the surface mount technology (SMT) mass production process of Sn-9Zn-2.5Bi-1.5In solder. It explores it with some components that will provide theoretical support for the industrial SMT application of Sn-Zn solder.

This study evaluates the properties of solder pastes and selects a more appropriate reflow parameter by comparing the microstructure of solder joints with different reflow soldering profile parameters. The aim is to provide an economical and reliable process for SMT production in the industry.

Solder paste wettability and solder ball testing in a nitrogen environment with an oxygen content of 3,000 ppm meet the requirements of industrial production. The printing performance of the solder paste is good and can achieve a printing rate of 100–160 mm/s. When soldering with a traditional stepped reflow soldering profile, air bubbles are generated on the surface of the solder joint, and there are many voids and defects in the solder joint. A linear reflow soldering profile reduces the residence time below the melting point of the solder paste (approximately 110 s). This reduces the time the zinc is oxidized, reducing solder joint defects. The joint strength of tin-zinc joints soldered with the optimized reflow parameters is close to that of Sn-58Bi and SAC305, with high joint strength.

This study attempts to industrialize the application of Sn-Zn solder and solves the problem that Sn-Zn solder paste is prone to be oxidized in the application and obtains the SMT process parameters suitable for Sn-9Zn-2.5Bi-1.5In solder.

Milling is a flexible creation process for the manufacturing of dies and aeronautical parts. While machining thin-walled parts, heat generation during machining essentially affects the accuracy. The workpiece temperature (WT), as well as the responses like material removal rate (MRR) and surface roughness (SR) for input parameters like cutting speed (CS), feed rate (F), depth-of-cut (DOC), step over (SO) and tool diameter (TD), becomes critical for sustaining the accuracy of the thin walls.

Response surface methodology was used to make 46 tests. To convert the multi-character problem into a single-character problem, the weightage was assessed using the entropy approach and the grey relational coefficient (GRC) was determined. To investigate the connection among input parameters and single-objective (GRC), a fuzzy mathematical modelling technique was used. The optimal performance of process parameters was estimated by grey relational entropy grade (GREG)-fuzzy and genetic algorithm (GA) optimization.

SR was found to be a significant process parameter, with CS, feed and DOC, respectively. Similarly, F, DOC and TD were found to be significant process parameters with MRR, respectively, and F, DOC, SO and TD were found to be significant process parameters with WT, respectively. GREG-fuzzy-GA found more suitable for minimizing the WT with the constraint s of SR and MRR and provide maximum desirability of 0.665. The projected and experimental values have a good agreement, with a standard error of 5.85%, and so the responses predicted by the suggested method are better optimized.

The GREG-fuzzy-GA is a new hybrid technique for analysing Inconel625 behaviour during machining in a 2.5D milling process.

Bearings play a critical role in the reliable operation of induction machines, and their failure can lead to significant operational challenges and downtime. Detecting and diagnosing these defects is imperative to ensure the longevity of induction machines and preventing costly downtime. The purpose of this paper is to develop a novel approach for diagnosis of bearing faults in induction machine.

To identify the different fault states of the bearing with accurately and efficiently in this paper, the original bearing vibration signal is first decomposed into several intrinsic mode functions (IMFs) using variational mode decomposition (VMD). The IMFs that contain more noise information are selected using the Pearson correlation coefficient. Subsequently, discrete wavelet transform (DWT) is used to filter the noisy IMFs. Second, the composite multiscale weighted permutation entropy (CMWPE) of each component is calculated to form the features vector. Finally, the features vector is reduced using the locality-sensitive discriminant analysis algorithm, to be fed into the support vector machine model for training and classification.

The obtained results showed the ability of the VMD_DWT algorithm to reduce the noise of raw vibration signals. It also demonstrated that the proposed method can effectively extract different fault features from vibration signals.

This study suggested a new VMD_DWT method to reduce the noise of the bearing vibration signal. The proposed approach for bearing fault diagnosis of induction machine based on VMD-DWT and CMWPE is highly effective. Its effectiveness has been verified using experimental data.